R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(13
+ ,38
+ ,14
+ ,16
+ ,32
+ ,18
+ ,19
+ ,35
+ ,11
+ ,15
+ ,33
+ ,12
+ ,14
+ ,37
+ ,16
+ ,13
+ ,29
+ ,18
+ ,19
+ ,31
+ ,14
+ ,15
+ ,36
+ ,14
+ ,14
+ ,35
+ ,15
+ ,15
+ ,38
+ ,15
+ ,16
+ ,31
+ ,17
+ ,16
+ ,34
+ ,19
+ ,16
+ ,35
+ ,10
+ ,16
+ ,38
+ ,16
+ ,17
+ ,37
+ ,18
+ ,15
+ ,33
+ ,14
+ ,15
+ ,32
+ ,14
+ ,20
+ ,38
+ ,17
+ ,18
+ ,38
+ ,14
+ ,16
+ ,32
+ ,16
+ ,16
+ ,33
+ ,18
+ ,16
+ ,31
+ ,11
+ ,19
+ ,38
+ ,14
+ ,16
+ ,39
+ ,12
+ ,17
+ ,32
+ ,17
+ ,17
+ ,32
+ ,9
+ ,16
+ ,35
+ ,16
+ ,15
+ ,37
+ ,14
+ ,16
+ ,33
+ ,15
+ ,14
+ ,33
+ ,11
+ ,15
+ ,31
+ ,16
+ ,12
+ ,32
+ ,13
+ ,14
+ ,31
+ ,17
+ ,16
+ ,37
+ ,15
+ ,14
+ ,30
+ ,14
+ ,10
+ ,33
+ ,16
+ ,10
+ ,31
+ ,9
+ ,14
+ ,33
+ ,15
+ ,16
+ ,31
+ ,17
+ ,16
+ ,33
+ ,13
+ ,16
+ ,32
+ ,15
+ ,14
+ ,33
+ ,16
+ ,20
+ ,32
+ ,16
+ ,14
+ ,33
+ ,12
+ ,14
+ ,28
+ ,15
+ ,11
+ ,35
+ ,11
+ ,14
+ ,39
+ ,15
+ ,15
+ ,34
+ ,15
+ ,16
+ ,38
+ ,17
+ ,14
+ ,32
+ ,13
+ ,16
+ ,38
+ ,16
+ ,14
+ ,30
+ ,14
+ ,12
+ ,33
+ ,11
+ ,16
+ ,38
+ ,12
+ ,9
+ ,32
+ ,12
+ ,14
+ ,35
+ ,15
+ ,16
+ ,34
+ ,16
+ ,16
+ ,34
+ ,15
+ ,15
+ ,36
+ ,12
+ ,16
+ ,34
+ ,12
+ ,12
+ ,28
+ ,8
+ ,16
+ ,34
+ ,13
+ ,16
+ ,35
+ ,11
+ ,14
+ ,35
+ ,14
+ ,16
+ ,31
+ ,15
+ ,17
+ ,34
+ ,9
+ ,18
+ ,37
+ ,10
+ ,18
+ ,35
+ ,11
+ ,12
+ ,27
+ ,12
+ ,16
+ ,40
+ ,15
+ ,10
+ ,37
+ ,15
+ ,14
+ ,36
+ ,14
+ ,18
+ ,38
+ ,16
+ ,18
+ ,39
+ ,15
+ ,16
+ ,41
+ ,15
+ ,17
+ ,27
+ ,13
+ ,16
+ ,30
+ ,12
+ ,16
+ ,37
+ ,17
+ ,13
+ ,31
+ ,13
+ ,16
+ ,31
+ ,15
+ ,16
+ ,27
+ ,13
+ ,16
+ ,36
+ ,15
+ ,15
+ ,37
+ ,15
+ ,15
+ ,33
+ ,16
+ ,16
+ ,34
+ ,15
+ ,14
+ ,31
+ ,14
+ ,16
+ ,39
+ ,15
+ ,16
+ ,34
+ ,14
+ ,15
+ ,32
+ ,13
+ ,12
+ ,33
+ ,7
+ ,17
+ ,36
+ ,17
+ ,16
+ ,32
+ ,13
+ ,15
+ ,41
+ ,15
+ ,13
+ ,28
+ ,14
+ ,16
+ ,30
+ ,13
+ ,16
+ ,36
+ ,16
+ ,16
+ ,35
+ ,12
+ ,16
+ ,31
+ ,14
+ ,14
+ ,34
+ ,17
+ ,16
+ ,36
+ ,15
+ ,16
+ ,36
+ ,17
+ ,20
+ ,35
+ ,12
+ ,15
+ ,37
+ ,16
+ ,16
+ ,28
+ ,11
+ ,13
+ ,39
+ ,15
+ ,17
+ ,32
+ ,9
+ ,16
+ ,35
+ ,16
+ ,16
+ ,39
+ ,15
+ ,12
+ ,35
+ ,10
+ ,16
+ ,42
+ ,10
+ ,16
+ ,34
+ ,15
+ ,17
+ ,33
+ ,11
+ ,13
+ ,41
+ ,13
+ ,12
+ ,33
+ ,14
+ ,18
+ ,34
+ ,18
+ ,14
+ ,32
+ ,16
+ ,14
+ ,40
+ ,14
+ ,13
+ ,40
+ ,14
+ ,16
+ ,35
+ ,14
+ ,13
+ ,36
+ ,14
+ ,16
+ ,37
+ ,12
+ ,13
+ ,27
+ ,14
+ ,16
+ ,39
+ ,15
+ ,15
+ ,38
+ ,15
+ ,16
+ ,31
+ ,15
+ ,15
+ ,33
+ ,13
+ ,17
+ ,32
+ ,17
+ ,15
+ ,39
+ ,17
+ ,12
+ ,36
+ ,19
+ ,16
+ ,33
+ ,15
+ ,10
+ ,33
+ ,13
+ ,16
+ ,32
+ ,9
+ ,12
+ ,37
+ ,15
+ ,14
+ ,30
+ ,15
+ ,15
+ ,38
+ ,15
+ ,13
+ ,29
+ ,16
+ ,15
+ ,22
+ ,11
+ ,11
+ ,35
+ ,14
+ ,12
+ ,35
+ ,11
+ ,11
+ ,34
+ ,15
+ ,16
+ ,35
+ ,13
+ ,15
+ ,34
+ ,15
+ ,17
+ ,37
+ ,16
+ ,16
+ ,35
+ ,14
+ ,10
+ ,23
+ ,15
+ ,18
+ ,31
+ ,16
+ ,13
+ ,27
+ ,16
+ ,16
+ ,36
+ ,11
+ ,13
+ ,31
+ ,12
+ ,10
+ ,32
+ ,9
+ ,15
+ ,39
+ ,16
+ ,16
+ ,37
+ ,13
+ ,16
+ ,38
+ ,16
+ ,14
+ ,39
+ ,12
+ ,10
+ ,31
+ ,13
+ ,17
+ ,32
+ ,13
+ ,13
+ ,37
+ ,14
+ ,15
+ ,36
+ ,19
+ ,16
+ ,32
+ ,13
+ ,12
+ ,38
+ ,12
+ ,13
+ ,36
+ ,13
+ ,13
+ ,26
+ ,10
+ ,12
+ ,26
+ ,14
+ ,17
+ ,33
+ ,16
+ ,15
+ ,39
+ ,10
+ ,10
+ ,30
+ ,11
+ ,14
+ ,33
+ ,14
+ ,11
+ ,25
+ ,12
+ ,13
+ ,38
+ ,9
+ ,16
+ ,37
+ ,9
+ ,12
+ ,31
+ ,11
+ ,16
+ ,37
+ ,16
+ ,12
+ ,35
+ ,9
+ ,9
+ ,25
+ ,13
+ ,12
+ ,28
+ ,16
+ ,15
+ ,35
+ ,13
+ ,12
+ ,33
+ ,9
+ ,12
+ ,30
+ ,12
+ ,14
+ ,31
+ ,16
+ ,12
+ ,37
+ ,11
+ ,16
+ ,36
+ ,14
+ ,11
+ ,30
+ ,13
+ ,19
+ ,36
+ ,15
+ ,15
+ ,32
+ ,14
+ ,8
+ ,28
+ ,16
+ ,16
+ ,36
+ ,13
+ ,17
+ ,34
+ ,14
+ ,12
+ ,31
+ ,15
+ ,11
+ ,28
+ ,13
+ ,11
+ ,36
+ ,11
+ ,14
+ ,36
+ ,11
+ ,16
+ ,40
+ ,14
+ ,12
+ ,33
+ ,15
+ ,16
+ ,37
+ ,11
+ ,13
+ ,32
+ ,15
+ ,15
+ ,38
+ ,12
+ ,16
+ ,31
+ ,14
+ ,16
+ ,37
+ ,14
+ ,14
+ ,33
+ ,8
+ ,16
+ ,32
+ ,13
+ ,16
+ ,30
+ ,9
+ ,14
+ ,30
+ ,15
+ ,11
+ ,31
+ ,17
+ ,12
+ ,32
+ ,13
+ ,15
+ ,34
+ ,15
+ ,15
+ ,36
+ ,15
+ ,16
+ ,37
+ ,14
+ ,16
+ ,36
+ ,16
+ ,11
+ ,33
+ ,13
+ ,15
+ ,33
+ ,16
+ ,12
+ ,33
+ ,9
+ ,12
+ ,44
+ ,16
+ ,15
+ ,39
+ ,11
+ ,15
+ ,32
+ ,10
+ ,16
+ ,35
+ ,11
+ ,14
+ ,25
+ ,15
+ ,17
+ ,35
+ ,17
+ ,14
+ ,34
+ ,14
+ ,13
+ ,35
+ ,8
+ ,15
+ ,39
+ ,15
+ ,13
+ ,33
+ ,11
+ ,14
+ ,36
+ ,16
+ ,15
+ ,32
+ ,10
+ ,12
+ ,32
+ ,15
+ ,13
+ ,36
+ ,9
+ ,8
+ ,36
+ ,16
+ ,14
+ ,32
+ ,19
+ ,14
+ ,34
+ ,12
+ ,11
+ ,33
+ ,8
+ ,12
+ ,35
+ ,11
+ ,13
+ ,30
+ ,14
+ ,10
+ ,38
+ ,9
+ ,16
+ ,34
+ ,15
+ ,18
+ ,33
+ ,13
+ ,13
+ ,32
+ ,16
+ ,11
+ ,31
+ ,11
+ ,4
+ ,30
+ ,12
+ ,13
+ ,27
+ ,13
+ ,16
+ ,31
+ ,10
+ ,10
+ ,30
+ ,11
+ ,12
+ ,32
+ ,12
+ ,12
+ ,35
+ ,8
+ ,10
+ ,28
+ ,12
+ ,13
+ ,33
+ ,12
+ ,15
+ ,31
+ ,15
+ ,12
+ ,35
+ ,11
+ ,14
+ ,35
+ ,13
+ ,10
+ ,32
+ ,14
+ ,12
+ ,21
+ ,10
+ ,12
+ ,20
+ ,12
+ ,11
+ ,34
+ ,15
+ ,10
+ ,32
+ ,13
+ ,12
+ ,34
+ ,13
+ ,16
+ ,32
+ ,13
+ ,12
+ ,33
+ ,12
+ ,14
+ ,33
+ ,12
+ ,16
+ ,37
+ ,9
+ ,14
+ ,32
+ ,9
+ ,13
+ ,34
+ ,15
+ ,4
+ ,30
+ ,10
+ ,15
+ ,30
+ ,14
+ ,11
+ ,38
+ ,15
+ ,11
+ ,36
+ ,7
+ ,14
+ ,32
+ ,14)
+ ,dim=c(3
+ ,264)
+ ,dimnames=list(c('Doorzettingsvermogen'
+ ,'Zelfstandig'
+ ,'Stressbestendig')
+ ,1:264))
> y <- array(NA,dim=c(3,264),dimnames=list(c('Doorzettingsvermogen','Zelfstandig','Stressbestendig'),1:264))
> 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'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Doorzettingsvermogen Zelfstandig Stressbestendig
1 13 38 14
2 16 32 18
3 19 35 11
4 15 33 12
5 14 37 16
6 13 29 18
7 19 31 14
8 15 36 14
9 14 35 15
10 15 38 15
11 16 31 17
12 16 34 19
13 16 35 10
14 16 38 16
15 17 37 18
16 15 33 14
17 15 32 14
18 20 38 17
19 18 38 14
20 16 32 16
21 16 33 18
22 16 31 11
23 19 38 14
24 16 39 12
25 17 32 17
26 17 32 9
27 16 35 16
28 15 37 14
29 16 33 15
30 14 33 11
31 15 31 16
32 12 32 13
33 14 31 17
34 16 37 15
35 14 30 14
36 10 33 16
37 10 31 9
38 14 33 15
39 16 31 17
40 16 33 13
41 16 32 15
42 14 33 16
43 20 32 16
44 14 33 12
45 14 28 15
46 11 35 11
47 14 39 15
48 15 34 15
49 16 38 17
50 14 32 13
51 16 38 16
52 14 30 14
53 12 33 11
54 16 38 12
55 9 32 12
56 14 35 15
57 16 34 16
58 16 34 15
59 15 36 12
60 16 34 12
61 12 28 8
62 16 34 13
63 16 35 11
64 14 35 14
65 16 31 15
66 17 34 9
67 18 37 10
68 18 35 11
69 12 27 12
70 16 40 15
71 10 37 15
72 14 36 14
73 18 38 16
74 18 39 15
75 16 41 15
76 17 27 13
77 16 30 12
78 16 37 17
79 13 31 13
80 16 31 15
81 16 27 13
82 16 36 15
83 15 37 15
84 15 33 16
85 16 34 15
86 14 31 14
87 16 39 15
88 16 34 14
89 15 32 13
90 12 33 7
91 17 36 17
92 16 32 13
93 15 41 15
94 13 28 14
95 16 30 13
96 16 36 16
97 16 35 12
98 16 31 14
99 14 34 17
100 16 36 15
101 16 36 17
102 20 35 12
103 15 37 16
104 16 28 11
105 13 39 15
106 17 32 9
107 16 35 16
108 16 39 15
109 12 35 10
110 16 42 10
111 16 34 15
112 17 33 11
113 13 41 13
114 12 33 14
115 18 34 18
116 14 32 16
117 14 40 14
118 13 40 14
119 16 35 14
120 13 36 14
121 16 37 12
122 13 27 14
123 16 39 15
124 15 38 15
125 16 31 15
126 15 33 13
127 17 32 17
128 15 39 17
129 12 36 19
130 16 33 15
131 10 33 13
132 16 32 9
133 12 37 15
134 14 30 15
135 15 38 15
136 13 29 16
137 15 22 11
138 11 35 14
139 12 35 11
140 11 34 15
141 16 35 13
142 15 34 15
143 17 37 16
144 16 35 14
145 10 23 15
146 18 31 16
147 13 27 16
148 16 36 11
149 13 31 12
150 10 32 9
151 15 39 16
152 16 37 13
153 16 38 16
154 14 39 12
155 10 31 13
156 17 32 13
157 13 37 14
158 15 36 19
159 16 32 13
160 12 38 12
161 13 36 13
162 13 26 10
163 12 26 14
164 17 33 16
165 15 39 10
166 10 30 11
167 14 33 14
168 11 25 12
169 13 38 9
170 16 37 9
171 12 31 11
172 16 37 16
173 12 35 9
174 9 25 13
175 12 28 16
176 15 35 13
177 12 33 9
178 12 30 12
179 14 31 16
180 12 37 11
181 16 36 14
182 11 30 13
183 19 36 15
184 15 32 14
185 8 28 16
186 16 36 13
187 17 34 14
188 12 31 15
189 11 28 13
190 11 36 11
191 14 36 11
192 16 40 14
193 12 33 15
194 16 37 11
195 13 32 15
196 15 38 12
197 16 31 14
198 16 37 14
199 14 33 8
200 16 32 13
201 16 30 9
202 14 30 15
203 11 31 17
204 12 32 13
205 15 34 15
206 15 36 15
207 16 37 14
208 16 36 16
209 11 33 13
210 15 33 16
211 12 33 9
212 12 44 16
213 15 39 11
214 15 32 10
215 16 35 11
216 14 25 15
217 17 35 17
218 14 34 14
219 13 35 8
220 15 39 15
221 13 33 11
222 14 36 16
223 15 32 10
224 12 32 15
225 13 36 9
226 8 36 16
227 14 32 19
228 14 34 12
229 11 33 8
230 12 35 11
231 13 30 14
232 10 38 9
233 16 34 15
234 18 33 13
235 13 32 16
236 11 31 11
237 4 30 12
238 13 27 13
239 16 31 10
240 10 30 11
241 12 32 12
242 12 35 8
243 10 28 12
244 13 33 12
245 15 31 15
246 12 35 11
247 14 35 13
248 10 32 14
249 12 21 10
250 12 20 12
251 11 34 15
252 10 32 13
253 12 34 13
254 16 32 13
255 12 33 12
256 14 33 12
257 16 37 9
258 14 32 9
259 13 34 15
260 4 30 10
261 15 30 14
262 11 38 15
263 11 36 7
264 14 32 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Zelfstandig Stressbestendig
5.7149 0.1728 0.2028
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.3320 -1.3782 0.2787 1.4813 5.8040
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.71492 1.44754 3.948 0.000101 ***
Zelfstandig 0.17279 0.03868 4.467 1.18e-05 ***
Stressbestendig 0.20278 0.05735 3.536 0.000480 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.31 on 261 degrees of freedom
Multiple R-squared: 0.1223, Adjusted R-squared: 0.1156
F-statistic: 18.18 on 2 and 261 DF, p-value: 4.049e-08
> 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,] 0.642086189 0.715827621 0.3579138
[2,] 0.654869484 0.690261032 0.3451305
[3,] 0.516620249 0.966759503 0.4833798
[4,] 0.402618659 0.805237318 0.5973813
[5,] 0.319033124 0.638066249 0.6809669
[6,] 0.240528511 0.481057021 0.7594715
[7,] 0.264896037 0.529792074 0.7351040
[8,] 0.203557610 0.407115219 0.7964424
[9,] 0.179197387 0.358394775 0.8208026
[10,] 0.202823988 0.405647976 0.7971760
[11,] 0.155773138 0.311546276 0.8442269
[12,] 0.117800899 0.235601798 0.8821991
[13,] 0.325632182 0.651264363 0.6743678
[14,] 0.308350983 0.616701966 0.6916490
[15,] 0.248576008 0.497152015 0.7514240
[16,] 0.195689119 0.391378238 0.8043109
[17,] 0.153203875 0.306407750 0.8467961
[18,] 0.179052331 0.358104661 0.8209477
[19,] 0.143116384 0.286232768 0.8568836
[20,] 0.123401695 0.246803391 0.8765983
[21,] 0.103137844 0.206275689 0.8968622
[22,] 0.077012287 0.154024573 0.9229877
[23,] 0.064145915 0.128291829 0.9358541
[24,] 0.047083577 0.094167153 0.9529164
[25,] 0.047060387 0.094120773 0.9529396
[26,] 0.034857047 0.069714094 0.9651430
[27,] 0.066188994 0.132377988 0.9338110
[28,] 0.055233561 0.110467123 0.9447664
[29,] 0.041057587 0.082115174 0.9589424
[30,] 0.032536800 0.065073599 0.9674632
[31,] 0.143500665 0.287001330 0.8564993
[32,] 0.268741782 0.537483565 0.7312582
[33,] 0.237057826 0.474115652 0.7629422
[34,] 0.209121880 0.418243760 0.7908781
[35,] 0.182587808 0.365175615 0.8174122
[36,] 0.158319662 0.316639324 0.8416803
[37,] 0.138553385 0.277106769 0.8614466
[38,] 0.282649986 0.565299972 0.7173500
[39,] 0.249803690 0.499607379 0.7501963
[40,] 0.213263939 0.426527879 0.7867361
[41,] 0.300897170 0.601794341 0.6991028
[42,] 0.300167501 0.600335003 0.6998325
[43,] 0.261617830 0.523235661 0.7383822
[44,] 0.225738256 0.451476512 0.7742617
[45,] 0.195210833 0.390421665 0.8047892
[46,] 0.165246936 0.330493873 0.8347531
[47,] 0.139588960 0.279177919 0.8604110
[48,] 0.143445868 0.286891735 0.8565541
[49,] 0.121715550 0.243431101 0.8782844
[50,] 0.254006952 0.508013904 0.7459930
[51,] 0.231562342 0.463124684 0.7684377
[52,] 0.202743717 0.405487434 0.7972563
[53,] 0.178177108 0.356354216 0.8218229
[54,] 0.151496195 0.302992389 0.8485038
[55,] 0.138678101 0.277356201 0.8613219
[56,] 0.117039429 0.234078858 0.8829606
[57,] 0.104131125 0.208262249 0.8958689
[58,] 0.094432101 0.188864202 0.9055679
[59,] 0.082242198 0.164484395 0.9177578
[60,] 0.073832934 0.147665868 0.9261671
[61,] 0.088361442 0.176722885 0.9116386
[62,] 0.107094043 0.214188086 0.8929060
[63,] 0.132519971 0.265039942 0.8674800
[64,] 0.117807914 0.235615829 0.8821921
[65,] 0.100876848 0.201753697 0.8991232
[66,] 0.242571845 0.485143690 0.7574282
[67,] 0.222864826 0.445729652 0.7771352
[68,] 0.218894400 0.437788801 0.7811056
[69,] 0.213337621 0.426675242 0.7866624
[70,] 0.188576133 0.377152266 0.8114239
[71,] 0.232971485 0.465942970 0.7670285
[72,] 0.229875056 0.459750111 0.7701249
[73,] 0.202063232 0.404126463 0.7979368
[74,] 0.185180727 0.370361454 0.8148193
[75,] 0.171690066 0.343380131 0.8283099
[76,] 0.176998448 0.353996897 0.8230016
[77,] 0.155712134 0.311424268 0.8442879
[78,] 0.135883049 0.271766098 0.8641170
[79,] 0.116762646 0.233525291 0.8832374
[80,] 0.102921740 0.205843479 0.8970783
[81,] 0.088256121 0.176512242 0.9117439
[82,] 0.074546343 0.149092687 0.9254537
[83,] 0.065668481 0.131336962 0.9343315
[84,] 0.055743315 0.111486630 0.9442567
[85,] 0.052565470 0.105130939 0.9474345
[86,] 0.046890650 0.093781300 0.9531093
[87,] 0.043332205 0.086664409 0.9566678
[88,] 0.037700158 0.075400316 0.9622998
[89,] 0.032386872 0.064773744 0.9676131
[90,] 0.031410267 0.062820534 0.9685897
[91,] 0.025975788 0.051951576 0.9740242
[92,] 0.022905976 0.045811952 0.9770940
[93,] 0.021087973 0.042175945 0.9789120
[94,] 0.018582876 0.037165752 0.9814171
[95,] 0.015334269 0.030668538 0.9846657
[96,] 0.012379524 0.024759048 0.9876205
[97,] 0.037943509 0.075887019 0.9620565
[98,] 0.031888072 0.063776144 0.9681119
[99,] 0.035085434 0.070170867 0.9649146
[100,] 0.039706800 0.079413600 0.9602932
[101,] 0.050555981 0.101111963 0.9494440
[102,] 0.043415823 0.086831646 0.9565842
[103,] 0.036246282 0.072492563 0.9637537
[104,] 0.039246093 0.078492187 0.9607539
[105,] 0.033254901 0.066509803 0.9667451
[106,] 0.029076025 0.058152049 0.9709240
[107,] 0.034287431 0.068574862 0.9657126
[108,] 0.037738650 0.075477300 0.9622614
[109,] 0.042204457 0.084408913 0.9577955
[110,] 0.047526007 0.095052013 0.9524740
[111,] 0.041055655 0.082111309 0.9589443
[112,] 0.037435964 0.074871929 0.9625640
[113,] 0.039808747 0.079617494 0.9601913
[114,] 0.035304591 0.070609181 0.9646954
[115,] 0.034338075 0.068676151 0.9656619
[116,] 0.030484125 0.060968250 0.9695159
[117,] 0.026708103 0.053416205 0.9732919
[118,] 0.022152892 0.044305784 0.9778471
[119,] 0.018122899 0.036245799 0.9818771
[120,] 0.016879864 0.033759729 0.9831201
[121,] 0.014157067 0.028314134 0.9858429
[122,] 0.014675631 0.029351262 0.9853244
[123,] 0.012046448 0.024092896 0.9879536
[124,] 0.017704635 0.035409270 0.9822954
[125,] 0.015937061 0.031874122 0.9840629
[126,] 0.030523770 0.061047541 0.9694762
[127,] 0.032987980 0.065975960 0.9670120
[128,] 0.040306961 0.080613923 0.9596930
[129,] 0.034271861 0.068543721 0.9657281
[130,] 0.028293764 0.056587528 0.9717062
[131,] 0.025145200 0.050290400 0.9748548
[132,] 0.030106220 0.060212440 0.9698938
[133,] 0.043306064 0.086612128 0.9566939
[134,] 0.044378465 0.088756930 0.9556215
[135,] 0.061265996 0.122531993 0.9387340
[136,] 0.056604401 0.113208802 0.9433956
[137,] 0.048043288 0.096086576 0.9519567
[138,] 0.045425308 0.090850617 0.9545747
[139,] 0.041173909 0.082347817 0.9588261
[140,] 0.050374244 0.100748487 0.9496258
[141,] 0.071976201 0.143952401 0.9280238
[142,] 0.063384073 0.126768147 0.9366159
[143,] 0.060152984 0.120305968 0.9398470
[144,] 0.052732183 0.105464367 0.9472678
[145,] 0.068075058 0.136150116 0.9319249
[146,] 0.057758398 0.115516797 0.9422416
[147,] 0.052003366 0.104006731 0.9479966
[148,] 0.044441793 0.088883587 0.9555582
[149,] 0.038041086 0.076082172 0.9619589
[150,] 0.053886697 0.107773393 0.9461133
[151,] 0.066049641 0.132099282 0.9339504
[152,] 0.062349302 0.124698603 0.9376507
[153,] 0.052595780 0.105191560 0.9474042
[154,] 0.053569507 0.107139013 0.9464305
[155,] 0.057727102 0.115454204 0.9422729
[156,] 0.052383216 0.104766432 0.9476168
[157,] 0.046875624 0.093751249 0.9531244
[158,] 0.041843947 0.083687894 0.9581561
[159,] 0.045911940 0.091823879 0.9540881
[160,] 0.038529862 0.077059725 0.9614701
[161,] 0.046825793 0.093651585 0.9531742
[162,] 0.039340476 0.078680952 0.9606595
[163,] 0.036147478 0.072294956 0.9638525
[164,] 0.031278840 0.062557680 0.9687212
[165,] 0.030642466 0.061284931 0.9693575
[166,] 0.026953681 0.053907361 0.9730463
[167,] 0.022971589 0.045943178 0.9770284
[168,] 0.020680095 0.041360189 0.9793199
[169,] 0.027997642 0.055995284 0.9720024
[170,] 0.025054604 0.050109207 0.9749454
[171,] 0.021103718 0.042207435 0.9788963
[172,] 0.018116612 0.036233225 0.9818834
[173,] 0.015456955 0.030913911 0.9845430
[174,] 0.012454927 0.024909853 0.9875451
[175,] 0.012144208 0.024288416 0.9878558
[176,] 0.010817204 0.021634408 0.9891828
[177,] 0.010807332 0.021614664 0.9891927
[178,] 0.021087348 0.042174696 0.9789127
[179,] 0.018490327 0.036980655 0.9815097
[180,] 0.046591981 0.093183962 0.9534080
[181,] 0.044108731 0.088217462 0.9558913
[182,] 0.052031954 0.104063907 0.9479680
[183,] 0.047580904 0.095161808 0.9524191
[184,] 0.044782322 0.089564643 0.9552177
[185,] 0.049382782 0.098765564 0.9506172
[186,] 0.040848514 0.081697028 0.9591515
[187,] 0.035832202 0.071664403 0.9641678
[188,] 0.033733731 0.067467462 0.9662663
[189,] 0.033222353 0.066444705 0.9667776
[190,] 0.027546663 0.055093327 0.9724533
[191,] 0.023202216 0.046404433 0.9767978
[192,] 0.024473894 0.048947789 0.9755261
[193,] 0.022838629 0.045677258 0.9771614
[194,] 0.019539445 0.039078891 0.9804606
[195,] 0.021216477 0.042432955 0.9787835
[196,] 0.029789885 0.059579770 0.9702101
[197,] 0.024321371 0.048642742 0.9756786
[198,] 0.027612049 0.055224098 0.9723880
[199,] 0.023750597 0.047501194 0.9762494
[200,] 0.019904453 0.039808906 0.9800955
[201,] 0.016345956 0.032691913 0.9836540
[202,] 0.015592314 0.031184628 0.9844077
[203,] 0.014480452 0.028960904 0.9855195
[204,] 0.014623183 0.029246366 0.9853768
[205,] 0.012264452 0.024528905 0.9877355
[206,] 0.009712917 0.019425834 0.9902871
[207,] 0.012333783 0.024667566 0.9876662
[208,] 0.010404642 0.020809284 0.9895954
[209,] 0.010595208 0.021190415 0.9894048
[210,] 0.012571342 0.025142685 0.9874287
[211,] 0.010473111 0.020946223 0.9895269
[212,] 0.012606304 0.025212608 0.9873937
[213,] 0.009930993 0.019861986 0.9900690
[214,] 0.007687834 0.015375668 0.9923122
[215,] 0.006287374 0.012574749 0.9937126
[216,] 0.004719642 0.009439284 0.9952804
[217,] 0.003555805 0.007111610 0.9964442
[218,] 0.003878599 0.007757198 0.9961214
[219,] 0.003047217 0.006094433 0.9969528
[220,] 0.002281508 0.004563015 0.9977185
[221,] 0.011993964 0.023987929 0.9880060
[222,] 0.008784024 0.017568048 0.9912160
[223,] 0.006930950 0.013861900 0.9930690
[224,] 0.005322490 0.010644980 0.9946775
[225,] 0.003964675 0.007929349 0.9960353
[226,] 0.002758690 0.005517379 0.9972413
[227,] 0.003084578 0.006169156 0.9969154
[228,] 0.003160969 0.006321939 0.9968390
[229,] 0.011162472 0.022324945 0.9888375
[230,] 0.008046603 0.016093207 0.9919534
[231,] 0.006142758 0.012285516 0.9938572
[232,] 0.145308833 0.290617667 0.8546912
[233,] 0.117607366 0.235214733 0.8823926
[234,] 0.173899240 0.347798481 0.8261008
[235,] 0.164738355 0.329476711 0.8352616
[236,] 0.130995824 0.261991648 0.8690042
[237,] 0.101208686 0.202417372 0.8987913
[238,] 0.094797266 0.189594531 0.9052027
[239,] 0.070994357 0.141988714 0.9290056
[240,] 0.066962786 0.133925573 0.9330372
[241,] 0.048622204 0.097244409 0.9513778
[242,] 0.037403783 0.074807566 0.9625962
[243,] 0.037665203 0.075330406 0.9623348
[244,] 0.025304134 0.050608267 0.9746959
[245,] 0.017807007 0.035614014 0.9821930
[246,] 0.015276312 0.030552624 0.9847237
[247,] 0.014553286 0.029106572 0.9854467
[248,] 0.009171576 0.018343152 0.9908284
[249,] 0.011676749 0.023353497 0.9883233
[250,] 0.006160440 0.012320881 0.9938396
[251,] 0.003641297 0.007282593 0.9963587
[252,] 0.006612089 0.013224178 0.9933879
[253,] 0.012189266 0.024378532 0.9878107
> postscript(file="/var/fisher/rcomp/tmp/1qurw1352133134.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/fisher/rcomp/tmp/2kbou1352133134.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3oupi1352133134.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4ugr61352133134.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5min31352133134.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 264
Frequency = 1
1 2 3 4 5 6
-2.11991877 1.10569357 5.00680100 1.14959936 -1.35269367 -1.37593481
7 8 9 10 11 12
5.08961499 0.22566231 -0.80432988 -0.32270149 1.48126683 0.55732978
13 14 15 16 17 18
2.20958372 0.47451579 1.24174089 0.74403392 0.91682445 4.27173307
19 20 21 22 23 24
2.88008123 1.51125901 0.93290304 2.69796315 3.88008123 1.11285614
25 26 27 28 29 30
2.30847629 3.93073805 0.99288740 0.05287177 1.54125120 0.35238208
31 32 33 34 35 36
0.68404955 -1.88039283 -0.51873317 0.85008905 0.26240553 -4.66153152
37 38 39 40 41 42
-2.89647141 -0.45874880 1.48126683 1.94681664 1.71404173 -0.66153152
43 44 45 46 47 48
5.51125901 0.14959936 0.40520388 -2.99319900 -1.49549202 0.36846066
49 50 51 52 53 54
0.27173307 0.11960717 0.47451579 0.26240553 -1.64761792 1.28564667
55 56 57 58 59 60
-4.67761011 -0.80432988 1.16567794 1.36846066 0.63122775 1.97680882
61 62 63 64 65 66
-0.17531708 1.77402610 2.00680100 -0.60154716 1.88683227 3.58515698
67 68 69 70 71 72
3.86400265 4.00680100 -0.81365742 0.33171744 -5.14991095 -0.77433769
73 74 75 76 77 78
2.47451579 2.50450798 0.15892690 3.98355986 2.66797097 0.44452361
79 80 81 82 83 84
-0.70760229 1.88683227 2.98355986 1.02287959 -0.14991095 0.33846848
85 86 87 88 89 90
1.36846066 0.08961499 0.50450798 1.57124338 1.11960717 -0.83648704
91 92 93 94 95 96
1.61731415 2.11960717 -0.84107310 -0.39201340 2.46518825 0.82009687
97 98 99 100 101 102
1.80401828 2.08961499 -1.03710478 1.02287959 0.61731415 5.80401828
103 104 105 106 107 108
-0.35269367 3.21633476 -2.49549202 3.93073805 0.99288740 0.50450798
109 110 111 112 113 114
-1.79041628 1.00004996 1.36846066 3.35238208 -2.43550766 -2.25596608
115 116 117 118 119 120
2.76011250 -0.48874099 -1.46549984 -2.46549984 1.39845284 -1.77433769
121 122 123 124 125 126
1.45843721 -0.21922286 0.50450798 -0.32270149 1.88683227 0.94681664
127 128 129 130 131 132
2.30847629 -0.90105746 -3.78825129 1.54125120 -4.05318336 2.93073805
133 134 135 136 137 138
-3.14991095 0.05962281 -0.32270149 -0.97036937 3.25307798 -3.60154716
139 140 141 142 143 144
-1.99319900 -3.63153934 1.60123556 0.36846066 1.64730633 1.39845284
145 146 147 148 149 150
-2.73084343 3.68404955 -0.62478830 1.83401047 -0.50481957 -3.06926195
151 152 153 154 155 156
-0.69827474 1.25565449 0.47451579 -0.88714386 -3.70760229 3.11960717
157 158 159 160 161 162
-1.94712823 -0.78825129 2.11960717 -2.71435333 -1.57155497 0.76469856
163 164 165 166 167 168
-1.04643232 2.33846848 0.51842158 -3.12924631 -0.25596608 -1.46807635
169 170 171 172 173 174
-1.10600517 2.06678537 -1.30203685 0.64730633 -1.58763356 -3.67085907
175 176 177 178 179 180
-1.79757884 0.60123556 -1.24205248 -1.33202903 -0.31595045 -2.33878007
181 182 183 184 185 186
1.22566231 -2.53481175 4.02287959 0.91682445 -5.79757884 1.42844503
187 188 189 190 191 192
2.57124338 -2.11316773 -2.18923068 -3.16598953 -0.16598953 0.53450016
193 194 195 196 197 198
-2.45874880 1.66121993 -1.28595827 0.28564667 2.08961499 1.05287177
199 200 201 202 203 204
0.96073024 2.11960717 3.27631913 0.05962281 -3.51873317 -1.88039283
205 206 207 208 209 210
0.36846066 0.02287959 1.05287177 0.82009687 -3.05318336 0.33846848
211 212 213 214 215 216
-1.24205248 -4.56222743 0.31563886 1.72795533 2.00680100 0.92357549
217 218 219 220 221 222
1.79010468 -0.42875662 -0.38485084 -0.49549202 -0.64761792 -1.17990313
223 224 225 226 227 228
1.72795533 -2.28595827 -0.76042409 -7.17990313 -1.09708915 -0.02319118
229 230 231 232 233 234
-2.03926976 -1.99319900 -0.73759447 -4.10600517 1.36846066 3.94681664
235 236 237 238 239 240
-1.48874099 -2.30203685 -9.33202903 -0.01644014 2.90074587 -3.12924631
241 242 243 244 245 246
-1.67761011 -1.38485084 -2.98644796 -0.85040064 0.88683227 -1.99319900
247 248 249 250 251 252
-0.39876444 -4.08317555 0.62865124 0.39587634 -3.63153934 -3.88039283
253 254 255 256 257 258
-2.22597390 2.11960717 -1.85040064 0.14959936 2.06678537 0.93073805
259 260 261 262 263 264
-1.63153934 -8.92646359 1.26240553 -4.32270149 -2.35485865 -0.08317555
> postscript(file="/var/fisher/rcomp/tmp/6wrgp1352133134.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 = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.11991877 NA
1 1.10569357 -2.11991877
2 5.00680100 1.10569357
3 1.14959936 5.00680100
4 -1.35269367 1.14959936
5 -1.37593481 -1.35269367
6 5.08961499 -1.37593481
7 0.22566231 5.08961499
8 -0.80432988 0.22566231
9 -0.32270149 -0.80432988
10 1.48126683 -0.32270149
11 0.55732978 1.48126683
12 2.20958372 0.55732978
13 0.47451579 2.20958372
14 1.24174089 0.47451579
15 0.74403392 1.24174089
16 0.91682445 0.74403392
17 4.27173307 0.91682445
18 2.88008123 4.27173307
19 1.51125901 2.88008123
20 0.93290304 1.51125901
21 2.69796315 0.93290304
22 3.88008123 2.69796315
23 1.11285614 3.88008123
24 2.30847629 1.11285614
25 3.93073805 2.30847629
26 0.99288740 3.93073805
27 0.05287177 0.99288740
28 1.54125120 0.05287177
29 0.35238208 1.54125120
30 0.68404955 0.35238208
31 -1.88039283 0.68404955
32 -0.51873317 -1.88039283
33 0.85008905 -0.51873317
34 0.26240553 0.85008905
35 -4.66153152 0.26240553
36 -2.89647141 -4.66153152
37 -0.45874880 -2.89647141
38 1.48126683 -0.45874880
39 1.94681664 1.48126683
40 1.71404173 1.94681664
41 -0.66153152 1.71404173
42 5.51125901 -0.66153152
43 0.14959936 5.51125901
44 0.40520388 0.14959936
45 -2.99319900 0.40520388
46 -1.49549202 -2.99319900
47 0.36846066 -1.49549202
48 0.27173307 0.36846066
49 0.11960717 0.27173307
50 0.47451579 0.11960717
51 0.26240553 0.47451579
52 -1.64761792 0.26240553
53 1.28564667 -1.64761792
54 -4.67761011 1.28564667
55 -0.80432988 -4.67761011
56 1.16567794 -0.80432988
57 1.36846066 1.16567794
58 0.63122775 1.36846066
59 1.97680882 0.63122775
60 -0.17531708 1.97680882
61 1.77402610 -0.17531708
62 2.00680100 1.77402610
63 -0.60154716 2.00680100
64 1.88683227 -0.60154716
65 3.58515698 1.88683227
66 3.86400265 3.58515698
67 4.00680100 3.86400265
68 -0.81365742 4.00680100
69 0.33171744 -0.81365742
70 -5.14991095 0.33171744
71 -0.77433769 -5.14991095
72 2.47451579 -0.77433769
73 2.50450798 2.47451579
74 0.15892690 2.50450798
75 3.98355986 0.15892690
76 2.66797097 3.98355986
77 0.44452361 2.66797097
78 -0.70760229 0.44452361
79 1.88683227 -0.70760229
80 2.98355986 1.88683227
81 1.02287959 2.98355986
82 -0.14991095 1.02287959
83 0.33846848 -0.14991095
84 1.36846066 0.33846848
85 0.08961499 1.36846066
86 0.50450798 0.08961499
87 1.57124338 0.50450798
88 1.11960717 1.57124338
89 -0.83648704 1.11960717
90 1.61731415 -0.83648704
91 2.11960717 1.61731415
92 -0.84107310 2.11960717
93 -0.39201340 -0.84107310
94 2.46518825 -0.39201340
95 0.82009687 2.46518825
96 1.80401828 0.82009687
97 2.08961499 1.80401828
98 -1.03710478 2.08961499
99 1.02287959 -1.03710478
100 0.61731415 1.02287959
101 5.80401828 0.61731415
102 -0.35269367 5.80401828
103 3.21633476 -0.35269367
104 -2.49549202 3.21633476
105 3.93073805 -2.49549202
106 0.99288740 3.93073805
107 0.50450798 0.99288740
108 -1.79041628 0.50450798
109 1.00004996 -1.79041628
110 1.36846066 1.00004996
111 3.35238208 1.36846066
112 -2.43550766 3.35238208
113 -2.25596608 -2.43550766
114 2.76011250 -2.25596608
115 -0.48874099 2.76011250
116 -1.46549984 -0.48874099
117 -2.46549984 -1.46549984
118 1.39845284 -2.46549984
119 -1.77433769 1.39845284
120 1.45843721 -1.77433769
121 -0.21922286 1.45843721
122 0.50450798 -0.21922286
123 -0.32270149 0.50450798
124 1.88683227 -0.32270149
125 0.94681664 1.88683227
126 2.30847629 0.94681664
127 -0.90105746 2.30847629
128 -3.78825129 -0.90105746
129 1.54125120 -3.78825129
130 -4.05318336 1.54125120
131 2.93073805 -4.05318336
132 -3.14991095 2.93073805
133 0.05962281 -3.14991095
134 -0.32270149 0.05962281
135 -0.97036937 -0.32270149
136 3.25307798 -0.97036937
137 -3.60154716 3.25307798
138 -1.99319900 -3.60154716
139 -3.63153934 -1.99319900
140 1.60123556 -3.63153934
141 0.36846066 1.60123556
142 1.64730633 0.36846066
143 1.39845284 1.64730633
144 -2.73084343 1.39845284
145 3.68404955 -2.73084343
146 -0.62478830 3.68404955
147 1.83401047 -0.62478830
148 -0.50481957 1.83401047
149 -3.06926195 -0.50481957
150 -0.69827474 -3.06926195
151 1.25565449 -0.69827474
152 0.47451579 1.25565449
153 -0.88714386 0.47451579
154 -3.70760229 -0.88714386
155 3.11960717 -3.70760229
156 -1.94712823 3.11960717
157 -0.78825129 -1.94712823
158 2.11960717 -0.78825129
159 -2.71435333 2.11960717
160 -1.57155497 -2.71435333
161 0.76469856 -1.57155497
162 -1.04643232 0.76469856
163 2.33846848 -1.04643232
164 0.51842158 2.33846848
165 -3.12924631 0.51842158
166 -0.25596608 -3.12924631
167 -1.46807635 -0.25596608
168 -1.10600517 -1.46807635
169 2.06678537 -1.10600517
170 -1.30203685 2.06678537
171 0.64730633 -1.30203685
172 -1.58763356 0.64730633
173 -3.67085907 -1.58763356
174 -1.79757884 -3.67085907
175 0.60123556 -1.79757884
176 -1.24205248 0.60123556
177 -1.33202903 -1.24205248
178 -0.31595045 -1.33202903
179 -2.33878007 -0.31595045
180 1.22566231 -2.33878007
181 -2.53481175 1.22566231
182 4.02287959 -2.53481175
183 0.91682445 4.02287959
184 -5.79757884 0.91682445
185 1.42844503 -5.79757884
186 2.57124338 1.42844503
187 -2.11316773 2.57124338
188 -2.18923068 -2.11316773
189 -3.16598953 -2.18923068
190 -0.16598953 -3.16598953
191 0.53450016 -0.16598953
192 -2.45874880 0.53450016
193 1.66121993 -2.45874880
194 -1.28595827 1.66121993
195 0.28564667 -1.28595827
196 2.08961499 0.28564667
197 1.05287177 2.08961499
198 0.96073024 1.05287177
199 2.11960717 0.96073024
200 3.27631913 2.11960717
201 0.05962281 3.27631913
202 -3.51873317 0.05962281
203 -1.88039283 -3.51873317
204 0.36846066 -1.88039283
205 0.02287959 0.36846066
206 1.05287177 0.02287959
207 0.82009687 1.05287177
208 -3.05318336 0.82009687
209 0.33846848 -3.05318336
210 -1.24205248 0.33846848
211 -4.56222743 -1.24205248
212 0.31563886 -4.56222743
213 1.72795533 0.31563886
214 2.00680100 1.72795533
215 0.92357549 2.00680100
216 1.79010468 0.92357549
217 -0.42875662 1.79010468
218 -0.38485084 -0.42875662
219 -0.49549202 -0.38485084
220 -0.64761792 -0.49549202
221 -1.17990313 -0.64761792
222 1.72795533 -1.17990313
223 -2.28595827 1.72795533
224 -0.76042409 -2.28595827
225 -7.17990313 -0.76042409
226 -1.09708915 -7.17990313
227 -0.02319118 -1.09708915
228 -2.03926976 -0.02319118
229 -1.99319900 -2.03926976
230 -0.73759447 -1.99319900
231 -4.10600517 -0.73759447
232 1.36846066 -4.10600517
233 3.94681664 1.36846066
234 -1.48874099 3.94681664
235 -2.30203685 -1.48874099
236 -9.33202903 -2.30203685
237 -0.01644014 -9.33202903
238 2.90074587 -0.01644014
239 -3.12924631 2.90074587
240 -1.67761011 -3.12924631
241 -1.38485084 -1.67761011
242 -2.98644796 -1.38485084
243 -0.85040064 -2.98644796
244 0.88683227 -0.85040064
245 -1.99319900 0.88683227
246 -0.39876444 -1.99319900
247 -4.08317555 -0.39876444
248 0.62865124 -4.08317555
249 0.39587634 0.62865124
250 -3.63153934 0.39587634
251 -3.88039283 -3.63153934
252 -2.22597390 -3.88039283
253 2.11960717 -2.22597390
254 -1.85040064 2.11960717
255 0.14959936 -1.85040064
256 2.06678537 0.14959936
257 0.93073805 2.06678537
258 -1.63153934 0.93073805
259 -8.92646359 -1.63153934
260 1.26240553 -8.92646359
261 -4.32270149 1.26240553
262 -2.35485865 -4.32270149
263 -0.08317555 -2.35485865
264 NA -0.08317555
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.10569357 -2.11991877
[2,] 5.00680100 1.10569357
[3,] 1.14959936 5.00680100
[4,] -1.35269367 1.14959936
[5,] -1.37593481 -1.35269367
[6,] 5.08961499 -1.37593481
[7,] 0.22566231 5.08961499
[8,] -0.80432988 0.22566231
[9,] -0.32270149 -0.80432988
[10,] 1.48126683 -0.32270149
[11,] 0.55732978 1.48126683
[12,] 2.20958372 0.55732978
[13,] 0.47451579 2.20958372
[14,] 1.24174089 0.47451579
[15,] 0.74403392 1.24174089
[16,] 0.91682445 0.74403392
[17,] 4.27173307 0.91682445
[18,] 2.88008123 4.27173307
[19,] 1.51125901 2.88008123
[20,] 0.93290304 1.51125901
[21,] 2.69796315 0.93290304
[22,] 3.88008123 2.69796315
[23,] 1.11285614 3.88008123
[24,] 2.30847629 1.11285614
[25,] 3.93073805 2.30847629
[26,] 0.99288740 3.93073805
[27,] 0.05287177 0.99288740
[28,] 1.54125120 0.05287177
[29,] 0.35238208 1.54125120
[30,] 0.68404955 0.35238208
[31,] -1.88039283 0.68404955
[32,] -0.51873317 -1.88039283
[33,] 0.85008905 -0.51873317
[34,] 0.26240553 0.85008905
[35,] -4.66153152 0.26240553
[36,] -2.89647141 -4.66153152
[37,] -0.45874880 -2.89647141
[38,] 1.48126683 -0.45874880
[39,] 1.94681664 1.48126683
[40,] 1.71404173 1.94681664
[41,] -0.66153152 1.71404173
[42,] 5.51125901 -0.66153152
[43,] 0.14959936 5.51125901
[44,] 0.40520388 0.14959936
[45,] -2.99319900 0.40520388
[46,] -1.49549202 -2.99319900
[47,] 0.36846066 -1.49549202
[48,] 0.27173307 0.36846066
[49,] 0.11960717 0.27173307
[50,] 0.47451579 0.11960717
[51,] 0.26240553 0.47451579
[52,] -1.64761792 0.26240553
[53,] 1.28564667 -1.64761792
[54,] -4.67761011 1.28564667
[55,] -0.80432988 -4.67761011
[56,] 1.16567794 -0.80432988
[57,] 1.36846066 1.16567794
[58,] 0.63122775 1.36846066
[59,] 1.97680882 0.63122775
[60,] -0.17531708 1.97680882
[61,] 1.77402610 -0.17531708
[62,] 2.00680100 1.77402610
[63,] -0.60154716 2.00680100
[64,] 1.88683227 -0.60154716
[65,] 3.58515698 1.88683227
[66,] 3.86400265 3.58515698
[67,] 4.00680100 3.86400265
[68,] -0.81365742 4.00680100
[69,] 0.33171744 -0.81365742
[70,] -5.14991095 0.33171744
[71,] -0.77433769 -5.14991095
[72,] 2.47451579 -0.77433769
[73,] 2.50450798 2.47451579
[74,] 0.15892690 2.50450798
[75,] 3.98355986 0.15892690
[76,] 2.66797097 3.98355986
[77,] 0.44452361 2.66797097
[78,] -0.70760229 0.44452361
[79,] 1.88683227 -0.70760229
[80,] 2.98355986 1.88683227
[81,] 1.02287959 2.98355986
[82,] -0.14991095 1.02287959
[83,] 0.33846848 -0.14991095
[84,] 1.36846066 0.33846848
[85,] 0.08961499 1.36846066
[86,] 0.50450798 0.08961499
[87,] 1.57124338 0.50450798
[88,] 1.11960717 1.57124338
[89,] -0.83648704 1.11960717
[90,] 1.61731415 -0.83648704
[91,] 2.11960717 1.61731415
[92,] -0.84107310 2.11960717
[93,] -0.39201340 -0.84107310
[94,] 2.46518825 -0.39201340
[95,] 0.82009687 2.46518825
[96,] 1.80401828 0.82009687
[97,] 2.08961499 1.80401828
[98,] -1.03710478 2.08961499
[99,] 1.02287959 -1.03710478
[100,] 0.61731415 1.02287959
[101,] 5.80401828 0.61731415
[102,] -0.35269367 5.80401828
[103,] 3.21633476 -0.35269367
[104,] -2.49549202 3.21633476
[105,] 3.93073805 -2.49549202
[106,] 0.99288740 3.93073805
[107,] 0.50450798 0.99288740
[108,] -1.79041628 0.50450798
[109,] 1.00004996 -1.79041628
[110,] 1.36846066 1.00004996
[111,] 3.35238208 1.36846066
[112,] -2.43550766 3.35238208
[113,] -2.25596608 -2.43550766
[114,] 2.76011250 -2.25596608
[115,] -0.48874099 2.76011250
[116,] -1.46549984 -0.48874099
[117,] -2.46549984 -1.46549984
[118,] 1.39845284 -2.46549984
[119,] -1.77433769 1.39845284
[120,] 1.45843721 -1.77433769
[121,] -0.21922286 1.45843721
[122,] 0.50450798 -0.21922286
[123,] -0.32270149 0.50450798
[124,] 1.88683227 -0.32270149
[125,] 0.94681664 1.88683227
[126,] 2.30847629 0.94681664
[127,] -0.90105746 2.30847629
[128,] -3.78825129 -0.90105746
[129,] 1.54125120 -3.78825129
[130,] -4.05318336 1.54125120
[131,] 2.93073805 -4.05318336
[132,] -3.14991095 2.93073805
[133,] 0.05962281 -3.14991095
[134,] -0.32270149 0.05962281
[135,] -0.97036937 -0.32270149
[136,] 3.25307798 -0.97036937
[137,] -3.60154716 3.25307798
[138,] -1.99319900 -3.60154716
[139,] -3.63153934 -1.99319900
[140,] 1.60123556 -3.63153934
[141,] 0.36846066 1.60123556
[142,] 1.64730633 0.36846066
[143,] 1.39845284 1.64730633
[144,] -2.73084343 1.39845284
[145,] 3.68404955 -2.73084343
[146,] -0.62478830 3.68404955
[147,] 1.83401047 -0.62478830
[148,] -0.50481957 1.83401047
[149,] -3.06926195 -0.50481957
[150,] -0.69827474 -3.06926195
[151,] 1.25565449 -0.69827474
[152,] 0.47451579 1.25565449
[153,] -0.88714386 0.47451579
[154,] -3.70760229 -0.88714386
[155,] 3.11960717 -3.70760229
[156,] -1.94712823 3.11960717
[157,] -0.78825129 -1.94712823
[158,] 2.11960717 -0.78825129
[159,] -2.71435333 2.11960717
[160,] -1.57155497 -2.71435333
[161,] 0.76469856 -1.57155497
[162,] -1.04643232 0.76469856
[163,] 2.33846848 -1.04643232
[164,] 0.51842158 2.33846848
[165,] -3.12924631 0.51842158
[166,] -0.25596608 -3.12924631
[167,] -1.46807635 -0.25596608
[168,] -1.10600517 -1.46807635
[169,] 2.06678537 -1.10600517
[170,] -1.30203685 2.06678537
[171,] 0.64730633 -1.30203685
[172,] -1.58763356 0.64730633
[173,] -3.67085907 -1.58763356
[174,] -1.79757884 -3.67085907
[175,] 0.60123556 -1.79757884
[176,] -1.24205248 0.60123556
[177,] -1.33202903 -1.24205248
[178,] -0.31595045 -1.33202903
[179,] -2.33878007 -0.31595045
[180,] 1.22566231 -2.33878007
[181,] -2.53481175 1.22566231
[182,] 4.02287959 -2.53481175
[183,] 0.91682445 4.02287959
[184,] -5.79757884 0.91682445
[185,] 1.42844503 -5.79757884
[186,] 2.57124338 1.42844503
[187,] -2.11316773 2.57124338
[188,] -2.18923068 -2.11316773
[189,] -3.16598953 -2.18923068
[190,] -0.16598953 -3.16598953
[191,] 0.53450016 -0.16598953
[192,] -2.45874880 0.53450016
[193,] 1.66121993 -2.45874880
[194,] -1.28595827 1.66121993
[195,] 0.28564667 -1.28595827
[196,] 2.08961499 0.28564667
[197,] 1.05287177 2.08961499
[198,] 0.96073024 1.05287177
[199,] 2.11960717 0.96073024
[200,] 3.27631913 2.11960717
[201,] 0.05962281 3.27631913
[202,] -3.51873317 0.05962281
[203,] -1.88039283 -3.51873317
[204,] 0.36846066 -1.88039283
[205,] 0.02287959 0.36846066
[206,] 1.05287177 0.02287959
[207,] 0.82009687 1.05287177
[208,] -3.05318336 0.82009687
[209,] 0.33846848 -3.05318336
[210,] -1.24205248 0.33846848
[211,] -4.56222743 -1.24205248
[212,] 0.31563886 -4.56222743
[213,] 1.72795533 0.31563886
[214,] 2.00680100 1.72795533
[215,] 0.92357549 2.00680100
[216,] 1.79010468 0.92357549
[217,] -0.42875662 1.79010468
[218,] -0.38485084 -0.42875662
[219,] -0.49549202 -0.38485084
[220,] -0.64761792 -0.49549202
[221,] -1.17990313 -0.64761792
[222,] 1.72795533 -1.17990313
[223,] -2.28595827 1.72795533
[224,] -0.76042409 -2.28595827
[225,] -7.17990313 -0.76042409
[226,] -1.09708915 -7.17990313
[227,] -0.02319118 -1.09708915
[228,] -2.03926976 -0.02319118
[229,] -1.99319900 -2.03926976
[230,] -0.73759447 -1.99319900
[231,] -4.10600517 -0.73759447
[232,] 1.36846066 -4.10600517
[233,] 3.94681664 1.36846066
[234,] -1.48874099 3.94681664
[235,] -2.30203685 -1.48874099
[236,] -9.33202903 -2.30203685
[237,] -0.01644014 -9.33202903
[238,] 2.90074587 -0.01644014
[239,] -3.12924631 2.90074587
[240,] -1.67761011 -3.12924631
[241,] -1.38485084 -1.67761011
[242,] -2.98644796 -1.38485084
[243,] -0.85040064 -2.98644796
[244,] 0.88683227 -0.85040064
[245,] -1.99319900 0.88683227
[246,] -0.39876444 -1.99319900
[247,] -4.08317555 -0.39876444
[248,] 0.62865124 -4.08317555
[249,] 0.39587634 0.62865124
[250,] -3.63153934 0.39587634
[251,] -3.88039283 -3.63153934
[252,] -2.22597390 -3.88039283
[253,] 2.11960717 -2.22597390
[254,] -1.85040064 2.11960717
[255,] 0.14959936 -1.85040064
[256,] 2.06678537 0.14959936
[257,] 0.93073805 2.06678537
[258,] -1.63153934 0.93073805
[259,] -8.92646359 -1.63153934
[260,] 1.26240553 -8.92646359
[261,] -4.32270149 1.26240553
[262,] -2.35485865 -4.32270149
[263,] -0.08317555 -2.35485865
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.10569357 -2.11991877
2 5.00680100 1.10569357
3 1.14959936 5.00680100
4 -1.35269367 1.14959936
5 -1.37593481 -1.35269367
6 5.08961499 -1.37593481
7 0.22566231 5.08961499
8 -0.80432988 0.22566231
9 -0.32270149 -0.80432988
10 1.48126683 -0.32270149
11 0.55732978 1.48126683
12 2.20958372 0.55732978
13 0.47451579 2.20958372
14 1.24174089 0.47451579
15 0.74403392 1.24174089
16 0.91682445 0.74403392
17 4.27173307 0.91682445
18 2.88008123 4.27173307
19 1.51125901 2.88008123
20 0.93290304 1.51125901
21 2.69796315 0.93290304
22 3.88008123 2.69796315
23 1.11285614 3.88008123
24 2.30847629 1.11285614
25 3.93073805 2.30847629
26 0.99288740 3.93073805
27 0.05287177 0.99288740
28 1.54125120 0.05287177
29 0.35238208 1.54125120
30 0.68404955 0.35238208
31 -1.88039283 0.68404955
32 -0.51873317 -1.88039283
33 0.85008905 -0.51873317
34 0.26240553 0.85008905
35 -4.66153152 0.26240553
36 -2.89647141 -4.66153152
37 -0.45874880 -2.89647141
38 1.48126683 -0.45874880
39 1.94681664 1.48126683
40 1.71404173 1.94681664
41 -0.66153152 1.71404173
42 5.51125901 -0.66153152
43 0.14959936 5.51125901
44 0.40520388 0.14959936
45 -2.99319900 0.40520388
46 -1.49549202 -2.99319900
47 0.36846066 -1.49549202
48 0.27173307 0.36846066
49 0.11960717 0.27173307
50 0.47451579 0.11960717
51 0.26240553 0.47451579
52 -1.64761792 0.26240553
53 1.28564667 -1.64761792
54 -4.67761011 1.28564667
55 -0.80432988 -4.67761011
56 1.16567794 -0.80432988
57 1.36846066 1.16567794
58 0.63122775 1.36846066
59 1.97680882 0.63122775
60 -0.17531708 1.97680882
61 1.77402610 -0.17531708
62 2.00680100 1.77402610
63 -0.60154716 2.00680100
64 1.88683227 -0.60154716
65 3.58515698 1.88683227
66 3.86400265 3.58515698
67 4.00680100 3.86400265
68 -0.81365742 4.00680100
69 0.33171744 -0.81365742
70 -5.14991095 0.33171744
71 -0.77433769 -5.14991095
72 2.47451579 -0.77433769
73 2.50450798 2.47451579
74 0.15892690 2.50450798
75 3.98355986 0.15892690
76 2.66797097 3.98355986
77 0.44452361 2.66797097
78 -0.70760229 0.44452361
79 1.88683227 -0.70760229
80 2.98355986 1.88683227
81 1.02287959 2.98355986
82 -0.14991095 1.02287959
83 0.33846848 -0.14991095
84 1.36846066 0.33846848
85 0.08961499 1.36846066
86 0.50450798 0.08961499
87 1.57124338 0.50450798
88 1.11960717 1.57124338
89 -0.83648704 1.11960717
90 1.61731415 -0.83648704
91 2.11960717 1.61731415
92 -0.84107310 2.11960717
93 -0.39201340 -0.84107310
94 2.46518825 -0.39201340
95 0.82009687 2.46518825
96 1.80401828 0.82009687
97 2.08961499 1.80401828
98 -1.03710478 2.08961499
99 1.02287959 -1.03710478
100 0.61731415 1.02287959
101 5.80401828 0.61731415
102 -0.35269367 5.80401828
103 3.21633476 -0.35269367
104 -2.49549202 3.21633476
105 3.93073805 -2.49549202
106 0.99288740 3.93073805
107 0.50450798 0.99288740
108 -1.79041628 0.50450798
109 1.00004996 -1.79041628
110 1.36846066 1.00004996
111 3.35238208 1.36846066
112 -2.43550766 3.35238208
113 -2.25596608 -2.43550766
114 2.76011250 -2.25596608
115 -0.48874099 2.76011250
116 -1.46549984 -0.48874099
117 -2.46549984 -1.46549984
118 1.39845284 -2.46549984
119 -1.77433769 1.39845284
120 1.45843721 -1.77433769
121 -0.21922286 1.45843721
122 0.50450798 -0.21922286
123 -0.32270149 0.50450798
124 1.88683227 -0.32270149
125 0.94681664 1.88683227
126 2.30847629 0.94681664
127 -0.90105746 2.30847629
128 -3.78825129 -0.90105746
129 1.54125120 -3.78825129
130 -4.05318336 1.54125120
131 2.93073805 -4.05318336
132 -3.14991095 2.93073805
133 0.05962281 -3.14991095
134 -0.32270149 0.05962281
135 -0.97036937 -0.32270149
136 3.25307798 -0.97036937
137 -3.60154716 3.25307798
138 -1.99319900 -3.60154716
139 -3.63153934 -1.99319900
140 1.60123556 -3.63153934
141 0.36846066 1.60123556
142 1.64730633 0.36846066
143 1.39845284 1.64730633
144 -2.73084343 1.39845284
145 3.68404955 -2.73084343
146 -0.62478830 3.68404955
147 1.83401047 -0.62478830
148 -0.50481957 1.83401047
149 -3.06926195 -0.50481957
150 -0.69827474 -3.06926195
151 1.25565449 -0.69827474
152 0.47451579 1.25565449
153 -0.88714386 0.47451579
154 -3.70760229 -0.88714386
155 3.11960717 -3.70760229
156 -1.94712823 3.11960717
157 -0.78825129 -1.94712823
158 2.11960717 -0.78825129
159 -2.71435333 2.11960717
160 -1.57155497 -2.71435333
161 0.76469856 -1.57155497
162 -1.04643232 0.76469856
163 2.33846848 -1.04643232
164 0.51842158 2.33846848
165 -3.12924631 0.51842158
166 -0.25596608 -3.12924631
167 -1.46807635 -0.25596608
168 -1.10600517 -1.46807635
169 2.06678537 -1.10600517
170 -1.30203685 2.06678537
171 0.64730633 -1.30203685
172 -1.58763356 0.64730633
173 -3.67085907 -1.58763356
174 -1.79757884 -3.67085907
175 0.60123556 -1.79757884
176 -1.24205248 0.60123556
177 -1.33202903 -1.24205248
178 -0.31595045 -1.33202903
179 -2.33878007 -0.31595045
180 1.22566231 -2.33878007
181 -2.53481175 1.22566231
182 4.02287959 -2.53481175
183 0.91682445 4.02287959
184 -5.79757884 0.91682445
185 1.42844503 -5.79757884
186 2.57124338 1.42844503
187 -2.11316773 2.57124338
188 -2.18923068 -2.11316773
189 -3.16598953 -2.18923068
190 -0.16598953 -3.16598953
191 0.53450016 -0.16598953
192 -2.45874880 0.53450016
193 1.66121993 -2.45874880
194 -1.28595827 1.66121993
195 0.28564667 -1.28595827
196 2.08961499 0.28564667
197 1.05287177 2.08961499
198 0.96073024 1.05287177
199 2.11960717 0.96073024
200 3.27631913 2.11960717
201 0.05962281 3.27631913
202 -3.51873317 0.05962281
203 -1.88039283 -3.51873317
204 0.36846066 -1.88039283
205 0.02287959 0.36846066
206 1.05287177 0.02287959
207 0.82009687 1.05287177
208 -3.05318336 0.82009687
209 0.33846848 -3.05318336
210 -1.24205248 0.33846848
211 -4.56222743 -1.24205248
212 0.31563886 -4.56222743
213 1.72795533 0.31563886
214 2.00680100 1.72795533
215 0.92357549 2.00680100
216 1.79010468 0.92357549
217 -0.42875662 1.79010468
218 -0.38485084 -0.42875662
219 -0.49549202 -0.38485084
220 -0.64761792 -0.49549202
221 -1.17990313 -0.64761792
222 1.72795533 -1.17990313
223 -2.28595827 1.72795533
224 -0.76042409 -2.28595827
225 -7.17990313 -0.76042409
226 -1.09708915 -7.17990313
227 -0.02319118 -1.09708915
228 -2.03926976 -0.02319118
229 -1.99319900 -2.03926976
230 -0.73759447 -1.99319900
231 -4.10600517 -0.73759447
232 1.36846066 -4.10600517
233 3.94681664 1.36846066
234 -1.48874099 3.94681664
235 -2.30203685 -1.48874099
236 -9.33202903 -2.30203685
237 -0.01644014 -9.33202903
238 2.90074587 -0.01644014
239 -3.12924631 2.90074587
240 -1.67761011 -3.12924631
241 -1.38485084 -1.67761011
242 -2.98644796 -1.38485084
243 -0.85040064 -2.98644796
244 0.88683227 -0.85040064
245 -1.99319900 0.88683227
246 -0.39876444 -1.99319900
247 -4.08317555 -0.39876444
248 0.62865124 -4.08317555
249 0.39587634 0.62865124
250 -3.63153934 0.39587634
251 -3.88039283 -3.63153934
252 -2.22597390 -3.88039283
253 2.11960717 -2.22597390
254 -1.85040064 2.11960717
255 0.14959936 -1.85040064
256 2.06678537 0.14959936
257 0.93073805 2.06678537
258 -1.63153934 0.93073805
259 -8.92646359 -1.63153934
260 1.26240553 -8.92646359
261 -4.32270149 1.26240553
262 -2.35485865 -4.32270149
263 -0.08317555 -2.35485865
> 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/fisher/rcomp/tmp/7sxod1352133134.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/fisher/rcomp/tmp/8nh7g1352133134.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/fisher/rcomp/tmp/9aw6w1352133134.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/fisher/rcomp/tmp/10fy2m1352133134.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11o4lq1352133134.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12hhf31352133134.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13jop01352133134.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14yi7v1352133134.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/fisher/rcomp/tmp/15ycck1352133134.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/fisher/rcomp/tmp/1696lj1352133134.tab")
+ }
>
> try(system("convert tmp/1qurw1352133134.ps tmp/1qurw1352133134.png",intern=TRUE))
character(0)
> try(system("convert tmp/2kbou1352133134.ps tmp/2kbou1352133134.png",intern=TRUE))
character(0)
> try(system("convert tmp/3oupi1352133134.ps tmp/3oupi1352133134.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ugr61352133134.ps tmp/4ugr61352133134.png",intern=TRUE))
character(0)
> try(system("convert tmp/5min31352133134.ps tmp/5min31352133134.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wrgp1352133134.ps tmp/6wrgp1352133134.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sxod1352133134.ps tmp/7sxod1352133134.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nh7g1352133134.ps tmp/8nh7g1352133134.png",intern=TRUE))
character(0)
> try(system("convert tmp/9aw6w1352133134.ps tmp/9aw6w1352133134.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fy2m1352133134.ps tmp/10fy2m1352133134.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.947 1.146 11.106