R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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+ ,dim=c(5
+ ,312)
+ ,dimnames=list(c('Popularity'
+ ,'B'
+ ,'2B'
+ ,'3B'
+ ,'Month')
+ ,1:312))
> y <- array(NA,dim=c(5,312),dimnames=list(c('Popularity','B','2B','3B','Month'),1:312))
> 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Popularity B 2B 3B Month
1 15 2 0 0 9
2 12 1 1 2 9
3 9 1 2 1 9
4 10 0 0 0 9
5 13 0 0 0 9
6 16 1 0 0 9
7 14 0 0 0 9
8 16 1 1 0 9
9 10 0 0 0 9
10 8 2 0 1 10
11 12 1 0 0 10
12 15 0 0 0 10
13 14 0 1 0 10
14 14 1 1 2 10
15 12 1 2 1 10
16 12 0 0 0 10
17 10 0 0 0 10
18 4 0 0 0 10
19 14 0 1 0 10
20 15 0 0 0 10
21 16 0 0 0 10
22 12 0 1 0 10
23 12 0 0 0 10
24 12 0 0 1 10
25 12 1 0 1 9
26 12 0 0 0 9
27 11 3 2 1 9
28 11 1 0 0 9
29 11 1 1 0 9
30 11 1 1 0 9
31 11 3 1 1 9
32 11 0 0 0 9
33 15 0 0 0 9
34 15 0 0 0 9
35 9 0 0 0 9
36 16 0 0 0 9
37 13 0 2 1 9
38 9 1 0 0 9
39 16 0 0 0 9
40 12 0 0 0 9
41 15 0 0 2 9
42 5 0 0 0 9
43 11 2 2 0 9
44 17 2 2 0 9
45 9 0 1 1 9
46 13 0 0 0 9
47 16 0 0 0 10
48 16 0 0 0 10
49 14 2 0 2 10
50 16 1 0 0 10
51 11 0 0 0 10
52 11 0 0 0 10
53 11 0 0 0 10
54 12 0 0 0 10
55 12 1 1 1 10
56 12 0 0 0 10
57 14 0 0 0 10
58 10 2 0 0 10
59 9 0 2 0 10
60 12 0 0 1 10
61 10 0 0 0 10
62 14 0 0 0 10
63 8 0 0 0 10
64 16 1 0 0 10
65 14 1 0 0 10
66 14 0 0 0 10
67 12 0 0 0 10
68 14 1 0 0 10
69 7 1 1 1 10
70 19 0 0 0 10
71 15 0 0 0 10
72 8 0 0 0 10
73 10 0 0 0 10
74 13 0 0 0 10
75 13 0 0 0 9
76 10 0 0 0 9
77 12 0 0 0 9
78 15 0 1 1 9
79 7 1 0 0 9
80 14 0 0 0 9
81 10 0 0 0 9
82 6 0 3 0 9
83 11 2 0 0 9
84 12 0 0 0 9
85 14 0 0 2 9
86 12 0 0 0 9
87 14 0 0 0 9
88 11 2 2 0 9
89 10 1 0 1 9
90 13 0 0 1 9
91 8 0 0 0 9
92 9 0 0 0 9
93 6 0 0 0 10
94 12 1 0 2 10
95 14 0 0 0 10
96 11 0 0 0 10
97 8 1 0 1 10
98 7 0 0 0 10
99 9 0 2 1 10
100 14 2 1 0 10
101 13 0 0 0 10
102 15 0 0 0 10
103 5 0 0 0 10
104 15 3 1 0 10
105 13 0 1 0 10
106 12 0 0 0 10
107 6 1 0 0 10
108 7 0 0 0 10
109 13 0 0 0 10
110 16 1 1 0 10
111 10 0 0 0 10
112 16 0 0 0 10
113 15 0 0 0 10
114 8 0 0 0 10
115 11 0 0 0 10
116 13 0 3 1 10
117 16 1 0 0 10
118 11 0 0 0 10
119 14 0 0 0 10
120 9 2 1 0 10
121 8 0 0 0 10
122 8 1 0 1 10
123 11 2 0 0 10
124 12 0 0 0 10
125 11 4 0 0 10
126 14 0 1 2 10
127 11 2 1 0 10
128 14 0 0 0 10
129 13 2 1 2 10
130 12 0 0 0 10
131 4 0 0 0 10
132 15 2 1 1 10
133 10 0 0 0 10
134 13 1 2 1 10
135 15 1 1 2 10
136 12 1 2 1 10
137 13 0 0 0 10
138 8 0 0 0 10
139 10 2 0 0 10
140 15 0 0 0 10
141 16 0 0 0 10
142 16 0 0 0 10
143 14 0 0 0 10
144 14 1 1 1 10
145 12 1 1 1 10
146 15 0 1 2 9
147 13 1 1 1 9
148 16 0 0 0 10
149 14 0 0 0 10
150 8 0 0 0 10
151 16 0 1 0 10
152 16 1 1 1 10
153 12 0 0 0 10
154 11 0 3 1 10
155 16 1 1 1 10
156 9 0 0 0 10
157 15 1 1 3 11
158 12 1 0 0 11
159 9 1 0 3 11
160 10 1 3 0 11
161 13 1 1 3 11
162 16 1 1 0 11
163 14 1 2 0 11
164 16 2 0 1 11
165 10 1 1 1 11
166 8 0 0 0 11
167 12 2 1 0 11
168 15 1 0 2 11
169 14 1 0 0 11
170 14 1 0 0 11
171 12 1 0 1 11
172 12 0 2 1 11
173 10 0 3 1 11
174 4 0 2 0 11
175 14 0 2 1 11
176 15 0 0 0 11
177 16 1 0 0 11
178 12 2 0 0 11
179 12 0 0 0 11
180 12 0 1 0 11
181 12 0 2 0 11
182 12 1 0 0 11
183 11 1 1 0 11
184 11 3 0 0 11
185 11 0 1 3 11
186 11 0 1 2 11
187 11 1 0 0 11
188 11 2 0 1 11
189 15 1 0 0 11
190 15 1 0 1 11
191 9 0 2 2 11
192 16 0 2 1 11
193 13 0 0 1 11
194 9 2 2 0 11
195 16 1 2 0 11
196 12 1 0 0 11
197 15 2 1 0 11
198 5 0 3 0 11
199 11 1 2 0 11
200 17 2 0 0 11
201 9 0 2 1 11
202 13 2 0 0 11
203 16 0 1 1 11
204 16 0 1 0 11
205 14 1 1 0 11
206 16 0 1 1 11
207 11 1 0 0 11
208 11 0 1 2 11
209 11 1 2 1 11
210 12 1 0 0 11
211 12 1 1 1 11
212 12 1 1 0 11
213 14 1 1 1 11
214 10 1 0 2 11
215 9 0 1 0 11
216 12 0 1 0 11
217 10 1 0 0 11
218 14 2 2 0 11
219 8 1 0 0 11
220 16 0 2 1 11
221 14 0 1 3 11
222 14 0 0 1 11
223 12 0 1 0 11
224 14 0 1 0 11
225 7 0 1 0 11
226 19 2 1 0 11
227 15 0 0 2 11
228 8 0 0 0 11
229 10 1 0 0 11
230 13 1 1 0 11
231 13 1 0 3 11
232 10 1 0 1 11
233 12 1 0 0 11
234 15 0 0 1 11
235 7 0 0 1 11
236 14 0 0 1 11
237 10 1 1 1 11
238 6 2 0 0 11
239 11 1 1 0 11
240 12 2 2 0 11
241 14 3 1 0 11
242 12 1 2 0 11
243 14 0 1 2 11
244 11 2 1 1 11
245 10 1 0 0 11
246 13 0 1 2 11
247 8 0 0 1 11
248 9 2 0 4 11
249 6 1 1 0 11
250 12 1 0 0 11
251 14 0 0 0 11
252 11 2 2 0 11
253 8 0 0 1 11
254 7 1 0 0 11
255 9 0 2 0 11
256 14 3 1 0 11
257 13 0 0 0 11
258 15 0 1 2 11
259 5 1 1 2 11
260 15 0 2 2 11
261 13 0 1 0 11
262 12 0 0 1 11
263 6 1 0 0 11
264 7 1 0 0 11
265 13 3 0 0 11
266 16 2 0 0 11
267 10 0 1 0 11
268 16 1 0 0 11
269 15 1 0 0 11
270 8 1 0 1 11
271 11 1 0 0 11
272 13 1 1 2 11
273 16 0 2 1 11
274 11 0 1 3 11
275 14 0 1 1 11
276 9 0 0 2 11
277 8 0 1 0 12
278 8 2 0 0 12
279 11 1 3 0 12
280 12 0 2 0 12
281 11 2 1 0 12
282 14 1 0 0 12
283 11 1 0 0 12
284 14 1 1 0 12
285 13 0 0 1 12
286 12 1 1 0 12
287 4 1 0 0 12
288 15 0 0 0 12
289 10 0 0 1 12
290 13 0 0 0 12
291 15 0 0 2 12
292 12 0 0 0 12
293 13 1 0 0 12
294 8 0 0 0 12
295 10 0 0 1 12
296 15 1 0 0 12
297 16 0 1 0 12
298 16 1 0 0 12
299 14 0 0 0 12
300 14 1 0 0 12
301 12 0 0 0 12
302 15 0 0 0 12
303 13 0 0 1 12
304 16 1 0 0 12
305 14 0 0 1 12
306 8 0 0 0 12
307 16 0 0 0 12
308 16 1 0 1 12
309 12 0 1 0 12
310 11 0 0 0 12
311 16 0 0 0 12
312 9 0 0 0 12
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) B `2B` `3B` Month
11.3073 0.1029 -0.1205 0.1935 0.0625
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.16007 -1.87085 0.06778 2.06778 7.06778
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.3073 1.9499 5.799 1.66e-08 ***
B 0.1029 0.2130 0.483 0.630
`2B` -0.1205 0.2230 -0.540 0.589
`3B` 0.1935 0.2247 0.861 0.390
Month 0.0625 0.1870 0.334 0.738
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.945 on 307 degrees of freedom
Multiple R-squared: 0.00401, Adjusted R-squared: -0.008967
F-statistic: 0.309 on 4 and 307 DF, p-value: 0.8719
> 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,] 0.58523587 0.8295283 0.4147641
[2,] 0.53103836 0.9379233 0.4689616
[3,] 0.38838495 0.7767699 0.6116150
[4,] 0.42048571 0.8409714 0.5795143
[5,] 0.59080431 0.8183914 0.4091957
[6,] 0.51362240 0.9727552 0.4863776
[7,] 0.58630600 0.8273880 0.4136940
[8,] 0.49337995 0.9867599 0.5066200
[9,] 0.41038801 0.8207760 0.5896120
[10,] 0.38902717 0.7780543 0.6109728
[11,] 0.78915760 0.4216848 0.2108424
[12,] 0.75823970 0.4835206 0.2417603
[13,] 0.77335925 0.4532815 0.2266408
[14,] 0.80780939 0.3843812 0.1921906
[15,] 0.75862457 0.4827509 0.2413754
[16,] 0.70184143 0.5963171 0.2981586
[17,] 0.64602116 0.7079577 0.3539788
[18,] 0.58247587 0.8350483 0.4175241
[19,] 0.52073210 0.9585358 0.4792679
[20,] 0.48069177 0.9613835 0.5193082
[21,] 0.43621211 0.8724242 0.5637879
[22,] 0.39448958 0.7889792 0.6055104
[23,] 0.35064794 0.7012959 0.6493521
[24,] 0.29901809 0.5980362 0.7009819
[25,] 0.25979887 0.5195977 0.7402011
[26,] 0.25266757 0.5053351 0.7473324
[27,] 0.24015361 0.4803072 0.7598464
[28,] 0.26510332 0.5302066 0.7348967
[29,] 0.28755889 0.5751178 0.7124411
[30,] 0.24484442 0.4896888 0.7551556
[31,] 0.25709823 0.5141965 0.7429018
[32,] 0.27500561 0.5500112 0.7249944
[33,] 0.23569650 0.4713930 0.7643035
[34,] 0.22393564 0.4478713 0.7760644
[35,] 0.49160914 0.9832183 0.5083909
[36,] 0.44357563 0.8871513 0.5564244
[37,] 0.53802823 0.9239435 0.4619718
[38,] 0.55334538 0.8933092 0.4466546
[39,] 0.50887982 0.9822404 0.4911202
[40,] 0.53665120 0.9266976 0.4633488
[41,] 0.55608802 0.8878240 0.4439120
[42,] 0.53172022 0.9365596 0.4682798
[43,] 0.54403315 0.9119337 0.4559668
[44,] 0.51469432 0.9706114 0.4853057
[45,] 0.48380025 0.9676005 0.5161998
[46,] 0.45181731 0.9036346 0.5481827
[47,] 0.40958319 0.8191664 0.5904168
[48,] 0.36762480 0.7352496 0.6323752
[49,] 0.32816930 0.6563386 0.6718307
[50,] 0.30048254 0.6009651 0.6995175
[51,] 0.28878896 0.5775779 0.7112110
[52,] 0.29609968 0.5921994 0.7039003
[53,] 0.26053153 0.5210631 0.7394685
[54,] 0.24763428 0.4952686 0.7523657
[55,] 0.22683921 0.4536784 0.7731608
[56,] 0.26427971 0.5285594 0.7357203
[57,] 0.28707599 0.5741520 0.7129240
[58,] 0.26335079 0.5267016 0.7366492
[59,] 0.24208251 0.4841650 0.7579175
[60,] 0.21171162 0.4234232 0.7882884
[61,] 0.19140752 0.3828150 0.8085925
[62,] 0.25277959 0.5055592 0.7472204
[63,] 0.40583627 0.8116725 0.5941637
[64,] 0.39827540 0.7965508 0.6017246
[65,] 0.44647484 0.8929497 0.5535252
[66,] 0.43200194 0.8640039 0.5679981
[67,] 0.39677438 0.7935488 0.6032256
[68,] 0.36328353 0.7265671 0.6367165
[69,] 0.34817898 0.6963580 0.6518210
[70,] 0.31397440 0.6279488 0.6860256
[71,] 0.32179268 0.6435854 0.6782073
[72,] 0.39779470 0.7955894 0.6022053
[73,] 0.37778321 0.7555664 0.6222168
[74,] 0.36044316 0.7208863 0.6395568
[75,] 0.42722681 0.8544536 0.5727732
[76,] 0.39776080 0.7955216 0.6022392
[77,] 0.36261027 0.7252205 0.6373897
[78,] 0.33857024 0.6771405 0.6614298
[79,] 0.30581479 0.6116296 0.6941852
[80,] 0.28943804 0.5788761 0.7105620
[81,] 0.25924585 0.5184917 0.7407541
[82,] 0.24830742 0.4966148 0.7516926
[83,] 0.22279801 0.4455960 0.7772020
[84,] 0.24700708 0.4940142 0.7529929
[85,] 0.24693844 0.4938769 0.7530616
[86,] 0.35471105 0.7094221 0.6452890
[87,] 0.32376123 0.6475225 0.6762388
[88,] 0.30511930 0.6102386 0.6948807
[89,] 0.27906669 0.5581334 0.7209333
[90,] 0.31672007 0.6334401 0.6832799
[91,] 0.38020519 0.7604104 0.6197948
[92,] 0.36954185 0.7390837 0.6304581
[93,] 0.35343284 0.7068657 0.6465672
[94,] 0.32476436 0.6495287 0.6752356
[95,] 0.32527000 0.6505400 0.6747300
[96,] 0.48114240 0.9622848 0.5188576
[97,] 0.47636346 0.9527269 0.5236365
[98,] 0.44939097 0.8987819 0.5506090
[99,] 0.41530034 0.8306007 0.5846997
[100,] 0.53061887 0.9387623 0.4693811
[101,] 0.59077294 0.8184541 0.4092271
[102,] 0.56139180 0.8772164 0.4386082
[103,] 0.59288992 0.8142202 0.4071101
[104,] 0.57409933 0.8518013 0.4259007
[105,] 0.60158627 0.7968275 0.3984137
[106,] 0.60243028 0.7951394 0.3975697
[107,] 0.62780525 0.7443895 0.3721947
[108,] 0.59888219 0.8022356 0.4011178
[109,] 0.57687843 0.8462431 0.4231216
[110,] 0.59858452 0.8028310 0.4014155
[111,] 0.56928521 0.8614296 0.4307148
[112,] 0.55135003 0.8972999 0.4486500
[113,] 0.55441118 0.8911776 0.4455888
[114,] 0.58048617 0.8390277 0.4195138
[115,] 0.61353882 0.7729224 0.3864612
[116,] 0.58699643 0.8260071 0.4130036
[117,] 0.55430680 0.8913864 0.4456932
[118,] 0.52894955 0.9421009 0.4710505
[119,] 0.51013975 0.9797205 0.4898602
[120,] 0.48109918 0.9621984 0.5189008
[121,] 0.46284449 0.9256890 0.5371555
[122,] 0.43222497 0.8644499 0.5677750
[123,] 0.39990113 0.7998023 0.6000989
[124,] 0.60701662 0.7859668 0.3929834
[125,] 0.60293316 0.7941337 0.3970668
[126,] 0.58719099 0.8256180 0.4128090
[127,] 0.55866987 0.8826603 0.4413301
[128,] 0.55043871 0.8991226 0.4495613
[129,] 0.51784618 0.9643076 0.4821538
[130,] 0.48849125 0.9769825 0.5115087
[131,] 0.51969111 0.9606178 0.4803089
[132,] 0.50720828 0.9855834 0.4927917
[133,] 0.50490337 0.9901933 0.4950966
[134,] 0.52764607 0.9447079 0.4723539
[135,] 0.55111464 0.8977707 0.4488854
[136,] 0.53240743 0.9351851 0.4675926
[137,] 0.51145998 0.9770800 0.4885400
[138,] 0.47902368 0.9580474 0.5209763
[139,] 0.47205798 0.9441160 0.5279420
[140,] 0.44252007 0.8850401 0.5574799
[141,] 0.47257134 0.9451427 0.5274287
[142,] 0.45827862 0.9165572 0.5417214
[143,] 0.47909467 0.9581893 0.5209053
[144,] 0.51516913 0.9696617 0.4848309
[145,] 0.54174354 0.9165129 0.4582565
[146,] 0.51048610 0.9790278 0.4895139
[147,] 0.48065059 0.9613012 0.5193494
[148,] 0.51129217 0.9774157 0.4887078
[149,] 0.50270349 0.9945930 0.4972965
[150,] 0.48712013 0.9742403 0.5128799
[151,] 0.45474440 0.9094888 0.5452556
[152,] 0.48033033 0.9606607 0.5196697
[153,] 0.45923264 0.9184653 0.5407674
[154,] 0.42770843 0.8554169 0.5722916
[155,] 0.45656923 0.9131385 0.5434308
[156,] 0.44056312 0.8811262 0.5594369
[157,] 0.45442773 0.9088555 0.5455723
[158,] 0.44132997 0.8826599 0.5586700
[159,] 0.46591473 0.9318295 0.5340853
[160,] 0.43360998 0.8672200 0.5663900
[161,] 0.42452565 0.8490513 0.5754743
[162,] 0.40650888 0.8130178 0.5934911
[163,] 0.38862372 0.7772474 0.6113763
[164,] 0.35838465 0.7167693 0.6416154
[165,] 0.32864638 0.6572928 0.6713536
[166,] 0.31152725 0.6230545 0.6884728
[167,] 0.49182670 0.9836534 0.5081733
[168,] 0.47507597 0.9501519 0.5249240
[169,] 0.47896245 0.9579249 0.5210376
[170,] 0.50709054 0.9858189 0.4929095
[171,] 0.47483942 0.9496788 0.5251606
[172,] 0.44253892 0.8850778 0.5574611
[173,] 0.41037443 0.8207489 0.5896256
[174,] 0.37889613 0.7577923 0.6211039
[175,] 0.34831223 0.6966245 0.6516878
[176,] 0.32101092 0.6420218 0.6789891
[177,] 0.29684574 0.5936915 0.7031543
[178,] 0.27665122 0.5533024 0.7233488
[179,] 0.25435344 0.5087069 0.7456466
[180,] 0.23099763 0.4619953 0.7690024
[181,] 0.21114796 0.4222959 0.7888520
[182,] 0.21361536 0.4272307 0.7863846
[183,] 0.21248066 0.4249613 0.7875193
[184,] 0.21709137 0.4341827 0.7829086
[185,] 0.23750539 0.4750108 0.7624946
[186,] 0.21590042 0.4318008 0.7840996
[187,] 0.21682480 0.4336496 0.7831752
[188,] 0.24006346 0.4801269 0.7599365
[189,] 0.21523530 0.4304706 0.7847647
[190,] 0.21584018 0.4316804 0.7841598
[191,] 0.33824960 0.6764992 0.6617504
[192,] 0.31198115 0.6239623 0.6880188
[193,] 0.37969353 0.7593871 0.6203065
[194,] 0.38941797 0.7788359 0.6105820
[195,] 0.36652581 0.7330516 0.6334742
[196,] 0.39066959 0.7813392 0.6093304
[197,] 0.42513640 0.8502728 0.5748636
[198,] 0.41031906 0.8206381 0.5896809
[199,] 0.43870270 0.8774054 0.5612973
[200,] 0.40823647 0.8164729 0.5917635
[201,] 0.38147028 0.7629406 0.6185297
[202,] 0.35581413 0.7116283 0.6441859
[203,] 0.32707154 0.6541431 0.6729285
[204,] 0.29653367 0.5930673 0.7034663
[205,] 0.26750677 0.5350135 0.7324932
[206,] 0.25151102 0.5030220 0.7484890
[207,] 0.23923817 0.4784763 0.7607618
[208,] 0.23414706 0.4682941 0.7658529
[209,] 0.20831675 0.4166335 0.7916833
[210,] 0.19094388 0.3818878 0.8090561
[211,] 0.17729464 0.3545893 0.8227054
[212,] 0.18667384 0.3733477 0.8133262
[213,] 0.20274233 0.4054847 0.7972577
[214,] 0.18521142 0.3704228 0.8147886
[215,] 0.17674246 0.3534849 0.8232575
[216,] 0.15498515 0.3099703 0.8450149
[217,] 0.14853056 0.2970611 0.8514694
[218,] 0.17731922 0.3546384 0.8226808
[219,] 0.32285183 0.6457037 0.6771482
[220,] 0.32894893 0.6578979 0.6710511
[221,] 0.33989144 0.6797829 0.6601086
[222,] 0.31537726 0.6307545 0.6846227
[223,] 0.29103743 0.5820749 0.7089626
[224,] 0.26765217 0.5353043 0.7323478
[225,] 0.24714265 0.4942853 0.7528574
[226,] 0.22089641 0.4417928 0.7791036
[227,] 0.23266955 0.4653391 0.7673305
[228,] 0.27159425 0.5431885 0.7284057
[229,] 0.26340328 0.5268066 0.7365967
[230,] 0.24277542 0.4855508 0.7572246
[231,] 0.31473732 0.6294746 0.6852627
[232,] 0.28269197 0.5653839 0.7173080
[233,] 0.25094566 0.5018913 0.7490543
[234,] 0.24217401 0.4843480 0.7578260
[235,] 0.21308363 0.4261673 0.7869164
[236,] 0.20134116 0.4026823 0.7986588
[237,] 0.17641361 0.3528272 0.8235864
[238,] 0.15719430 0.3143886 0.8428057
[239,] 0.13951702 0.2790340 0.8604830
[240,] 0.14743785 0.2948757 0.8525621
[241,] 0.14638364 0.2927673 0.8536164
[242,] 0.20735146 0.4147029 0.7926485
[243,] 0.18004832 0.3600966 0.8199517
[244,] 0.17255900 0.3451180 0.8274410
[245,] 0.14915191 0.2983038 0.8508481
[246,] 0.15876314 0.3175263 0.8412369
[247,] 0.19516903 0.3903381 0.8048310
[248,] 0.19567113 0.3913423 0.8043289
[249,] 0.18216306 0.3643261 0.8178369
[250,] 0.15824875 0.3164975 0.8417513
[251,] 0.15770814 0.3154163 0.8422919
[252,] 0.30063302 0.6012660 0.6993670
[253,] 0.28934286 0.5786857 0.7106571
[254,] 0.25878555 0.5175711 0.7412144
[255,] 0.22401121 0.4480224 0.7759888
[256,] 0.32917546 0.6583509 0.6708245
[257,] 0.43082799 0.8616560 0.5691720
[258,] 0.38595815 0.7719163 0.6140419
[259,] 0.40370948 0.8074190 0.5962905
[260,] 0.39535262 0.7907052 0.6046474
[261,] 0.42013750 0.8402750 0.5798625
[262,] 0.43735543 0.8747109 0.5626446
[263,] 0.45944741 0.9188948 0.5405526
[264,] 0.41581375 0.8316275 0.5841862
[265,] 0.37035150 0.7407030 0.6296485
[266,] 0.41648018 0.8329604 0.5835198
[267,] 0.37021065 0.7404213 0.6297894
[268,] 0.40326072 0.8065214 0.5967393
[269,] 0.35886213 0.7177243 0.6411379
[270,] 0.41065415 0.8213083 0.5893459
[271,] 0.47090297 0.9418059 0.5290970
[272,] 0.42603676 0.8520735 0.5739632
[273,] 0.37557820 0.7511564 0.6244218
[274,] 0.36429114 0.7285823 0.6357089
[275,] 0.32009769 0.6401954 0.6799023
[276,] 0.28773183 0.5754637 0.7122682
[277,] 0.24130767 0.4826153 0.7586923
[278,] 0.19699457 0.3939891 0.8030054
[279,] 0.17821401 0.3564280 0.8217860
[280,] 0.77568150 0.4486370 0.2243185
[281,] 0.78191511 0.4361698 0.2180849
[282,] 0.78444269 0.4311146 0.2155573
[283,] 0.73450026 0.5309995 0.2654997
[284,] 0.68810130 0.6237974 0.3118987
[285,] 0.61694690 0.7661062 0.3830531
[286,] 0.58074666 0.8385067 0.4192533
[287,] 0.67949525 0.6410095 0.3205048
[288,] 0.68762990 0.6247402 0.3123701
[289,] 0.60841504 0.7831699 0.3915850
[290,] 0.61083266 0.7783347 0.3891673
[291,] 0.52753990 0.9449202 0.4724601
[292,] 0.45135626 0.9027125 0.5486437
[293,] 0.36525214 0.7305043 0.6347479
[294,] 0.26700959 0.5340192 0.7329904
[295,] 0.23519632 0.4703926 0.7648037
[296,] 0.14631345 0.2926269 0.8536866
[297,] 0.08435575 0.1687115 0.9156442
> postscript(file="/var/www/html/rcomp/tmp/12jjp1292680918.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22jjp1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/32jjp1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4daia1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5daia1292680918.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 = 312
Frequency = 1
1 2 3 4 5 6
2.924545480 -0.239188189 -2.925178185 -1.869720370 1.130279630 4.027412555
7 8 9 10 11 12
2.130279630 4.147885643 -1.869720370 -4.331486618 -0.035082626 3.067784449
13 14 15 16 17 18
2.188257537 1.698316629 0.012326633 0.067784449 -1.932215551 -7.932215551
19 20 21 22 23 24
2.188257537 3.067784449 4.067784449 0.188257537 0.067784449 -0.125752468
25 26 27 28 29 30
-0.166124361 0.130279630 -1.130912335 -0.972587445 -0.852114357 -0.852114357
31 32 33 34 35 36
-1.251385423 -0.869720370 3.130279630 3.130279630 -2.869720370 4.130279630
37 38 39 40 41 42
1.177688890 -2.972587445 4.130279630 0.130279630 2.743205798 -6.869720370
43 44 45 46 47 48
-0.834508344 5.165491656 -2.942784198 1.130279630 4.067784449 4.067784449
49 50 51 52 53 54
1.474976466 3.964917374 -0.932215551 -0.932215551 -0.932215551 0.067784449
55 56 57 58 59 60
-0.108146455 0.067784449 2.067784449 -2.137949702 -2.691269375 -0.125752468
61 62 63 64 65 66
-1.932215551 2.067784449 -3.932215551 3.964917374 1.964917374 2.067784449
67 68 69 70 71 72
0.067784449 1.964917374 -5.108146455 7.067784449 3.067784449 -3.932215551
73 74 75 76 77 78
-1.932215551 1.067784449 1.130279630 -1.869720370 0.130279630 3.057215802
79 80 81 82 83 84
-4.972587445 2.130279630 -1.869720370 -5.508301106 -1.075454520 0.130279630
85 86 87 88 89 90
1.743205798 0.130279630 2.130279630 -0.834508344 -2.166124361 0.936742714
91 92 93 94 95 96
-3.869720370 -2.869720370 -5.932215551 -0.422156459 2.067784449 -0.932215551
97 98 99 100 101 102
-4.228619543 -4.932215551 -2.884806292 1.982523386 1.067784449 3.067784449
103 104 105 106 107 108
-6.932215551 2.879656311 1.188257537 0.067784449 -6.035082626 -4.932215551
109 110 111 112 113 114
1.067784449 4.085390462 -1.932215551 4.067784449 3.067784449 -3.932215551
115 116 117 118 119 120
-0.932215551 1.235666796 3.964917374 -0.932215551 2.067784449 -3.017476614
121 122 123 124 125 126
-3.932215551 -4.228619543 -1.137949702 0.067784449 -1.343683852 1.801183704
127 128 129 130 131 132
-1.017476614 2.067784449 0.595449554 0.067784449 -7.932215551 2.788986470
133 134 135 136 137 138
-1.932215551 1.012326633 2.698316629 0.012326633 1.067784449 -3.932215551
139 140 141 142 143 144
-2.137949702 3.067784449 4.067784449 4.067784449 2.067784449 1.891853545
145 146 147 148 149 150
-0.108146455 2.863678886 0.954348727 4.067784449 2.067784449 -3.932215551
151 152 153 154 155 156
4.188257537 3.891853545 0.067784449 -0.764333204 3.891853545 -2.932215551
157 158 159 160 161 162
2.442284531 -0.097577808 -3.678188557 -1.736158544 0.442284531 4.022895280
163 164 165 166 167 168
2.143368368 3.606018201 -2.170641636 -3.994710733 -0.079971795 2.515348360
169 170 171 172 173 174
1.902422192 1.902422192 -0.291114724 0.052698527 -1.826828385 -7.753764557
175 176 177 178 179 180
2.052698527 3.005289267 3.902422192 -0.200444883 0.005289267 0.125762355
181 182 183 184 185 186
0.246235443 -0.097577808 -0.977104720 -1.303311958 -1.454848394 -1.261311477
187 188 189 190 191 192
-1.097577808 -1.393981799 2.902422192 2.708885276 -3.140838389 4.052698527
193 194 195 196 197 198
0.811752351 -2.959498707 4.143368368 -0.097577808 2.920028205 -6.633291469
199 200 201 202 203 204
-0.856631632 4.799555117 -2.947301473 0.799555117 3.932225439 4.125762355
205 206 207 208 209 210
2.022895280 3.932225439 -1.097577808 -1.261311477 -1.050168548 -0.097577808
211 212 213 214 215 216
-0.170641636 0.022895280 1.829358364 -2.484651640 -2.874237645 0.125762355
217 218 219 220 221 222
-2.097577808 2.040501293 -4.097577808 4.052698527 1.545151606 1.811752351
223 224 225 226 227 228
0.125762355 2.125762355 -4.874237645 6.920028205 2.618215435 -3.994710733
229 230 231 232 233 234
-2.097577808 1.022895280 0.321811443 -2.291114724 -0.097577808 2.811752351
235 236 237 238 239 240
-5.188247649 1.811752351 -2.170641636 -6.200444883 -0.977104720 0.040501293
241 242 243 244 245 246
1.817161130 0.143368368 1.738688523 -1.273508711 -2.097577808 0.738688523
247 248 249 250 251 252
-4.188247649 -3.974592548 -5.977104720 -0.097577808 2.005289267 -0.959498707
253 254 255 256 257 258
-4.188247649 -5.097577808 -2.753764557 1.817161130 1.005289267 2.738688523
259 260 261 262 263 264
-7.364178552 2.859161611 1.125762355 -0.188247649 -6.097577808 -5.097577808
265 266 267 268 269 270
0.696688042 3.799555117 -1.874237645 3.902422192 2.902422192 -4.291114724
271 272 273 274 275 276
-1.097577808 0.635821448 4.052698527 -1.454848394 1.932225439 -3.381784565
277 278 279 280 281 282
-3.936732826 -4.262940065 -0.798653726 0.183740262 -1.142466977 1.839927010
283 284 285 286 287 288
-1.160072990 1.960400098 0.749257169 -0.039599902 -8.160072990 2.942794086
289 290 291 292 293 294
-2.250742831 0.942794086 2.555720253 -0.057205914 0.839927010 -4.057205914
295 296 297 298 299 300
-2.250742831 2.839927010 4.063267174 3.839927010 1.942794086 1.839927010
301 302 303 304 305 306
-0.057205914 2.942794086 0.749257169 3.839927010 1.749257169 -4.057205914
307 308 309 310 311 312
3.942794086 3.646390094 0.063267174 -1.057205914 3.942794086 -3.057205914
> postscript(file="/var/www/html/rcomp/tmp/6daia1292680918.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 = 312
Frequency = 1
lag(myerror, k = 1) myerror
0 2.924545480 NA
1 -0.239188189 2.924545480
2 -2.925178185 -0.239188189
3 -1.869720370 -2.925178185
4 1.130279630 -1.869720370
5 4.027412555 1.130279630
6 2.130279630 4.027412555
7 4.147885643 2.130279630
8 -1.869720370 4.147885643
9 -4.331486618 -1.869720370
10 -0.035082626 -4.331486618
11 3.067784449 -0.035082626
12 2.188257537 3.067784449
13 1.698316629 2.188257537
14 0.012326633 1.698316629
15 0.067784449 0.012326633
16 -1.932215551 0.067784449
17 -7.932215551 -1.932215551
18 2.188257537 -7.932215551
19 3.067784449 2.188257537
20 4.067784449 3.067784449
21 0.188257537 4.067784449
22 0.067784449 0.188257537
23 -0.125752468 0.067784449
24 -0.166124361 -0.125752468
25 0.130279630 -0.166124361
26 -1.130912335 0.130279630
27 -0.972587445 -1.130912335
28 -0.852114357 -0.972587445
29 -0.852114357 -0.852114357
30 -1.251385423 -0.852114357
31 -0.869720370 -1.251385423
32 3.130279630 -0.869720370
33 3.130279630 3.130279630
34 -2.869720370 3.130279630
35 4.130279630 -2.869720370
36 1.177688890 4.130279630
37 -2.972587445 1.177688890
38 4.130279630 -2.972587445
39 0.130279630 4.130279630
40 2.743205798 0.130279630
41 -6.869720370 2.743205798
42 -0.834508344 -6.869720370
43 5.165491656 -0.834508344
44 -2.942784198 5.165491656
45 1.130279630 -2.942784198
46 4.067784449 1.130279630
47 4.067784449 4.067784449
48 1.474976466 4.067784449
49 3.964917374 1.474976466
50 -0.932215551 3.964917374
51 -0.932215551 -0.932215551
52 -0.932215551 -0.932215551
53 0.067784449 -0.932215551
54 -0.108146455 0.067784449
55 0.067784449 -0.108146455
56 2.067784449 0.067784449
57 -2.137949702 2.067784449
58 -2.691269375 -2.137949702
59 -0.125752468 -2.691269375
60 -1.932215551 -0.125752468
61 2.067784449 -1.932215551
62 -3.932215551 2.067784449
63 3.964917374 -3.932215551
64 1.964917374 3.964917374
65 2.067784449 1.964917374
66 0.067784449 2.067784449
67 1.964917374 0.067784449
68 -5.108146455 1.964917374
69 7.067784449 -5.108146455
70 3.067784449 7.067784449
71 -3.932215551 3.067784449
72 -1.932215551 -3.932215551
73 1.067784449 -1.932215551
74 1.130279630 1.067784449
75 -1.869720370 1.130279630
76 0.130279630 -1.869720370
77 3.057215802 0.130279630
78 -4.972587445 3.057215802
79 2.130279630 -4.972587445
80 -1.869720370 2.130279630
81 -5.508301106 -1.869720370
82 -1.075454520 -5.508301106
83 0.130279630 -1.075454520
84 1.743205798 0.130279630
85 0.130279630 1.743205798
86 2.130279630 0.130279630
87 -0.834508344 2.130279630
88 -2.166124361 -0.834508344
89 0.936742714 -2.166124361
90 -3.869720370 0.936742714
91 -2.869720370 -3.869720370
92 -5.932215551 -2.869720370
93 -0.422156459 -5.932215551
94 2.067784449 -0.422156459
95 -0.932215551 2.067784449
96 -4.228619543 -0.932215551
97 -4.932215551 -4.228619543
98 -2.884806292 -4.932215551
99 1.982523386 -2.884806292
100 1.067784449 1.982523386
101 3.067784449 1.067784449
102 -6.932215551 3.067784449
103 2.879656311 -6.932215551
104 1.188257537 2.879656311
105 0.067784449 1.188257537
106 -6.035082626 0.067784449
107 -4.932215551 -6.035082626
108 1.067784449 -4.932215551
109 4.085390462 1.067784449
110 -1.932215551 4.085390462
111 4.067784449 -1.932215551
112 3.067784449 4.067784449
113 -3.932215551 3.067784449
114 -0.932215551 -3.932215551
115 1.235666796 -0.932215551
116 3.964917374 1.235666796
117 -0.932215551 3.964917374
118 2.067784449 -0.932215551
119 -3.017476614 2.067784449
120 -3.932215551 -3.017476614
121 -4.228619543 -3.932215551
122 -1.137949702 -4.228619543
123 0.067784449 -1.137949702
124 -1.343683852 0.067784449
125 1.801183704 -1.343683852
126 -1.017476614 1.801183704
127 2.067784449 -1.017476614
128 0.595449554 2.067784449
129 0.067784449 0.595449554
130 -7.932215551 0.067784449
131 2.788986470 -7.932215551
132 -1.932215551 2.788986470
133 1.012326633 -1.932215551
134 2.698316629 1.012326633
135 0.012326633 2.698316629
136 1.067784449 0.012326633
137 -3.932215551 1.067784449
138 -2.137949702 -3.932215551
139 3.067784449 -2.137949702
140 4.067784449 3.067784449
141 4.067784449 4.067784449
142 2.067784449 4.067784449
143 1.891853545 2.067784449
144 -0.108146455 1.891853545
145 2.863678886 -0.108146455
146 0.954348727 2.863678886
147 4.067784449 0.954348727
148 2.067784449 4.067784449
149 -3.932215551 2.067784449
150 4.188257537 -3.932215551
151 3.891853545 4.188257537
152 0.067784449 3.891853545
153 -0.764333204 0.067784449
154 3.891853545 -0.764333204
155 -2.932215551 3.891853545
156 2.442284531 -2.932215551
157 -0.097577808 2.442284531
158 -3.678188557 -0.097577808
159 -1.736158544 -3.678188557
160 0.442284531 -1.736158544
161 4.022895280 0.442284531
162 2.143368368 4.022895280
163 3.606018201 2.143368368
164 -2.170641636 3.606018201
165 -3.994710733 -2.170641636
166 -0.079971795 -3.994710733
167 2.515348360 -0.079971795
168 1.902422192 2.515348360
169 1.902422192 1.902422192
170 -0.291114724 1.902422192
171 0.052698527 -0.291114724
172 -1.826828385 0.052698527
173 -7.753764557 -1.826828385
174 2.052698527 -7.753764557
175 3.005289267 2.052698527
176 3.902422192 3.005289267
177 -0.200444883 3.902422192
178 0.005289267 -0.200444883
179 0.125762355 0.005289267
180 0.246235443 0.125762355
181 -0.097577808 0.246235443
182 -0.977104720 -0.097577808
183 -1.303311958 -0.977104720
184 -1.454848394 -1.303311958
185 -1.261311477 -1.454848394
186 -1.097577808 -1.261311477
187 -1.393981799 -1.097577808
188 2.902422192 -1.393981799
189 2.708885276 2.902422192
190 -3.140838389 2.708885276
191 4.052698527 -3.140838389
192 0.811752351 4.052698527
193 -2.959498707 0.811752351
194 4.143368368 -2.959498707
195 -0.097577808 4.143368368
196 2.920028205 -0.097577808
197 -6.633291469 2.920028205
198 -0.856631632 -6.633291469
199 4.799555117 -0.856631632
200 -2.947301473 4.799555117
201 0.799555117 -2.947301473
202 3.932225439 0.799555117
203 4.125762355 3.932225439
204 2.022895280 4.125762355
205 3.932225439 2.022895280
206 -1.097577808 3.932225439
207 -1.261311477 -1.097577808
208 -1.050168548 -1.261311477
209 -0.097577808 -1.050168548
210 -0.170641636 -0.097577808
211 0.022895280 -0.170641636
212 1.829358364 0.022895280
213 -2.484651640 1.829358364
214 -2.874237645 -2.484651640
215 0.125762355 -2.874237645
216 -2.097577808 0.125762355
217 2.040501293 -2.097577808
218 -4.097577808 2.040501293
219 4.052698527 -4.097577808
220 1.545151606 4.052698527
221 1.811752351 1.545151606
222 0.125762355 1.811752351
223 2.125762355 0.125762355
224 -4.874237645 2.125762355
225 6.920028205 -4.874237645
226 2.618215435 6.920028205
227 -3.994710733 2.618215435
228 -2.097577808 -3.994710733
229 1.022895280 -2.097577808
230 0.321811443 1.022895280
231 -2.291114724 0.321811443
232 -0.097577808 -2.291114724
233 2.811752351 -0.097577808
234 -5.188247649 2.811752351
235 1.811752351 -5.188247649
236 -2.170641636 1.811752351
237 -6.200444883 -2.170641636
238 -0.977104720 -6.200444883
239 0.040501293 -0.977104720
240 1.817161130 0.040501293
241 0.143368368 1.817161130
242 1.738688523 0.143368368
243 -1.273508711 1.738688523
244 -2.097577808 -1.273508711
245 0.738688523 -2.097577808
246 -4.188247649 0.738688523
247 -3.974592548 -4.188247649
248 -5.977104720 -3.974592548
249 -0.097577808 -5.977104720
250 2.005289267 -0.097577808
251 -0.959498707 2.005289267
252 -4.188247649 -0.959498707
253 -5.097577808 -4.188247649
254 -2.753764557 -5.097577808
255 1.817161130 -2.753764557
256 1.005289267 1.817161130
257 2.738688523 1.005289267
258 -7.364178552 2.738688523
259 2.859161611 -7.364178552
260 1.125762355 2.859161611
261 -0.188247649 1.125762355
262 -6.097577808 -0.188247649
263 -5.097577808 -6.097577808
264 0.696688042 -5.097577808
265 3.799555117 0.696688042
266 -1.874237645 3.799555117
267 3.902422192 -1.874237645
268 2.902422192 3.902422192
269 -4.291114724 2.902422192
270 -1.097577808 -4.291114724
271 0.635821448 -1.097577808
272 4.052698527 0.635821448
273 -1.454848394 4.052698527
274 1.932225439 -1.454848394
275 -3.381784565 1.932225439
276 -3.936732826 -3.381784565
277 -4.262940065 -3.936732826
278 -0.798653726 -4.262940065
279 0.183740262 -0.798653726
280 -1.142466977 0.183740262
281 1.839927010 -1.142466977
282 -1.160072990 1.839927010
283 1.960400098 -1.160072990
284 0.749257169 1.960400098
285 -0.039599902 0.749257169
286 -8.160072990 -0.039599902
287 2.942794086 -8.160072990
288 -2.250742831 2.942794086
289 0.942794086 -2.250742831
290 2.555720253 0.942794086
291 -0.057205914 2.555720253
292 0.839927010 -0.057205914
293 -4.057205914 0.839927010
294 -2.250742831 -4.057205914
295 2.839927010 -2.250742831
296 4.063267174 2.839927010
297 3.839927010 4.063267174
298 1.942794086 3.839927010
299 1.839927010 1.942794086
300 -0.057205914 1.839927010
301 2.942794086 -0.057205914
302 0.749257169 2.942794086
303 3.839927010 0.749257169
304 1.749257169 3.839927010
305 -4.057205914 1.749257169
306 3.942794086 -4.057205914
307 3.646390094 3.942794086
308 0.063267174 3.646390094
309 -1.057205914 0.063267174
310 3.942794086 -1.057205914
311 -3.057205914 3.942794086
312 NA -3.057205914
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.239188189 2.924545480
[2,] -2.925178185 -0.239188189
[3,] -1.869720370 -2.925178185
[4,] 1.130279630 -1.869720370
[5,] 4.027412555 1.130279630
[6,] 2.130279630 4.027412555
[7,] 4.147885643 2.130279630
[8,] -1.869720370 4.147885643
[9,] -4.331486618 -1.869720370
[10,] -0.035082626 -4.331486618
[11,] 3.067784449 -0.035082626
[12,] 2.188257537 3.067784449
[13,] 1.698316629 2.188257537
[14,] 0.012326633 1.698316629
[15,] 0.067784449 0.012326633
[16,] -1.932215551 0.067784449
[17,] -7.932215551 -1.932215551
[18,] 2.188257537 -7.932215551
[19,] 3.067784449 2.188257537
[20,] 4.067784449 3.067784449
[21,] 0.188257537 4.067784449
[22,] 0.067784449 0.188257537
[23,] -0.125752468 0.067784449
[24,] -0.166124361 -0.125752468
[25,] 0.130279630 -0.166124361
[26,] -1.130912335 0.130279630
[27,] -0.972587445 -1.130912335
[28,] -0.852114357 -0.972587445
[29,] -0.852114357 -0.852114357
[30,] -1.251385423 -0.852114357
[31,] -0.869720370 -1.251385423
[32,] 3.130279630 -0.869720370
[33,] 3.130279630 3.130279630
[34,] -2.869720370 3.130279630
[35,] 4.130279630 -2.869720370
[36,] 1.177688890 4.130279630
[37,] -2.972587445 1.177688890
[38,] 4.130279630 -2.972587445
[39,] 0.130279630 4.130279630
[40,] 2.743205798 0.130279630
[41,] -6.869720370 2.743205798
[42,] -0.834508344 -6.869720370
[43,] 5.165491656 -0.834508344
[44,] -2.942784198 5.165491656
[45,] 1.130279630 -2.942784198
[46,] 4.067784449 1.130279630
[47,] 4.067784449 4.067784449
[48,] 1.474976466 4.067784449
[49,] 3.964917374 1.474976466
[50,] -0.932215551 3.964917374
[51,] -0.932215551 -0.932215551
[52,] -0.932215551 -0.932215551
[53,] 0.067784449 -0.932215551
[54,] -0.108146455 0.067784449
[55,] 0.067784449 -0.108146455
[56,] 2.067784449 0.067784449
[57,] -2.137949702 2.067784449
[58,] -2.691269375 -2.137949702
[59,] -0.125752468 -2.691269375
[60,] -1.932215551 -0.125752468
[61,] 2.067784449 -1.932215551
[62,] -3.932215551 2.067784449
[63,] 3.964917374 -3.932215551
[64,] 1.964917374 3.964917374
[65,] 2.067784449 1.964917374
[66,] 0.067784449 2.067784449
[67,] 1.964917374 0.067784449
[68,] -5.108146455 1.964917374
[69,] 7.067784449 -5.108146455
[70,] 3.067784449 7.067784449
[71,] -3.932215551 3.067784449
[72,] -1.932215551 -3.932215551
[73,] 1.067784449 -1.932215551
[74,] 1.130279630 1.067784449
[75,] -1.869720370 1.130279630
[76,] 0.130279630 -1.869720370
[77,] 3.057215802 0.130279630
[78,] -4.972587445 3.057215802
[79,] 2.130279630 -4.972587445
[80,] -1.869720370 2.130279630
[81,] -5.508301106 -1.869720370
[82,] -1.075454520 -5.508301106
[83,] 0.130279630 -1.075454520
[84,] 1.743205798 0.130279630
[85,] 0.130279630 1.743205798
[86,] 2.130279630 0.130279630
[87,] -0.834508344 2.130279630
[88,] -2.166124361 -0.834508344
[89,] 0.936742714 -2.166124361
[90,] -3.869720370 0.936742714
[91,] -2.869720370 -3.869720370
[92,] -5.932215551 -2.869720370
[93,] -0.422156459 -5.932215551
[94,] 2.067784449 -0.422156459
[95,] -0.932215551 2.067784449
[96,] -4.228619543 -0.932215551
[97,] -4.932215551 -4.228619543
[98,] -2.884806292 -4.932215551
[99,] 1.982523386 -2.884806292
[100,] 1.067784449 1.982523386
[101,] 3.067784449 1.067784449
[102,] -6.932215551 3.067784449
[103,] 2.879656311 -6.932215551
[104,] 1.188257537 2.879656311
[105,] 0.067784449 1.188257537
[106,] -6.035082626 0.067784449
[107,] -4.932215551 -6.035082626
[108,] 1.067784449 -4.932215551
[109,] 4.085390462 1.067784449
[110,] -1.932215551 4.085390462
[111,] 4.067784449 -1.932215551
[112,] 3.067784449 4.067784449
[113,] -3.932215551 3.067784449
[114,] -0.932215551 -3.932215551
[115,] 1.235666796 -0.932215551
[116,] 3.964917374 1.235666796
[117,] -0.932215551 3.964917374
[118,] 2.067784449 -0.932215551
[119,] -3.017476614 2.067784449
[120,] -3.932215551 -3.017476614
[121,] -4.228619543 -3.932215551
[122,] -1.137949702 -4.228619543
[123,] 0.067784449 -1.137949702
[124,] -1.343683852 0.067784449
[125,] 1.801183704 -1.343683852
[126,] -1.017476614 1.801183704
[127,] 2.067784449 -1.017476614
[128,] 0.595449554 2.067784449
[129,] 0.067784449 0.595449554
[130,] -7.932215551 0.067784449
[131,] 2.788986470 -7.932215551
[132,] -1.932215551 2.788986470
[133,] 1.012326633 -1.932215551
[134,] 2.698316629 1.012326633
[135,] 0.012326633 2.698316629
[136,] 1.067784449 0.012326633
[137,] -3.932215551 1.067784449
[138,] -2.137949702 -3.932215551
[139,] 3.067784449 -2.137949702
[140,] 4.067784449 3.067784449
[141,] 4.067784449 4.067784449
[142,] 2.067784449 4.067784449
[143,] 1.891853545 2.067784449
[144,] -0.108146455 1.891853545
[145,] 2.863678886 -0.108146455
[146,] 0.954348727 2.863678886
[147,] 4.067784449 0.954348727
[148,] 2.067784449 4.067784449
[149,] -3.932215551 2.067784449
[150,] 4.188257537 -3.932215551
[151,] 3.891853545 4.188257537
[152,] 0.067784449 3.891853545
[153,] -0.764333204 0.067784449
[154,] 3.891853545 -0.764333204
[155,] -2.932215551 3.891853545
[156,] 2.442284531 -2.932215551
[157,] -0.097577808 2.442284531
[158,] -3.678188557 -0.097577808
[159,] -1.736158544 -3.678188557
[160,] 0.442284531 -1.736158544
[161,] 4.022895280 0.442284531
[162,] 2.143368368 4.022895280
[163,] 3.606018201 2.143368368
[164,] -2.170641636 3.606018201
[165,] -3.994710733 -2.170641636
[166,] -0.079971795 -3.994710733
[167,] 2.515348360 -0.079971795
[168,] 1.902422192 2.515348360
[169,] 1.902422192 1.902422192
[170,] -0.291114724 1.902422192
[171,] 0.052698527 -0.291114724
[172,] -1.826828385 0.052698527
[173,] -7.753764557 -1.826828385
[174,] 2.052698527 -7.753764557
[175,] 3.005289267 2.052698527
[176,] 3.902422192 3.005289267
[177,] -0.200444883 3.902422192
[178,] 0.005289267 -0.200444883
[179,] 0.125762355 0.005289267
[180,] 0.246235443 0.125762355
[181,] -0.097577808 0.246235443
[182,] -0.977104720 -0.097577808
[183,] -1.303311958 -0.977104720
[184,] -1.454848394 -1.303311958
[185,] -1.261311477 -1.454848394
[186,] -1.097577808 -1.261311477
[187,] -1.393981799 -1.097577808
[188,] 2.902422192 -1.393981799
[189,] 2.708885276 2.902422192
[190,] -3.140838389 2.708885276
[191,] 4.052698527 -3.140838389
[192,] 0.811752351 4.052698527
[193,] -2.959498707 0.811752351
[194,] 4.143368368 -2.959498707
[195,] -0.097577808 4.143368368
[196,] 2.920028205 -0.097577808
[197,] -6.633291469 2.920028205
[198,] -0.856631632 -6.633291469
[199,] 4.799555117 -0.856631632
[200,] -2.947301473 4.799555117
[201,] 0.799555117 -2.947301473
[202,] 3.932225439 0.799555117
[203,] 4.125762355 3.932225439
[204,] 2.022895280 4.125762355
[205,] 3.932225439 2.022895280
[206,] -1.097577808 3.932225439
[207,] -1.261311477 -1.097577808
[208,] -1.050168548 -1.261311477
[209,] -0.097577808 -1.050168548
[210,] -0.170641636 -0.097577808
[211,] 0.022895280 -0.170641636
[212,] 1.829358364 0.022895280
[213,] -2.484651640 1.829358364
[214,] -2.874237645 -2.484651640
[215,] 0.125762355 -2.874237645
[216,] -2.097577808 0.125762355
[217,] 2.040501293 -2.097577808
[218,] -4.097577808 2.040501293
[219,] 4.052698527 -4.097577808
[220,] 1.545151606 4.052698527
[221,] 1.811752351 1.545151606
[222,] 0.125762355 1.811752351
[223,] 2.125762355 0.125762355
[224,] -4.874237645 2.125762355
[225,] 6.920028205 -4.874237645
[226,] 2.618215435 6.920028205
[227,] -3.994710733 2.618215435
[228,] -2.097577808 -3.994710733
[229,] 1.022895280 -2.097577808
[230,] 0.321811443 1.022895280
[231,] -2.291114724 0.321811443
[232,] -0.097577808 -2.291114724
[233,] 2.811752351 -0.097577808
[234,] -5.188247649 2.811752351
[235,] 1.811752351 -5.188247649
[236,] -2.170641636 1.811752351
[237,] -6.200444883 -2.170641636
[238,] -0.977104720 -6.200444883
[239,] 0.040501293 -0.977104720
[240,] 1.817161130 0.040501293
[241,] 0.143368368 1.817161130
[242,] 1.738688523 0.143368368
[243,] -1.273508711 1.738688523
[244,] -2.097577808 -1.273508711
[245,] 0.738688523 -2.097577808
[246,] -4.188247649 0.738688523
[247,] -3.974592548 -4.188247649
[248,] -5.977104720 -3.974592548
[249,] -0.097577808 -5.977104720
[250,] 2.005289267 -0.097577808
[251,] -0.959498707 2.005289267
[252,] -4.188247649 -0.959498707
[253,] -5.097577808 -4.188247649
[254,] -2.753764557 -5.097577808
[255,] 1.817161130 -2.753764557
[256,] 1.005289267 1.817161130
[257,] 2.738688523 1.005289267
[258,] -7.364178552 2.738688523
[259,] 2.859161611 -7.364178552
[260,] 1.125762355 2.859161611
[261,] -0.188247649 1.125762355
[262,] -6.097577808 -0.188247649
[263,] -5.097577808 -6.097577808
[264,] 0.696688042 -5.097577808
[265,] 3.799555117 0.696688042
[266,] -1.874237645 3.799555117
[267,] 3.902422192 -1.874237645
[268,] 2.902422192 3.902422192
[269,] -4.291114724 2.902422192
[270,] -1.097577808 -4.291114724
[271,] 0.635821448 -1.097577808
[272,] 4.052698527 0.635821448
[273,] -1.454848394 4.052698527
[274,] 1.932225439 -1.454848394
[275,] -3.381784565 1.932225439
[276,] -3.936732826 -3.381784565
[277,] -4.262940065 -3.936732826
[278,] -0.798653726 -4.262940065
[279,] 0.183740262 -0.798653726
[280,] -1.142466977 0.183740262
[281,] 1.839927010 -1.142466977
[282,] -1.160072990 1.839927010
[283,] 1.960400098 -1.160072990
[284,] 0.749257169 1.960400098
[285,] -0.039599902 0.749257169
[286,] -8.160072990 -0.039599902
[287,] 2.942794086 -8.160072990
[288,] -2.250742831 2.942794086
[289,] 0.942794086 -2.250742831
[290,] 2.555720253 0.942794086
[291,] -0.057205914 2.555720253
[292,] 0.839927010 -0.057205914
[293,] -4.057205914 0.839927010
[294,] -2.250742831 -4.057205914
[295,] 2.839927010 -2.250742831
[296,] 4.063267174 2.839927010
[297,] 3.839927010 4.063267174
[298,] 1.942794086 3.839927010
[299,] 1.839927010 1.942794086
[300,] -0.057205914 1.839927010
[301,] 2.942794086 -0.057205914
[302,] 0.749257169 2.942794086
[303,] 3.839927010 0.749257169
[304,] 1.749257169 3.839927010
[305,] -4.057205914 1.749257169
[306,] 3.942794086 -4.057205914
[307,] 3.646390094 3.942794086
[308,] 0.063267174 3.646390094
[309,] -1.057205914 0.063267174
[310,] 3.942794086 -1.057205914
[311,] -3.057205914 3.942794086
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.239188189 2.924545480
2 -2.925178185 -0.239188189
3 -1.869720370 -2.925178185
4 1.130279630 -1.869720370
5 4.027412555 1.130279630
6 2.130279630 4.027412555
7 4.147885643 2.130279630
8 -1.869720370 4.147885643
9 -4.331486618 -1.869720370
10 -0.035082626 -4.331486618
11 3.067784449 -0.035082626
12 2.188257537 3.067784449
13 1.698316629 2.188257537
14 0.012326633 1.698316629
15 0.067784449 0.012326633
16 -1.932215551 0.067784449
17 -7.932215551 -1.932215551
18 2.188257537 -7.932215551
19 3.067784449 2.188257537
20 4.067784449 3.067784449
21 0.188257537 4.067784449
22 0.067784449 0.188257537
23 -0.125752468 0.067784449
24 -0.166124361 -0.125752468
25 0.130279630 -0.166124361
26 -1.130912335 0.130279630
27 -0.972587445 -1.130912335
28 -0.852114357 -0.972587445
29 -0.852114357 -0.852114357
30 -1.251385423 -0.852114357
31 -0.869720370 -1.251385423
32 3.130279630 -0.869720370
33 3.130279630 3.130279630
34 -2.869720370 3.130279630
35 4.130279630 -2.869720370
36 1.177688890 4.130279630
37 -2.972587445 1.177688890
38 4.130279630 -2.972587445
39 0.130279630 4.130279630
40 2.743205798 0.130279630
41 -6.869720370 2.743205798
42 -0.834508344 -6.869720370
43 5.165491656 -0.834508344
44 -2.942784198 5.165491656
45 1.130279630 -2.942784198
46 4.067784449 1.130279630
47 4.067784449 4.067784449
48 1.474976466 4.067784449
49 3.964917374 1.474976466
50 -0.932215551 3.964917374
51 -0.932215551 -0.932215551
52 -0.932215551 -0.932215551
53 0.067784449 -0.932215551
54 -0.108146455 0.067784449
55 0.067784449 -0.108146455
56 2.067784449 0.067784449
57 -2.137949702 2.067784449
58 -2.691269375 -2.137949702
59 -0.125752468 -2.691269375
60 -1.932215551 -0.125752468
61 2.067784449 -1.932215551
62 -3.932215551 2.067784449
63 3.964917374 -3.932215551
64 1.964917374 3.964917374
65 2.067784449 1.964917374
66 0.067784449 2.067784449
67 1.964917374 0.067784449
68 -5.108146455 1.964917374
69 7.067784449 -5.108146455
70 3.067784449 7.067784449
71 -3.932215551 3.067784449
72 -1.932215551 -3.932215551
73 1.067784449 -1.932215551
74 1.130279630 1.067784449
75 -1.869720370 1.130279630
76 0.130279630 -1.869720370
77 3.057215802 0.130279630
78 -4.972587445 3.057215802
79 2.130279630 -4.972587445
80 -1.869720370 2.130279630
81 -5.508301106 -1.869720370
82 -1.075454520 -5.508301106
83 0.130279630 -1.075454520
84 1.743205798 0.130279630
85 0.130279630 1.743205798
86 2.130279630 0.130279630
87 -0.834508344 2.130279630
88 -2.166124361 -0.834508344
89 0.936742714 -2.166124361
90 -3.869720370 0.936742714
91 -2.869720370 -3.869720370
92 -5.932215551 -2.869720370
93 -0.422156459 -5.932215551
94 2.067784449 -0.422156459
95 -0.932215551 2.067784449
96 -4.228619543 -0.932215551
97 -4.932215551 -4.228619543
98 -2.884806292 -4.932215551
99 1.982523386 -2.884806292
100 1.067784449 1.982523386
101 3.067784449 1.067784449
102 -6.932215551 3.067784449
103 2.879656311 -6.932215551
104 1.188257537 2.879656311
105 0.067784449 1.188257537
106 -6.035082626 0.067784449
107 -4.932215551 -6.035082626
108 1.067784449 -4.932215551
109 4.085390462 1.067784449
110 -1.932215551 4.085390462
111 4.067784449 -1.932215551
112 3.067784449 4.067784449
113 -3.932215551 3.067784449
114 -0.932215551 -3.932215551
115 1.235666796 -0.932215551
116 3.964917374 1.235666796
117 -0.932215551 3.964917374
118 2.067784449 -0.932215551
119 -3.017476614 2.067784449
120 -3.932215551 -3.017476614
121 -4.228619543 -3.932215551
122 -1.137949702 -4.228619543
123 0.067784449 -1.137949702
124 -1.343683852 0.067784449
125 1.801183704 -1.343683852
126 -1.017476614 1.801183704
127 2.067784449 -1.017476614
128 0.595449554 2.067784449
129 0.067784449 0.595449554
130 -7.932215551 0.067784449
131 2.788986470 -7.932215551
132 -1.932215551 2.788986470
133 1.012326633 -1.932215551
134 2.698316629 1.012326633
135 0.012326633 2.698316629
136 1.067784449 0.012326633
137 -3.932215551 1.067784449
138 -2.137949702 -3.932215551
139 3.067784449 -2.137949702
140 4.067784449 3.067784449
141 4.067784449 4.067784449
142 2.067784449 4.067784449
143 1.891853545 2.067784449
144 -0.108146455 1.891853545
145 2.863678886 -0.108146455
146 0.954348727 2.863678886
147 4.067784449 0.954348727
148 2.067784449 4.067784449
149 -3.932215551 2.067784449
150 4.188257537 -3.932215551
151 3.891853545 4.188257537
152 0.067784449 3.891853545
153 -0.764333204 0.067784449
154 3.891853545 -0.764333204
155 -2.932215551 3.891853545
156 2.442284531 -2.932215551
157 -0.097577808 2.442284531
158 -3.678188557 -0.097577808
159 -1.736158544 -3.678188557
160 0.442284531 -1.736158544
161 4.022895280 0.442284531
162 2.143368368 4.022895280
163 3.606018201 2.143368368
164 -2.170641636 3.606018201
165 -3.994710733 -2.170641636
166 -0.079971795 -3.994710733
167 2.515348360 -0.079971795
168 1.902422192 2.515348360
169 1.902422192 1.902422192
170 -0.291114724 1.902422192
171 0.052698527 -0.291114724
172 -1.826828385 0.052698527
173 -7.753764557 -1.826828385
174 2.052698527 -7.753764557
175 3.005289267 2.052698527
176 3.902422192 3.005289267
177 -0.200444883 3.902422192
178 0.005289267 -0.200444883
179 0.125762355 0.005289267
180 0.246235443 0.125762355
181 -0.097577808 0.246235443
182 -0.977104720 -0.097577808
183 -1.303311958 -0.977104720
184 -1.454848394 -1.303311958
185 -1.261311477 -1.454848394
186 -1.097577808 -1.261311477
187 -1.393981799 -1.097577808
188 2.902422192 -1.393981799
189 2.708885276 2.902422192
190 -3.140838389 2.708885276
191 4.052698527 -3.140838389
192 0.811752351 4.052698527
193 -2.959498707 0.811752351
194 4.143368368 -2.959498707
195 -0.097577808 4.143368368
196 2.920028205 -0.097577808
197 -6.633291469 2.920028205
198 -0.856631632 -6.633291469
199 4.799555117 -0.856631632
200 -2.947301473 4.799555117
201 0.799555117 -2.947301473
202 3.932225439 0.799555117
203 4.125762355 3.932225439
204 2.022895280 4.125762355
205 3.932225439 2.022895280
206 -1.097577808 3.932225439
207 -1.261311477 -1.097577808
208 -1.050168548 -1.261311477
209 -0.097577808 -1.050168548
210 -0.170641636 -0.097577808
211 0.022895280 -0.170641636
212 1.829358364 0.022895280
213 -2.484651640 1.829358364
214 -2.874237645 -2.484651640
215 0.125762355 -2.874237645
216 -2.097577808 0.125762355
217 2.040501293 -2.097577808
218 -4.097577808 2.040501293
219 4.052698527 -4.097577808
220 1.545151606 4.052698527
221 1.811752351 1.545151606
222 0.125762355 1.811752351
223 2.125762355 0.125762355
224 -4.874237645 2.125762355
225 6.920028205 -4.874237645
226 2.618215435 6.920028205
227 -3.994710733 2.618215435
228 -2.097577808 -3.994710733
229 1.022895280 -2.097577808
230 0.321811443 1.022895280
231 -2.291114724 0.321811443
232 -0.097577808 -2.291114724
233 2.811752351 -0.097577808
234 -5.188247649 2.811752351
235 1.811752351 -5.188247649
236 -2.170641636 1.811752351
237 -6.200444883 -2.170641636
238 -0.977104720 -6.200444883
239 0.040501293 -0.977104720
240 1.817161130 0.040501293
241 0.143368368 1.817161130
242 1.738688523 0.143368368
243 -1.273508711 1.738688523
244 -2.097577808 -1.273508711
245 0.738688523 -2.097577808
246 -4.188247649 0.738688523
247 -3.974592548 -4.188247649
248 -5.977104720 -3.974592548
249 -0.097577808 -5.977104720
250 2.005289267 -0.097577808
251 -0.959498707 2.005289267
252 -4.188247649 -0.959498707
253 -5.097577808 -4.188247649
254 -2.753764557 -5.097577808
255 1.817161130 -2.753764557
256 1.005289267 1.817161130
257 2.738688523 1.005289267
258 -7.364178552 2.738688523
259 2.859161611 -7.364178552
260 1.125762355 2.859161611
261 -0.188247649 1.125762355
262 -6.097577808 -0.188247649
263 -5.097577808 -6.097577808
264 0.696688042 -5.097577808
265 3.799555117 0.696688042
266 -1.874237645 3.799555117
267 3.902422192 -1.874237645
268 2.902422192 3.902422192
269 -4.291114724 2.902422192
270 -1.097577808 -4.291114724
271 0.635821448 -1.097577808
272 4.052698527 0.635821448
273 -1.454848394 4.052698527
274 1.932225439 -1.454848394
275 -3.381784565 1.932225439
276 -3.936732826 -3.381784565
277 -4.262940065 -3.936732826
278 -0.798653726 -4.262940065
279 0.183740262 -0.798653726
280 -1.142466977 0.183740262
281 1.839927010 -1.142466977
282 -1.160072990 1.839927010
283 1.960400098 -1.160072990
284 0.749257169 1.960400098
285 -0.039599902 0.749257169
286 -8.160072990 -0.039599902
287 2.942794086 -8.160072990
288 -2.250742831 2.942794086
289 0.942794086 -2.250742831
290 2.555720253 0.942794086
291 -0.057205914 2.555720253
292 0.839927010 -0.057205914
293 -4.057205914 0.839927010
294 -2.250742831 -4.057205914
295 2.839927010 -2.250742831
296 4.063267174 2.839927010
297 3.839927010 4.063267174
298 1.942794086 3.839927010
299 1.839927010 1.942794086
300 -0.057205914 1.839927010
301 2.942794086 -0.057205914
302 0.749257169 2.942794086
303 3.839927010 0.749257169
304 1.749257169 3.839927010
305 -4.057205914 1.749257169
306 3.942794086 -4.057205914
307 3.646390094 3.942794086
308 0.063267174 3.646390094
309 -1.057205914 0.063267174
310 3.942794086 -1.057205914
311 -3.057205914 3.942794086
> 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/html/rcomp/tmp/7n2zd1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gtzy1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9gtzy1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rkg11292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11ulw71292680918.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/html/rcomp/tmp/12y3vu1292680918.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/html/rcomp/tmp/13uva31292680918.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/html/rcomp/tmp/14n4so1292680918.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/html/rcomp/tmp/158nqu1292680918.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/html/rcomp/tmp/16mx631292680918.tab")
+ }
>
> try(system("convert tmp/12jjp1292680918.ps tmp/12jjp1292680918.png",intern=TRUE))
character(0)
> try(system("convert tmp/22jjp1292680918.ps tmp/22jjp1292680918.png",intern=TRUE))
character(0)
> try(system("convert tmp/32jjp1292680918.ps tmp/32jjp1292680918.png",intern=TRUE))
character(0)
> try(system("convert tmp/4daia1292680918.ps tmp/4daia1292680918.png",intern=TRUE))
character(0)
> try(system("convert tmp/5daia1292680918.ps tmp/5daia1292680918.png",intern=TRUE))
character(0)
> try(system("convert tmp/6daia1292680918.ps tmp/6daia1292680918.png",intern=TRUE))
character(0)
> try(system("convert tmp/7n2zd1292680918.ps tmp/7n2zd1292680918.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gtzy1292680918.ps tmp/8gtzy1292680918.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gtzy1292680918.ps tmp/9gtzy1292680918.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rkg11292680918.ps tmp/10rkg11292680918.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.770 2.010 17.195