R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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.
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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(12.42
+ ,10.4
+ ,12.37
+ ,10.5
+ ,12.53
+ ,10.6
+ ,12.02
+ ,10.6
+ ,11.70
+ ,10.7
+ ,11.67
+ ,10.7
+ ,11.51
+ ,10.8
+ ,11.50
+ ,10.9
+ ,11.77
+ ,10.9
+ ,11.75
+ ,11.0
+ ,11.87
+ ,11.0
+ ,12.18
+ ,10.9
+ ,12.29
+ ,10.9
+ ,12.41
+ ,10.8
+ ,12.55
+ ,10.8
+ ,12.39
+ ,10.8
+ ,12.39
+ ,10.8
+ ,12.40
+ ,10.8
+ ,12.33
+ ,10.8
+ ,12.16
+ ,10.9
+ ,12.12
+ ,10.9
+ ,12.13
+ ,10.8
+ ,11.90
+ ,10.7
+ ,11.84
+ ,10.6
+ ,11.75
+ ,10.6
+ ,11.73
+ ,10.6
+ ,11.73
+ ,10.4
+ ,11.64
+ ,10.2
+ ,11.45
+ ,10.1
+ ,10.78
+ ,10.0
+ ,10.67
+ ,9.9
+ ,10.80
+ ,9.9
+ ,10.76
+ ,9.9
+ ,10.38
+ ,9.9
+ ,9.99
+ ,9.9
+ ,9.94
+ ,10.0
+ ,10.05
+ ,10.0
+ ,9.99
+ ,10.1
+ ,9.48
+ ,10.1
+ ,8.29
+ ,10.1
+ ,8.48
+ ,10.1
+ ,8.58
+ ,10.1
+ ,8.23
+ ,10.0
+ ,7.92
+ ,10.0
+ ,8.04
+ ,10.0
+ ,8.09
+ ,10.0
+ ,8.09
+ ,10.1
+ ,8.27
+ ,10.1
+ ,8.26
+ ,10.1
+ ,8.20
+ ,10.0
+ ,8.11
+ ,10.0
+ ,8.02
+ ,10.0
+ ,8.05
+ ,9.9
+ ,8.10
+ ,9.9
+ ,8.07
+ ,9.8
+ ,7.96
+ ,9.7
+ ,8.18
+ ,9.7
+ ,8.64
+ ,9.6
+ ,8.35
+ ,9.6
+ ,8.27
+ ,9.5
+ ,8.16
+ ,9.4
+ ,7.78
+ ,9.4
+ ,7.67
+ ,9.3
+ ,7.79
+ ,9.2
+ ,7.93
+ ,9.1
+ ,7.95
+ ,8.9
+ ,8.09
+ ,8.8
+ ,8.20
+ ,8.7
+ ,8.24
+ ,8.5
+ ,8.09
+ ,8.3
+ ,8.05
+ ,8.2
+ ,8.14
+ ,8.1
+ ,8.17
+ ,7.9
+ ,8.30
+ ,7.8
+ ,8.51
+ ,7.7
+ ,8.42
+ ,7.6
+ ,8.36
+ ,7.5
+ ,8.35
+ ,7.4
+ ,8.39
+ ,7.3
+ ,8.36
+ ,7.3
+ ,8.43
+ ,7.2
+ ,8.68
+ ,7.1
+ ,9.10
+ ,7.0
+ ,9.40
+ ,6.9
+ ,9.80
+ ,6.8
+ ,10.40
+ ,6.7
+ ,10.26
+ ,6.7
+ ,10.00
+ ,6.6
+ ,9.82
+ ,6.6
+ ,9.75
+ ,6.6
+ ,9.56
+ ,6.5
+ ,10.01
+ ,6.5
+ ,10.30
+ ,6.4
+ ,10.22
+ ,6.4
+ ,10.01
+ ,6.4
+ ,9.95
+ ,6.4
+ ,9.87
+ ,6.4
+ ,9.25
+ ,6.4
+ ,9.23
+ ,6.4
+ ,9.17
+ ,6.3
+ ,9.14
+ ,6.4
+ ,9.26
+ ,6.4
+ ,9.47
+ ,6.4
+ ,9.41
+ ,6.4
+ ,9.22
+ ,6.5
+ ,9.13
+ ,6.5
+ ,9.15
+ ,6.6
+ ,9.13
+ ,6.6
+ ,8.72
+ ,6.7
+ ,8.72
+ ,6.7
+ ,8.81
+ ,6.8
+ ,8.86
+ ,6.9
+ ,8.83
+ ,7.0
+ ,8.92
+ ,7.0
+ ,8.91
+ ,7.1
+ ,9.03
+ ,7.2
+ ,8.77
+ ,7.2
+ ,8.28
+ ,7.4
+ ,8.04
+ ,7.5
+ ,7.95
+ ,7.6
+ ,7.57
+ ,7.8
+ ,7.65
+ ,7.9
+ ,7.37
+ ,8.1
+ ,7.44
+ ,8.2
+ ,7.43
+ ,8.4
+ ,7.23
+ ,8.5
+ ,7.05
+ ,8.7
+ ,7.08
+ ,8.9
+ ,7.22
+ ,9.0
+ ,7.19
+ ,9.2
+ ,6.92
+ ,9.3
+ ,6.59
+ ,9.5
+ ,6.52
+ ,9.6
+ ,6.70
+ ,9.6
+ ,7.14
+ ,9.7
+ ,7.29
+ ,9.8
+ ,7.54
+ ,9.9
+ ,7.98
+ ,9.9
+ ,7.95
+ ,9.8
+ ,8.21
+ ,9.8
+ ,8.58
+ ,9.8
+ ,8.45
+ ,9.8
+ ,8.35
+ ,9.7
+ ,8.30
+ ,9.7
+ ,8.45
+ ,9.7
+ ,8.27
+ ,9.7
+ ,8.16
+ ,9.6
+ ,7.85
+ ,9.6
+ ,7.59
+ ,9.6
+ ,7.33
+ ,9.6
+ ,7.33
+ ,9.6
+ ,7.19
+ ,9.7
+ ,7.04
+ ,9.7
+ ,7.06
+ ,9.8
+ ,6.80
+ ,9.8
+ ,6.70
+ ,9.9
+ ,6.44
+ ,9.9
+ ,6.64
+ ,9.9
+ ,6.84
+ ,9.8
+ ,6.67
+ ,9.7
+ ,6.69
+ ,9.7
+ ,6.78
+ ,9.6
+ ,6.78
+ ,9.5
+ ,6.62
+ ,9.4
+ ,6.45
+ ,9.4
+ ,6.10
+ ,9.3
+ ,6.00
+ ,9.2
+ ,5.90
+ ,9.2
+ ,5.89
+ ,9.2
+ ,5.65
+ ,9.1
+ ,5.85
+ ,9.1
+ ,6.02
+ ,9.1
+ ,5.90
+ ,9.1
+ ,5.83
+ ,9.2
+ ,5.64
+ ,9.3
+ ,5.75
+ ,9.3
+ ,5.69
+ ,9.3
+ ,5.69
+ ,9.3
+ ,5.68
+ ,9.3
+ ,5.45
+ ,9.4
+ ,5.22
+ ,9.4
+ ,5.11
+ ,9.4
+ ,5.03
+ ,9.5
+ ,5.03
+ ,9.5
+ ,5.09
+ ,9.4
+ ,4.96
+ ,9.4
+ ,4.88
+ ,9.3
+ ,4.66
+ ,9.4
+ ,4.34
+ ,9.4
+ ,4.28
+ ,9.2
+ ,4.33
+ ,9.1
+ ,4.09
+ ,9.1
+ ,3.90
+ ,9.1
+ ,4.04
+ ,9.0
+ ,4.26
+ ,9.0
+ ,4.11
+ ,8.9
+ ,4.29
+ ,8.8
+ ,4.64
+ ,8.7
+ ,4.94
+ ,8.5
+ ,5.18
+ ,8.3
+ ,5.34
+ ,8.1
+ ,5.58
+ ,7.9
+ ,5.30
+ ,7.8
+ ,5.41
+ ,7.6
+ ,5.79
+ ,7.4
+ ,5.79
+ ,7.2
+ ,5.62
+ ,7.0
+ ,5.52
+ ,7.0
+ ,5.69
+ ,6.8
+ ,5.53
+ ,6.8
+ ,5.60
+ ,6.7
+ ,5.56
+ ,6.8
+ ,5.63
+ ,6.7
+ ,5.58
+ ,6.7
+ ,5.52
+ ,6.7
+ ,5.28
+ ,6.5
+ ,5.16
+ ,6.3
+ ,5.16
+ ,6.3
+ ,5.08
+ ,6.3
+ ,5.21
+ ,6.5
+ ,5.38
+ ,6.6
+ ,5.33
+ ,6.5
+ ,5.35
+ ,6.3
+ ,5.15
+ ,6.3
+ ,5.14
+ ,6.5
+ ,4.89
+ ,7.0
+ ,4.75
+ ,7.1
+ ,4.97
+ ,7.3
+ ,5.08
+ ,7.3
+ ,5.15
+ ,7.4
+ ,5.37
+ ,7.4
+ ,5.37
+ ,7.3
+ ,5.38
+ ,7.4
+ ,5.24
+ ,7.5
+ ,5.09
+ ,7.7
+ ,4.80
+ ,7.7
+ ,4.60
+ ,7.7
+ ,4.66
+ ,7.7
+ ,4.64
+ ,7.7
+ ,4.46
+ ,7.8
+ ,4.28
+ ,8.0
+ ,4.11
+ ,8.1
+ ,4.15
+ ,8.1
+ ,4.29
+ ,8.2
+ ,3.95
+ ,8.2
+ ,3.74
+ ,8.2
+ ,4.06
+ ,8.1
+ ,4.22
+ ,8.1
+ ,4.25
+ ,8.2
+ ,4.31
+ ,8.3
+ ,4.43
+ ,8.3
+ ,4.38
+ ,8.4
+ ,4.26
+ ,8.5
+ ,4.26
+ ,8.5
+ ,4.07
+ ,8.4
+ ,4.26
+ ,8.0
+ ,4.40
+ ,7.9
+ ,4.46
+ ,8.1
+ ,4.34
+ ,8.5
+ ,4.18
+ ,8.8
+ ,4.11
+ ,8.8
+ ,3.98
+ ,8.6
+ ,3.85
+ ,8.3
+ ,3.66
+ ,8.3
+ ,3.59
+ ,8.3
+ ,3.57
+ ,8.4
+ ,3.76
+ ,8.4
+ ,3.60
+ ,8.5
+ ,3.43
+ ,8.6
+ ,3.26
+ ,8.6
+ ,3.30
+ ,8.6
+ ,3.31
+ ,8.6
+ ,3.14
+ ,8.6
+ ,3.30
+ ,8.5
+ ,3.49
+ ,8.4
+ ,3.39
+ ,8.4
+ ,3.37
+ ,8.4
+ ,3.54
+ ,8.5
+ ,3.70
+ ,8.5
+ ,3.96
+ ,8.6
+ ,4.03
+ ,8.6
+ ,4.02
+ ,8.4
+ ,4.04
+ ,8.2
+ ,3.92
+ ,8.0
+ ,3.79
+ ,8.0
+ ,3.83
+ ,8.0
+ ,3.76
+ ,8.0
+ ,3.82
+ ,7.9
+ ,4.06
+ ,7.9
+ ,4.11
+ ,7.8
+ ,4.01
+ ,7.8
+ ,4.22
+ ,8.0)
+ ,dim=c(2
+ ,292)
+ ,dimnames=list(c('Rente'
+ ,'werkloosheid')
+ ,1:292))
> y <- array(NA,dim=c(2,292),dimnames=list(c('Rente','werkloosheid'),1:292))
> 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 = '2'
> #'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
werkloosheid Rente
1 10.4 12.42
2 10.5 12.37
3 10.6 12.53
4 10.6 12.02
5 10.7 11.70
6 10.7 11.67
7 10.8 11.51
8 10.9 11.50
9 10.9 11.77
10 11.0 11.75
11 11.0 11.87
12 10.9 12.18
13 10.9 12.29
14 10.8 12.41
15 10.8 12.55
16 10.8 12.39
17 10.8 12.39
18 10.8 12.40
19 10.8 12.33
20 10.9 12.16
21 10.9 12.12
22 10.8 12.13
23 10.7 11.90
24 10.6 11.84
25 10.6 11.75
26 10.6 11.73
27 10.4 11.73
28 10.2 11.64
29 10.1 11.45
30 10.0 10.78
31 9.9 10.67
32 9.9 10.80
33 9.9 10.76
34 9.9 10.38
35 9.9 9.99
36 10.0 9.94
37 10.0 10.05
38 10.1 9.99
39 10.1 9.48
40 10.1 8.29
41 10.1 8.48
42 10.1 8.58
43 10.0 8.23
44 10.0 7.92
45 10.0 8.04
46 10.0 8.09
47 10.1 8.09
48 10.1 8.27
49 10.1 8.26
50 10.0 8.20
51 10.0 8.11
52 10.0 8.02
53 9.9 8.05
54 9.9 8.10
55 9.8 8.07
56 9.7 7.96
57 9.7 8.18
58 9.6 8.64
59 9.6 8.35
60 9.5 8.27
61 9.4 8.16
62 9.4 7.78
63 9.3 7.67
64 9.2 7.79
65 9.1 7.93
66 8.9 7.95
67 8.8 8.09
68 8.7 8.20
69 8.5 8.24
70 8.3 8.09
71 8.2 8.05
72 8.1 8.14
73 7.9 8.17
74 7.8 8.30
75 7.7 8.51
76 7.6 8.42
77 7.5 8.36
78 7.4 8.35
79 7.3 8.39
80 7.3 8.36
81 7.2 8.43
82 7.1 8.68
83 7.0 9.10
84 6.9 9.40
85 6.8 9.80
86 6.7 10.40
87 6.7 10.26
88 6.6 10.00
89 6.6 9.82
90 6.6 9.75
91 6.5 9.56
92 6.5 10.01
93 6.4 10.30
94 6.4 10.22
95 6.4 10.01
96 6.4 9.95
97 6.4 9.87
98 6.4 9.25
99 6.4 9.23
100 6.3 9.17
101 6.4 9.14
102 6.4 9.26
103 6.4 9.47
104 6.4 9.41
105 6.5 9.22
106 6.5 9.13
107 6.6 9.15
108 6.6 9.13
109 6.7 8.72
110 6.7 8.72
111 6.8 8.81
112 6.9 8.86
113 7.0 8.83
114 7.0 8.92
115 7.1 8.91
116 7.2 9.03
117 7.2 8.77
118 7.4 8.28
119 7.5 8.04
120 7.6 7.95
121 7.8 7.57
122 7.9 7.65
123 8.1 7.37
124 8.2 7.44
125 8.4 7.43
126 8.5 7.23
127 8.7 7.05
128 8.9 7.08
129 9.0 7.22
130 9.2 7.19
131 9.3 6.92
132 9.5 6.59
133 9.6 6.52
134 9.6 6.70
135 9.7 7.14
136 9.8 7.29
137 9.9 7.54
138 9.9 7.98
139 9.8 7.95
140 9.8 8.21
141 9.8 8.58
142 9.8 8.45
143 9.7 8.35
144 9.7 8.30
145 9.7 8.45
146 9.7 8.27
147 9.6 8.16
148 9.6 7.85
149 9.6 7.59
150 9.6 7.33
151 9.6 7.33
152 9.7 7.19
153 9.7 7.04
154 9.8 7.06
155 9.8 6.80
156 9.9 6.70
157 9.9 6.44
158 9.9 6.64
159 9.8 6.84
160 9.7 6.67
161 9.7 6.69
162 9.6 6.78
163 9.5 6.78
164 9.4 6.62
165 9.4 6.45
166 9.3 6.10
167 9.2 6.00
168 9.2 5.90
169 9.2 5.89
170 9.1 5.65
171 9.1 5.85
172 9.1 6.02
173 9.1 5.90
174 9.2 5.83
175 9.3 5.64
176 9.3 5.75
177 9.3 5.69
178 9.3 5.69
179 9.3 5.68
180 9.4 5.45
181 9.4 5.22
182 9.4 5.11
183 9.5 5.03
184 9.5 5.03
185 9.4 5.09
186 9.4 4.96
187 9.3 4.88
188 9.4 4.66
189 9.4 4.34
190 9.2 4.28
191 9.1 4.33
192 9.1 4.09
193 9.1 3.90
194 9.0 4.04
195 9.0 4.26
196 8.9 4.11
197 8.8 4.29
198 8.7 4.64
199 8.5 4.94
200 8.3 5.18
201 8.1 5.34
202 7.9 5.58
203 7.8 5.30
204 7.6 5.41
205 7.4 5.79
206 7.2 5.79
207 7.0 5.62
208 7.0 5.52
209 6.8 5.69
210 6.8 5.53
211 6.7 5.60
212 6.8 5.56
213 6.7 5.63
214 6.7 5.58
215 6.7 5.52
216 6.5 5.28
217 6.3 5.16
218 6.3 5.16
219 6.3 5.08
220 6.5 5.21
221 6.6 5.38
222 6.5 5.33
223 6.3 5.35
224 6.3 5.15
225 6.5 5.14
226 7.0 4.89
227 7.1 4.75
228 7.3 4.97
229 7.3 5.08
230 7.4 5.15
231 7.4 5.37
232 7.3 5.37
233 7.4 5.38
234 7.5 5.24
235 7.7 5.09
236 7.7 4.80
237 7.7 4.60
238 7.7 4.66
239 7.7 4.64
240 7.8 4.46
241 8.0 4.28
242 8.1 4.11
243 8.1 4.15
244 8.2 4.29
245 8.2 3.95
246 8.2 3.74
247 8.1 4.06
248 8.1 4.22
249 8.2 4.25
250 8.3 4.31
251 8.3 4.43
252 8.4 4.38
253 8.5 4.26
254 8.5 4.26
255 8.4 4.07
256 8.0 4.26
257 7.9 4.40
258 8.1 4.46
259 8.5 4.34
260 8.8 4.18
261 8.8 4.11
262 8.6 3.98
263 8.3 3.85
264 8.3 3.66
265 8.3 3.59
266 8.4 3.57
267 8.4 3.76
268 8.5 3.60
269 8.6 3.43
270 8.6 3.26
271 8.6 3.30
272 8.6 3.31
273 8.6 3.14
274 8.5 3.30
275 8.4 3.49
276 8.4 3.39
277 8.4 3.37
278 8.5 3.54
279 8.5 3.70
280 8.6 3.96
281 8.6 4.03
282 8.4 4.02
283 8.2 4.04
284 8.0 3.92
285 8.0 3.79
286 8.0 3.83
287 8.0 3.76
288 7.9 3.82
289 7.9 4.06
290 7.8 4.11
291 7.8 4.01
292 8.0 4.22
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Rente
7.3874 0.1713
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.7519 -0.9165 0.4784 1.0039 1.5998
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.38742 0.21730 33.996 < 2e-16 ***
Rente 0.17130 0.02872 5.966 7.09e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.254 on 290 degrees of freedom
Multiple R-squared: 0.1093, Adjusted R-squared: 0.1062
F-statistic: 35.59 on 1 and 290 DF, p-value: 7.088e-09
> 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,] 5.752047e-04 1.150409e-03 9.994248e-01
[2,] 3.548920e-05 7.097840e-05 9.999645e-01
[3,] 2.548139e-06 5.096277e-06 9.999975e-01
[4,] 4.280740e-07 8.561479e-07 9.999996e-01
[5,] 1.669808e-07 3.339616e-07 9.999998e-01
[6,] 1.247107e-07 2.494214e-07 9.999999e-01
[7,] 7.494023e-08 1.498805e-07 9.999999e-01
[8,] 3.322333e-08 6.644665e-08 1.000000e+00
[9,] 1.362015e-08 2.724030e-08 1.000000e+00
[10,] 2.753432e-09 5.506864e-09 1.000000e+00
[11,] 5.708543e-10 1.141709e-09 1.000000e+00
[12,] 9.264920e-11 1.852984e-10 1.000000e+00
[13,] 1.441068e-11 2.882137e-11 1.000000e+00
[14,] 2.177334e-12 4.354669e-12 1.000000e+00
[15,] 3.100929e-13 6.201858e-13 1.000000e+00
[16,] 5.889810e-14 1.177962e-13 1.000000e+00
[17,] 1.060091e-14 2.120183e-14 1.000000e+00
[18,] 1.401527e-15 2.803055e-15 1.000000e+00
[19,] 2.485664e-16 4.971327e-16 1.000000e+00
[20,] 9.238551e-17 1.847710e-16 1.000000e+00
[21,] 3.244586e-17 6.489173e-17 1.000000e+00
[22,] 1.007114e-17 2.014229e-17 1.000000e+00
[23,] 2.010961e-17 4.021923e-17 1.000000e+00
[24,] 2.253677e-16 4.507354e-16 1.000000e+00
[25,] 1.437136e-15 2.874273e-15 1.000000e+00
[26,] 1.627868e-15 3.255735e-15 1.000000e+00
[27,] 1.033028e-15 2.066057e-15 1.000000e+00
[28,] 5.145967e-16 1.029193e-15 1.000000e+00
[29,] 1.856825e-16 3.713649e-16 1.000000e+00
[30,] 4.184387e-17 8.368774e-17 1.000000e+00
[31,] 1.044089e-17 2.088179e-17 1.000000e+00
[32,] 3.356218e-18 6.712435e-18 1.000000e+00
[33,] 8.709624e-19 1.741925e-18 1.000000e+00
[34,] 3.148720e-19 6.297439e-19 1.000000e+00
[35,] 2.404416e-19 4.808832e-19 1.000000e+00
[36,] 1.471672e-18 2.943345e-18 1.000000e+00
[37,] 1.425886e-18 2.851772e-18 1.000000e+00
[38,] 8.022030e-19 1.604406e-18 1.000000e+00
[39,] 3.414448e-19 6.828897e-19 1.000000e+00
[40,] 1.598585e-19 3.197169e-19 1.000000e+00
[41,] 6.071082e-20 1.214216e-19 1.000000e+00
[42,] 2.117955e-20 4.235911e-20 1.000000e+00
[43,] 9.671270e-21 1.934254e-20 1.000000e+00
[44,] 3.763368e-21 7.526737e-21 1.000000e+00
[45,] 1.434919e-21 2.869838e-21 1.000000e+00
[46,] 4.465487e-22 8.930975e-22 1.000000e+00
[47,] 1.418608e-22 2.837216e-22 1.000000e+00
[48,] 4.608017e-23 9.216033e-23 1.000000e+00
[49,] 1.362774e-23 2.725548e-23 1.000000e+00
[50,] 4.081477e-24 8.162953e-24 1.000000e+00
[51,] 1.311229e-24 2.622458e-24 1.000000e+00
[52,] 4.865086e-25 9.730173e-25 1.000000e+00
[53,] 2.039438e-25 4.078875e-25 1.000000e+00
[54,] 2.012902e-25 4.025805e-25 1.000000e+00
[55,] 1.324281e-25 2.648563e-25 1.000000e+00
[56,] 1.236766e-25 2.473531e-25 1.000000e+00
[57,] 1.602742e-25 3.205485e-25 1.000000e+00
[58,] 1.138546e-25 2.277091e-25 1.000000e+00
[59,] 1.064655e-25 2.129310e-25 1.000000e+00
[60,] 1.839772e-25 3.679545e-25 1.000000e+00
[61,] 6.316262e-25 1.263252e-24 1.000000e+00
[62,] 7.416401e-24 1.483280e-23 1.000000e+00
[63,] 1.416262e-22 2.832523e-22 1.000000e+00
[64,] 3.649770e-21 7.299540e-21 1.000000e+00
[65,] 2.098774e-19 4.197548e-19 1.000000e+00
[66,] 1.338260e-17 2.676521e-17 1.000000e+00
[67,] 5.238994e-16 1.047799e-15 1.000000e+00
[68,] 1.710055e-14 3.420109e-14 1.000000e+00
[69,] 6.987620e-13 1.397524e-12 1.000000e+00
[70,] 2.210463e-11 4.420926e-11 1.000000e+00
[71,] 6.083906e-10 1.216781e-09 1.000000e+00
[72,] 9.924441e-09 1.984888e-08 1.000000e+00
[73,] 1.104682e-07 2.209365e-07 9.999999e-01
[74,] 9.398801e-07 1.879760e-06 9.999991e-01
[75,] 6.595383e-06 1.319077e-05 9.999934e-01
[76,] 3.027712e-05 6.055424e-05 9.999697e-01
[77,] 1.279761e-04 2.559522e-04 9.998720e-01
[78,] 5.485866e-04 1.097173e-03 9.994514e-01
[79,] 2.487148e-03 4.974297e-03 9.975129e-01
[80,] 1.027943e-02 2.055887e-02 9.897206e-01
[81,] 3.793399e-02 7.586798e-02 9.620660e-01
[82,] 1.223227e-01 2.446454e-01 8.776773e-01
[83,] 2.571543e-01 5.143086e-01 7.428457e-01
[84,] 4.223682e-01 8.447364e-01 5.776318e-01
[85,] 5.764039e-01 8.471921e-01 4.235961e-01
[86,] 7.047508e-01 5.904984e-01 2.952492e-01
[87,] 8.070778e-01 3.858443e-01 1.929222e-01
[88,] 8.874887e-01 2.250226e-01 1.125113e-01
[89,] 9.440977e-01 1.118047e-01 5.590233e-02
[90,] 9.732372e-01 5.352557e-02 2.676279e-02
[91,] 9.871685e-01 2.566295e-02 1.283147e-02
[92,] 9.939677e-01 1.206450e-02 6.032251e-03
[93,] 9.971970e-01 5.606089e-03 2.803045e-03
[94,] 9.985578e-01 2.884421e-03 1.442210e-03
[95,] 9.992693e-01 1.461397e-03 7.306985e-04
[96,] 9.996655e-01 6.690365e-04 3.345182e-04
[97,] 9.998356e-01 3.288948e-04 1.644474e-04
[98,] 9.999236e-01 1.528789e-04 7.643944e-05
[99,] 9.999675e-01 6.502291e-05 3.251145e-05
[100,] 9.999866e-01 2.676388e-05 1.338194e-05
[101,] 9.999939e-01 1.227483e-05 6.137413e-06
[102,] 9.999973e-01 5.481749e-06 2.740874e-06
[103,] 9.999987e-01 2.580132e-06 1.290066e-06
[104,] 9.999994e-01 1.149993e-06 5.749967e-07
[105,] 9.999997e-01 6.066510e-07 3.033255e-07
[106,] 9.999998e-01 3.023296e-07 1.511648e-07
[107,] 9.999999e-01 1.546573e-07 7.732863e-08
[108,] 1.000000e+00 8.183508e-08 4.091754e-08
[109,] 1.000000e+00 4.577371e-08 2.288686e-08
[110,] 1.000000e+00 2.295967e-08 1.147983e-08
[111,] 1.000000e+00 1.193488e-08 5.967440e-09
[112,] 1.000000e+00 6.097295e-09 3.048648e-09
[113,] 1.000000e+00 3.086105e-09 1.543052e-09
[114,] 1.000000e+00 2.163917e-09 1.081958e-09
[115,] 1.000000e+00 1.709671e-09 8.548356e-10
[116,] 1.000000e+00 1.452355e-09 7.261775e-10
[117,] 1.000000e+00 1.533708e-09 7.668540e-10
[118,] 1.000000e+00 1.662952e-09 8.314760e-10
[119,] 1.000000e+00 2.057506e-09 1.028753e-09
[120,] 1.000000e+00 2.600156e-09 1.300078e-09
[121,] 1.000000e+00 3.465973e-09 1.732986e-09
[122,] 1.000000e+00 4.702161e-09 2.351081e-09
[123,] 1.000000e+00 6.361994e-09 3.180997e-09
[124,] 1.000000e+00 8.430641e-09 4.215321e-09
[125,] 1.000000e+00 1.120034e-08 5.600171e-09
[126,] 1.000000e+00 1.404422e-08 7.022111e-09
[127,] 1.000000e+00 1.621207e-08 8.106035e-09
[128,] 1.000000e+00 1.551892e-08 7.759460e-09
[129,] 1.000000e+00 1.388711e-08 6.943557e-09
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[131,] 1.000000e+00 1.397253e-08 6.986267e-09
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[134,] 1.000000e+00 1.615606e-08 8.078028e-09
[135,] 1.000000e+00 1.933083e-08 9.665417e-09
[136,] 1.000000e+00 2.425754e-08 1.212877e-08
[137,] 1.000000e+00 3.221634e-08 1.610817e-08
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[140,] 1.000000e+00 7.369346e-08 3.684673e-08
[141,] 1.000000e+00 9.871277e-08 4.935638e-08
[142,] 9.999999e-01 1.281050e-07 6.405250e-08
[143,] 9.999999e-01 1.704493e-07 8.522464e-08
[144,] 9.999999e-01 2.159827e-07 1.079914e-07
[145,] 9.999999e-01 2.615145e-07 1.307573e-07
[146,] 9.999998e-01 3.019483e-07 1.509742e-07
[147,] 9.999998e-01 3.451373e-07 1.725687e-07
[148,] 9.999998e-01 3.557242e-07 1.778621e-07
[149,] 9.999998e-01 3.521113e-07 1.760557e-07
[150,] 9.999998e-01 3.148120e-07 1.574060e-07
[151,] 9.999999e-01 2.629490e-07 1.314745e-07
[152,] 9.999999e-01 1.896832e-07 9.484159e-08
[153,] 9.999999e-01 1.269652e-07 6.348259e-08
[154,] 1.000000e+00 8.281370e-08 4.140685e-08
[155,] 1.000000e+00 5.756073e-08 2.878036e-08
[156,] 1.000000e+00 4.135766e-08 2.067883e-08
[157,] 1.000000e+00 2.761843e-08 1.380921e-08
[158,] 1.000000e+00 1.893322e-08 9.466611e-09
[159,] 1.000000e+00 1.321371e-08 6.606854e-09
[160,] 1.000000e+00 9.551266e-09 4.775633e-09
[161,] 1.000000e+00 6.443776e-09 3.221888e-09
[162,] 1.000000e+00 4.834554e-09 2.417277e-09
[163,] 1.000000e+00 3.922641e-09 1.961321e-09
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[166,] 1.000000e+00 1.995171e-09 9.975854e-10
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[169,] 1.000000e+00 6.587354e-10 3.293677e-10
[170,] 1.000000e+00 3.434091e-10 1.717046e-10
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[181,] 1.000000e+00 1.303716e-18 6.518582e-19
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[225,] 1.000000e+00 2.728758e-33 1.364379e-33
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[263,] 1.000000e+00 8.417833e-14 4.208917e-14
[264,] 1.000000e+00 3.981275e-13 1.990637e-13
[265,] 1.000000e+00 1.830908e-12 9.154538e-13
[266,] 1.000000e+00 9.905060e-12 4.952530e-12
[267,] 1.000000e+00 5.014552e-11 2.507276e-11
[268,] 1.000000e+00 2.402333e-10 1.201166e-10
[269,] 1.000000e+00 1.241383e-09 6.206915e-10
[270,] 1.000000e+00 6.289421e-09 3.144710e-09
[271,] 1.000000e+00 3.099103e-08 1.549551e-08
[272,] 9.999999e-01 1.501368e-07 7.506838e-08
[273,] 9.999996e-01 7.029714e-07 3.514857e-07
[274,] 9.999988e-01 2.418937e-06 1.209468e-06
[275,] 9.999976e-01 4.877413e-06 2.438707e-06
[276,] 9.999981e-01 3.744031e-06 1.872016e-06
[277,] 9.999996e-01 8.028971e-07 4.014485e-07
[278,] 9.999999e-01 1.649390e-07 8.246951e-08
[279,] 1.000000e+00 8.333278e-08 4.166639e-08
[280,] 9.999995e-01 9.992835e-07 4.996417e-07
[281,] 9.999934e-01 1.319901e-05 6.599504e-06
[282,] 9.999274e-01 1.452039e-04 7.260196e-05
[283,] 9.994627e-01 1.074603e-03 5.373016e-04
> postscript(file="/var/www/html/rcomp/tmp/1utsr1292972349.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2utsr1292972349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/352au1292972349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/452au1292972349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/552au1292972349.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 = 292
Frequency = 1
1 2 3 4 5 6
0.884984924 0.993550104 1.066141527 1.153506364 1.308323517 1.313462625
7 8 9 10 11 12
1.440871201 1.542584237 1.496332265 1.599758337 1.579201904 1.426097788
13 14 15 16 17 18
1.407254392 1.286697960 1.262715455 1.290124032 1.290124032 1.288410996
19 20 21 22 23 24
1.300402248 1.429523860 1.436376004 1.334662968 1.274062796 1.184341013
25 26 27 28 29 30
1.199758337 1.203184409 1.003184409 0.818601733 0.751149417 0.765922830
31 32 33 34 35 36
0.684766226 0.662496758 0.669348902 0.734444271 0.801252676 0.909817856
37 38 39 40 41 42
0.890974460 1.001252676 1.088617512 1.292468798 1.259921114 1.242790754
43 44 45 46 47 48
1.202747015 1.255851131 1.235294699 1.226729519 1.326729519 1.295894871
49 50 51 52 53 54
1.297607907 1.207886123 1.223303447 1.238720771 1.133581663 1.125016483
55 56 57 58 59 60
1.030155591 0.948998987 0.911312195 0.732512538 0.782190582 0.695894871
61 62 63 64 65 66
0.614738267 0.679833635 0.598677032 0.478120599 0.354138095 0.150712023
67 68 69 70 71 72
0.026729519 -0.092113877 -0.298966021 -0.473270481 -0.566418337 -0.681835661
73 74 75 76 77 78
-0.886974769 -1.009244238 -1.145217994 -1.229800670 -1.319522454 -1.417809418
79 80 81 82 83 84
-1.524661562 -1.519522454 -1.631513706 -1.774339606 -1.946287119 -2.097678199
85 86 87 88 89 90
-2.266199640 -2.468981801 -2.444999297 -2.500460360 -2.469625712 -2.457634460
91 92 93 94 95 96
-2.525086776 -2.602173396 -2.751851441 -2.738147153 -2.702173396 -2.691895180
97 98 99 100 101 102
-2.678190892 -2.571982659 -2.568556587 -2.658278371 -2.553139263 -2.573695695
103 104 105 106 107 108
-2.609669452 -2.599391235 -2.466843551 -2.451426227 -2.354852299 -2.351426227
109 110 111 112 113 114
-2.181191750 -2.181191750 -2.096609074 -2.005174254 -1.900035146 -1.915452471
115 116 117 118 119 120
-1.813739435 -1.734295867 -1.689756930 -1.405818166 -1.264705301 -1.149287977
121 122 123 124 125 126
-0.884192608 -0.797896896 -0.549931888 -0.461923140 -0.260210104 -0.125949384
127 128 129 130 131 132
0.104885265 0.299746156 0.375763652 0.580902760 0.727154733 0.983684921
133 134 135 136 137 138
1.095676173 1.064841525 1.089467940 1.163772400 1.220946500 1.145572915
139 140 141 142 143 144
1.050712023 1.006173087 0.942790754 0.965060222 0.882190582 0.890755762
145 146 147 148 149 150
0.865060222 0.895894871 0.814738267 0.867842383 0.912381320 0.956920256
151 152 153 154 155 156
0.956920256 1.080902760 1.106598301 1.203172229 1.247711165 1.364841525
157 158 159 160 161 162
1.409380462 1.375119741 1.240859021 1.169980633 1.166554561 1.051137237
163 164 165 166 167 168
0.951137237 0.878545813 0.907667426 0.867623686 0.784754046 0.801884406
169 170 171 172 173 174
0.803597442 0.744710307 0.710449587 0.681327974 0.701884406 0.813875659
175 176 177 178 179 180
0.946423343 0.927579947 0.937858163 0.937858163 0.939571199 1.078971027
181 182 183 184 185 186
1.118370856 1.137214252 1.250918540 1.250918540 1.140640324 1.162909792
187 188 189 190 191 192
1.076614080 1.214300873 1.269118025 1.079396241 0.970831061 1.011943925
193 194 195 196 197 198
1.044491610 0.920509106 0.882822313 0.808517853 0.677683205 0.517726945
199 200 201 202 203 204
0.266335864 0.025223000 -0.202185577 -0.443298441 -0.495333433 -0.714176829
205 206 207 208 209 210
-0.979272197 -1.179272197 -1.350150585 -1.333020225 -1.562141837 -1.534733261
211 212 213 214 215 216
-1.646724513 -1.539872369 -1.651863621 -1.643298441 -1.633020225 -1.791907360
217 218 219 220 221 222
-1.971350928 -1.971350928 -1.957646640 -1.779916108 -1.709037721 -1.800472541
223 224 225 226 227 228
-2.003898613 -1.969637892 -1.767924856 -1.225098956 -1.101116452 -0.938803244
229 230 231 232 233 234
-0.957646640 -0.869637892 -0.907324685 -1.007324685 -0.909037721 -0.785055216
235 236 237 238 239 240
-0.559359676 -0.509681632 -0.475420911 -0.485699127 -0.482273055 -0.351438407
241 242 243 244 245 246
-0.120603759 0.008517853 0.001665709 0.077683205 0.135926430 0.171900186
247 248 249 250 251 252
0.017083034 -0.010325543 0.084535349 0.174257133 0.153700701 0.262265881
253 254 255 256 257 258
0.382822313 0.382822313 0.315369998 -0.117177687 -0.241160191 -0.051438407
259 260 261 262 263 264
0.369118025 0.696526601 0.708517853 0.530787322 0.253056790 0.285604474
265 266 267 268 269 270
0.297595726 0.401021798 0.368474114 0.495882690 0.625004303 0.654125915
271 272 273 274 275 276
0.647273771 0.645560735 0.674682347 0.547273771 0.414726087 0.431856447
277 278 279 280 281 282
0.435282519 0.506160906 0.478752330 0.534213394 0.522222142 0.323935178
283 284 285 286 287 288
0.120509106 -0.058934462 -0.036664994 -0.043517138 -0.031525886 -0.141804102
289 290 291 292
-0.182916966 -0.291482147 -0.274351786 -0.110325543
> postscript(file="/var/www/html/rcomp/tmp/6gurf1292972349.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 = 292
Frequency = 1
lag(myerror, k = 1) myerror
0 0.884984924 NA
1 0.993550104 0.884984924
2 1.066141527 0.993550104
3 1.153506364 1.066141527
4 1.308323517 1.153506364
5 1.313462625 1.308323517
6 1.440871201 1.313462625
7 1.542584237 1.440871201
8 1.496332265 1.542584237
9 1.599758337 1.496332265
10 1.579201904 1.599758337
11 1.426097788 1.579201904
12 1.407254392 1.426097788
13 1.286697960 1.407254392
14 1.262715455 1.286697960
15 1.290124032 1.262715455
16 1.290124032 1.290124032
17 1.288410996 1.290124032
18 1.300402248 1.288410996
19 1.429523860 1.300402248
20 1.436376004 1.429523860
21 1.334662968 1.436376004
22 1.274062796 1.334662968
23 1.184341013 1.274062796
24 1.199758337 1.184341013
25 1.203184409 1.199758337
26 1.003184409 1.203184409
27 0.818601733 1.003184409
28 0.751149417 0.818601733
29 0.765922830 0.751149417
30 0.684766226 0.765922830
31 0.662496758 0.684766226
32 0.669348902 0.662496758
33 0.734444271 0.669348902
34 0.801252676 0.734444271
35 0.909817856 0.801252676
36 0.890974460 0.909817856
37 1.001252676 0.890974460
38 1.088617512 1.001252676
39 1.292468798 1.088617512
40 1.259921114 1.292468798
41 1.242790754 1.259921114
42 1.202747015 1.242790754
43 1.255851131 1.202747015
44 1.235294699 1.255851131
45 1.226729519 1.235294699
46 1.326729519 1.226729519
47 1.295894871 1.326729519
48 1.297607907 1.295894871
49 1.207886123 1.297607907
50 1.223303447 1.207886123
51 1.238720771 1.223303447
52 1.133581663 1.238720771
53 1.125016483 1.133581663
54 1.030155591 1.125016483
55 0.948998987 1.030155591
56 0.911312195 0.948998987
57 0.732512538 0.911312195
58 0.782190582 0.732512538
59 0.695894871 0.782190582
60 0.614738267 0.695894871
61 0.679833635 0.614738267
62 0.598677032 0.679833635
63 0.478120599 0.598677032
64 0.354138095 0.478120599
65 0.150712023 0.354138095
66 0.026729519 0.150712023
67 -0.092113877 0.026729519
68 -0.298966021 -0.092113877
69 -0.473270481 -0.298966021
70 -0.566418337 -0.473270481
71 -0.681835661 -0.566418337
72 -0.886974769 -0.681835661
73 -1.009244238 -0.886974769
74 -1.145217994 -1.009244238
75 -1.229800670 -1.145217994
76 -1.319522454 -1.229800670
77 -1.417809418 -1.319522454
78 -1.524661562 -1.417809418
79 -1.519522454 -1.524661562
80 -1.631513706 -1.519522454
81 -1.774339606 -1.631513706
82 -1.946287119 -1.774339606
83 -2.097678199 -1.946287119
84 -2.266199640 -2.097678199
85 -2.468981801 -2.266199640
86 -2.444999297 -2.468981801
87 -2.500460360 -2.444999297
88 -2.469625712 -2.500460360
89 -2.457634460 -2.469625712
90 -2.525086776 -2.457634460
91 -2.602173396 -2.525086776
92 -2.751851441 -2.602173396
93 -2.738147153 -2.751851441
94 -2.702173396 -2.738147153
95 -2.691895180 -2.702173396
96 -2.678190892 -2.691895180
97 -2.571982659 -2.678190892
98 -2.568556587 -2.571982659
99 -2.658278371 -2.568556587
100 -2.553139263 -2.658278371
101 -2.573695695 -2.553139263
102 -2.609669452 -2.573695695
103 -2.599391235 -2.609669452
104 -2.466843551 -2.599391235
105 -2.451426227 -2.466843551
106 -2.354852299 -2.451426227
107 -2.351426227 -2.354852299
108 -2.181191750 -2.351426227
109 -2.181191750 -2.181191750
110 -2.096609074 -2.181191750
111 -2.005174254 -2.096609074
112 -1.900035146 -2.005174254
113 -1.915452471 -1.900035146
114 -1.813739435 -1.915452471
115 -1.734295867 -1.813739435
116 -1.689756930 -1.734295867
117 -1.405818166 -1.689756930
118 -1.264705301 -1.405818166
119 -1.149287977 -1.264705301
120 -0.884192608 -1.149287977
121 -0.797896896 -0.884192608
122 -0.549931888 -0.797896896
123 -0.461923140 -0.549931888
124 -0.260210104 -0.461923140
125 -0.125949384 -0.260210104
126 0.104885265 -0.125949384
127 0.299746156 0.104885265
128 0.375763652 0.299746156
129 0.580902760 0.375763652
130 0.727154733 0.580902760
131 0.983684921 0.727154733
132 1.095676173 0.983684921
133 1.064841525 1.095676173
134 1.089467940 1.064841525
135 1.163772400 1.089467940
136 1.220946500 1.163772400
137 1.145572915 1.220946500
138 1.050712023 1.145572915
139 1.006173087 1.050712023
140 0.942790754 1.006173087
141 0.965060222 0.942790754
142 0.882190582 0.965060222
143 0.890755762 0.882190582
144 0.865060222 0.890755762
145 0.895894871 0.865060222
146 0.814738267 0.895894871
147 0.867842383 0.814738267
148 0.912381320 0.867842383
149 0.956920256 0.912381320
150 0.956920256 0.956920256
151 1.080902760 0.956920256
152 1.106598301 1.080902760
153 1.203172229 1.106598301
154 1.247711165 1.203172229
155 1.364841525 1.247711165
156 1.409380462 1.364841525
157 1.375119741 1.409380462
158 1.240859021 1.375119741
159 1.169980633 1.240859021
160 1.166554561 1.169980633
161 1.051137237 1.166554561
162 0.951137237 1.051137237
163 0.878545813 0.951137237
164 0.907667426 0.878545813
165 0.867623686 0.907667426
166 0.784754046 0.867623686
167 0.801884406 0.784754046
168 0.803597442 0.801884406
169 0.744710307 0.803597442
170 0.710449587 0.744710307
171 0.681327974 0.710449587
172 0.701884406 0.681327974
173 0.813875659 0.701884406
174 0.946423343 0.813875659
175 0.927579947 0.946423343
176 0.937858163 0.927579947
177 0.937858163 0.937858163
178 0.939571199 0.937858163
179 1.078971027 0.939571199
180 1.118370856 1.078971027
181 1.137214252 1.118370856
182 1.250918540 1.137214252
183 1.250918540 1.250918540
184 1.140640324 1.250918540
185 1.162909792 1.140640324
186 1.076614080 1.162909792
187 1.214300873 1.076614080
188 1.269118025 1.214300873
189 1.079396241 1.269118025
190 0.970831061 1.079396241
191 1.011943925 0.970831061
192 1.044491610 1.011943925
193 0.920509106 1.044491610
194 0.882822313 0.920509106
195 0.808517853 0.882822313
196 0.677683205 0.808517853
197 0.517726945 0.677683205
198 0.266335864 0.517726945
199 0.025223000 0.266335864
200 -0.202185577 0.025223000
201 -0.443298441 -0.202185577
202 -0.495333433 -0.443298441
203 -0.714176829 -0.495333433
204 -0.979272197 -0.714176829
205 -1.179272197 -0.979272197
206 -1.350150585 -1.179272197
207 -1.333020225 -1.350150585
208 -1.562141837 -1.333020225
209 -1.534733261 -1.562141837
210 -1.646724513 -1.534733261
211 -1.539872369 -1.646724513
212 -1.651863621 -1.539872369
213 -1.643298441 -1.651863621
214 -1.633020225 -1.643298441
215 -1.791907360 -1.633020225
216 -1.971350928 -1.791907360
217 -1.971350928 -1.971350928
218 -1.957646640 -1.971350928
219 -1.779916108 -1.957646640
220 -1.709037721 -1.779916108
221 -1.800472541 -1.709037721
222 -2.003898613 -1.800472541
223 -1.969637892 -2.003898613
224 -1.767924856 -1.969637892
225 -1.225098956 -1.767924856
226 -1.101116452 -1.225098956
227 -0.938803244 -1.101116452
228 -0.957646640 -0.938803244
229 -0.869637892 -0.957646640
230 -0.907324685 -0.869637892
231 -1.007324685 -0.907324685
232 -0.909037721 -1.007324685
233 -0.785055216 -0.909037721
234 -0.559359676 -0.785055216
235 -0.509681632 -0.559359676
236 -0.475420911 -0.509681632
237 -0.485699127 -0.475420911
238 -0.482273055 -0.485699127
239 -0.351438407 -0.482273055
240 -0.120603759 -0.351438407
241 0.008517853 -0.120603759
242 0.001665709 0.008517853
243 0.077683205 0.001665709
244 0.135926430 0.077683205
245 0.171900186 0.135926430
246 0.017083034 0.171900186
247 -0.010325543 0.017083034
248 0.084535349 -0.010325543
249 0.174257133 0.084535349
250 0.153700701 0.174257133
251 0.262265881 0.153700701
252 0.382822313 0.262265881
253 0.382822313 0.382822313
254 0.315369998 0.382822313
255 -0.117177687 0.315369998
256 -0.241160191 -0.117177687
257 -0.051438407 -0.241160191
258 0.369118025 -0.051438407
259 0.696526601 0.369118025
260 0.708517853 0.696526601
261 0.530787322 0.708517853
262 0.253056790 0.530787322
263 0.285604474 0.253056790
264 0.297595726 0.285604474
265 0.401021798 0.297595726
266 0.368474114 0.401021798
267 0.495882690 0.368474114
268 0.625004303 0.495882690
269 0.654125915 0.625004303
270 0.647273771 0.654125915
271 0.645560735 0.647273771
272 0.674682347 0.645560735
273 0.547273771 0.674682347
274 0.414726087 0.547273771
275 0.431856447 0.414726087
276 0.435282519 0.431856447
277 0.506160906 0.435282519
278 0.478752330 0.506160906
279 0.534213394 0.478752330
280 0.522222142 0.534213394
281 0.323935178 0.522222142
282 0.120509106 0.323935178
283 -0.058934462 0.120509106
284 -0.036664994 -0.058934462
285 -0.043517138 -0.036664994
286 -0.031525886 -0.043517138
287 -0.141804102 -0.031525886
288 -0.182916966 -0.141804102
289 -0.291482147 -0.182916966
290 -0.274351786 -0.291482147
291 -0.110325543 -0.274351786
292 NA -0.110325543
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.993550104 0.884984924
[2,] 1.066141527 0.993550104
[3,] 1.153506364 1.066141527
[4,] 1.308323517 1.153506364
[5,] 1.313462625 1.308323517
[6,] 1.440871201 1.313462625
[7,] 1.542584237 1.440871201
[8,] 1.496332265 1.542584237
[9,] 1.599758337 1.496332265
[10,] 1.579201904 1.599758337
[11,] 1.426097788 1.579201904
[12,] 1.407254392 1.426097788
[13,] 1.286697960 1.407254392
[14,] 1.262715455 1.286697960
[15,] 1.290124032 1.262715455
[16,] 1.290124032 1.290124032
[17,] 1.288410996 1.290124032
[18,] 1.300402248 1.288410996
[19,] 1.429523860 1.300402248
[20,] 1.436376004 1.429523860
[21,] 1.334662968 1.436376004
[22,] 1.274062796 1.334662968
[23,] 1.184341013 1.274062796
[24,] 1.199758337 1.184341013
[25,] 1.203184409 1.199758337
[26,] 1.003184409 1.203184409
[27,] 0.818601733 1.003184409
[28,] 0.751149417 0.818601733
[29,] 0.765922830 0.751149417
[30,] 0.684766226 0.765922830
[31,] 0.662496758 0.684766226
[32,] 0.669348902 0.662496758
[33,] 0.734444271 0.669348902
[34,] 0.801252676 0.734444271
[35,] 0.909817856 0.801252676
[36,] 0.890974460 0.909817856
[37,] 1.001252676 0.890974460
[38,] 1.088617512 1.001252676
[39,] 1.292468798 1.088617512
[40,] 1.259921114 1.292468798
[41,] 1.242790754 1.259921114
[42,] 1.202747015 1.242790754
[43,] 1.255851131 1.202747015
[44,] 1.235294699 1.255851131
[45,] 1.226729519 1.235294699
[46,] 1.326729519 1.226729519
[47,] 1.295894871 1.326729519
[48,] 1.297607907 1.295894871
[49,] 1.207886123 1.297607907
[50,] 1.223303447 1.207886123
[51,] 1.238720771 1.223303447
[52,] 1.133581663 1.238720771
[53,] 1.125016483 1.133581663
[54,] 1.030155591 1.125016483
[55,] 0.948998987 1.030155591
[56,] 0.911312195 0.948998987
[57,] 0.732512538 0.911312195
[58,] 0.782190582 0.732512538
[59,] 0.695894871 0.782190582
[60,] 0.614738267 0.695894871
[61,] 0.679833635 0.614738267
[62,] 0.598677032 0.679833635
[63,] 0.478120599 0.598677032
[64,] 0.354138095 0.478120599
[65,] 0.150712023 0.354138095
[66,] 0.026729519 0.150712023
[67,] -0.092113877 0.026729519
[68,] -0.298966021 -0.092113877
[69,] -0.473270481 -0.298966021
[70,] -0.566418337 -0.473270481
[71,] -0.681835661 -0.566418337
[72,] -0.886974769 -0.681835661
[73,] -1.009244238 -0.886974769
[74,] -1.145217994 -1.009244238
[75,] -1.229800670 -1.145217994
[76,] -1.319522454 -1.229800670
[77,] -1.417809418 -1.319522454
[78,] -1.524661562 -1.417809418
[79,] -1.519522454 -1.524661562
[80,] -1.631513706 -1.519522454
[81,] -1.774339606 -1.631513706
[82,] -1.946287119 -1.774339606
[83,] -2.097678199 -1.946287119
[84,] -2.266199640 -2.097678199
[85,] -2.468981801 -2.266199640
[86,] -2.444999297 -2.468981801
[87,] -2.500460360 -2.444999297
[88,] -2.469625712 -2.500460360
[89,] -2.457634460 -2.469625712
[90,] -2.525086776 -2.457634460
[91,] -2.602173396 -2.525086776
[92,] -2.751851441 -2.602173396
[93,] -2.738147153 -2.751851441
[94,] -2.702173396 -2.738147153
[95,] -2.691895180 -2.702173396
[96,] -2.678190892 -2.691895180
[97,] -2.571982659 -2.678190892
[98,] -2.568556587 -2.571982659
[99,] -2.658278371 -2.568556587
[100,] -2.553139263 -2.658278371
[101,] -2.573695695 -2.553139263
[102,] -2.609669452 -2.573695695
[103,] -2.599391235 -2.609669452
[104,] -2.466843551 -2.599391235
[105,] -2.451426227 -2.466843551
[106,] -2.354852299 -2.451426227
[107,] -2.351426227 -2.354852299
[108,] -2.181191750 -2.351426227
[109,] -2.181191750 -2.181191750
[110,] -2.096609074 -2.181191750
[111,] -2.005174254 -2.096609074
[112,] -1.900035146 -2.005174254
[113,] -1.915452471 -1.900035146
[114,] -1.813739435 -1.915452471
[115,] -1.734295867 -1.813739435
[116,] -1.689756930 -1.734295867
[117,] -1.405818166 -1.689756930
[118,] -1.264705301 -1.405818166
[119,] -1.149287977 -1.264705301
[120,] -0.884192608 -1.149287977
[121,] -0.797896896 -0.884192608
[122,] -0.549931888 -0.797896896
[123,] -0.461923140 -0.549931888
[124,] -0.260210104 -0.461923140
[125,] -0.125949384 -0.260210104
[126,] 0.104885265 -0.125949384
[127,] 0.299746156 0.104885265
[128,] 0.375763652 0.299746156
[129,] 0.580902760 0.375763652
[130,] 0.727154733 0.580902760
[131,] 0.983684921 0.727154733
[132,] 1.095676173 0.983684921
[133,] 1.064841525 1.095676173
[134,] 1.089467940 1.064841525
[135,] 1.163772400 1.089467940
[136,] 1.220946500 1.163772400
[137,] 1.145572915 1.220946500
[138,] 1.050712023 1.145572915
[139,] 1.006173087 1.050712023
[140,] 0.942790754 1.006173087
[141,] 0.965060222 0.942790754
[142,] 0.882190582 0.965060222
[143,] 0.890755762 0.882190582
[144,] 0.865060222 0.890755762
[145,] 0.895894871 0.865060222
[146,] 0.814738267 0.895894871
[147,] 0.867842383 0.814738267
[148,] 0.912381320 0.867842383
[149,] 0.956920256 0.912381320
[150,] 0.956920256 0.956920256
[151,] 1.080902760 0.956920256
[152,] 1.106598301 1.080902760
[153,] 1.203172229 1.106598301
[154,] 1.247711165 1.203172229
[155,] 1.364841525 1.247711165
[156,] 1.409380462 1.364841525
[157,] 1.375119741 1.409380462
[158,] 1.240859021 1.375119741
[159,] 1.169980633 1.240859021
[160,] 1.166554561 1.169980633
[161,] 1.051137237 1.166554561
[162,] 0.951137237 1.051137237
[163,] 0.878545813 0.951137237
[164,] 0.907667426 0.878545813
[165,] 0.867623686 0.907667426
[166,] 0.784754046 0.867623686
[167,] 0.801884406 0.784754046
[168,] 0.803597442 0.801884406
[169,] 0.744710307 0.803597442
[170,] 0.710449587 0.744710307
[171,] 0.681327974 0.710449587
[172,] 0.701884406 0.681327974
[173,] 0.813875659 0.701884406
[174,] 0.946423343 0.813875659
[175,] 0.927579947 0.946423343
[176,] 0.937858163 0.927579947
[177,] 0.937858163 0.937858163
[178,] 0.939571199 0.937858163
[179,] 1.078971027 0.939571199
[180,] 1.118370856 1.078971027
[181,] 1.137214252 1.118370856
[182,] 1.250918540 1.137214252
[183,] 1.250918540 1.250918540
[184,] 1.140640324 1.250918540
[185,] 1.162909792 1.140640324
[186,] 1.076614080 1.162909792
[187,] 1.214300873 1.076614080
[188,] 1.269118025 1.214300873
[189,] 1.079396241 1.269118025
[190,] 0.970831061 1.079396241
[191,] 1.011943925 0.970831061
[192,] 1.044491610 1.011943925
[193,] 0.920509106 1.044491610
[194,] 0.882822313 0.920509106
[195,] 0.808517853 0.882822313
[196,] 0.677683205 0.808517853
[197,] 0.517726945 0.677683205
[198,] 0.266335864 0.517726945
[199,] 0.025223000 0.266335864
[200,] -0.202185577 0.025223000
[201,] -0.443298441 -0.202185577
[202,] -0.495333433 -0.443298441
[203,] -0.714176829 -0.495333433
[204,] -0.979272197 -0.714176829
[205,] -1.179272197 -0.979272197
[206,] -1.350150585 -1.179272197
[207,] -1.333020225 -1.350150585
[208,] -1.562141837 -1.333020225
[209,] -1.534733261 -1.562141837
[210,] -1.646724513 -1.534733261
[211,] -1.539872369 -1.646724513
[212,] -1.651863621 -1.539872369
[213,] -1.643298441 -1.651863621
[214,] -1.633020225 -1.643298441
[215,] -1.791907360 -1.633020225
[216,] -1.971350928 -1.791907360
[217,] -1.971350928 -1.971350928
[218,] -1.957646640 -1.971350928
[219,] -1.779916108 -1.957646640
[220,] -1.709037721 -1.779916108
[221,] -1.800472541 -1.709037721
[222,] -2.003898613 -1.800472541
[223,] -1.969637892 -2.003898613
[224,] -1.767924856 -1.969637892
[225,] -1.225098956 -1.767924856
[226,] -1.101116452 -1.225098956
[227,] -0.938803244 -1.101116452
[228,] -0.957646640 -0.938803244
[229,] -0.869637892 -0.957646640
[230,] -0.907324685 -0.869637892
[231,] -1.007324685 -0.907324685
[232,] -0.909037721 -1.007324685
[233,] -0.785055216 -0.909037721
[234,] -0.559359676 -0.785055216
[235,] -0.509681632 -0.559359676
[236,] -0.475420911 -0.509681632
[237,] -0.485699127 -0.475420911
[238,] -0.482273055 -0.485699127
[239,] -0.351438407 -0.482273055
[240,] -0.120603759 -0.351438407
[241,] 0.008517853 -0.120603759
[242,] 0.001665709 0.008517853
[243,] 0.077683205 0.001665709
[244,] 0.135926430 0.077683205
[245,] 0.171900186 0.135926430
[246,] 0.017083034 0.171900186
[247,] -0.010325543 0.017083034
[248,] 0.084535349 -0.010325543
[249,] 0.174257133 0.084535349
[250,] 0.153700701 0.174257133
[251,] 0.262265881 0.153700701
[252,] 0.382822313 0.262265881
[253,] 0.382822313 0.382822313
[254,] 0.315369998 0.382822313
[255,] -0.117177687 0.315369998
[256,] -0.241160191 -0.117177687
[257,] -0.051438407 -0.241160191
[258,] 0.369118025 -0.051438407
[259,] 0.696526601 0.369118025
[260,] 0.708517853 0.696526601
[261,] 0.530787322 0.708517853
[262,] 0.253056790 0.530787322
[263,] 0.285604474 0.253056790
[264,] 0.297595726 0.285604474
[265,] 0.401021798 0.297595726
[266,] 0.368474114 0.401021798
[267,] 0.495882690 0.368474114
[268,] 0.625004303 0.495882690
[269,] 0.654125915 0.625004303
[270,] 0.647273771 0.654125915
[271,] 0.645560735 0.647273771
[272,] 0.674682347 0.645560735
[273,] 0.547273771 0.674682347
[274,] 0.414726087 0.547273771
[275,] 0.431856447 0.414726087
[276,] 0.435282519 0.431856447
[277,] 0.506160906 0.435282519
[278,] 0.478752330 0.506160906
[279,] 0.534213394 0.478752330
[280,] 0.522222142 0.534213394
[281,] 0.323935178 0.522222142
[282,] 0.120509106 0.323935178
[283,] -0.058934462 0.120509106
[284,] -0.036664994 -0.058934462
[285,] -0.043517138 -0.036664994
[286,] -0.031525886 -0.043517138
[287,] -0.141804102 -0.031525886
[288,] -0.182916966 -0.141804102
[289,] -0.291482147 -0.182916966
[290,] -0.274351786 -0.291482147
[291,] -0.110325543 -0.274351786
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.993550104 0.884984924
2 1.066141527 0.993550104
3 1.153506364 1.066141527
4 1.308323517 1.153506364
5 1.313462625 1.308323517
6 1.440871201 1.313462625
7 1.542584237 1.440871201
8 1.496332265 1.542584237
9 1.599758337 1.496332265
10 1.579201904 1.599758337
11 1.426097788 1.579201904
12 1.407254392 1.426097788
13 1.286697960 1.407254392
14 1.262715455 1.286697960
15 1.290124032 1.262715455
16 1.290124032 1.290124032
17 1.288410996 1.290124032
18 1.300402248 1.288410996
19 1.429523860 1.300402248
20 1.436376004 1.429523860
21 1.334662968 1.436376004
22 1.274062796 1.334662968
23 1.184341013 1.274062796
24 1.199758337 1.184341013
25 1.203184409 1.199758337
26 1.003184409 1.203184409
27 0.818601733 1.003184409
28 0.751149417 0.818601733
29 0.765922830 0.751149417
30 0.684766226 0.765922830
31 0.662496758 0.684766226
32 0.669348902 0.662496758
33 0.734444271 0.669348902
34 0.801252676 0.734444271
35 0.909817856 0.801252676
36 0.890974460 0.909817856
37 1.001252676 0.890974460
38 1.088617512 1.001252676
39 1.292468798 1.088617512
40 1.259921114 1.292468798
41 1.242790754 1.259921114
42 1.202747015 1.242790754
43 1.255851131 1.202747015
44 1.235294699 1.255851131
45 1.226729519 1.235294699
46 1.326729519 1.226729519
47 1.295894871 1.326729519
48 1.297607907 1.295894871
49 1.207886123 1.297607907
50 1.223303447 1.207886123
51 1.238720771 1.223303447
52 1.133581663 1.238720771
53 1.125016483 1.133581663
54 1.030155591 1.125016483
55 0.948998987 1.030155591
56 0.911312195 0.948998987
57 0.732512538 0.911312195
58 0.782190582 0.732512538
59 0.695894871 0.782190582
60 0.614738267 0.695894871
61 0.679833635 0.614738267
62 0.598677032 0.679833635
63 0.478120599 0.598677032
64 0.354138095 0.478120599
65 0.150712023 0.354138095
66 0.026729519 0.150712023
67 -0.092113877 0.026729519
68 -0.298966021 -0.092113877
69 -0.473270481 -0.298966021
70 -0.566418337 -0.473270481
71 -0.681835661 -0.566418337
72 -0.886974769 -0.681835661
73 -1.009244238 -0.886974769
74 -1.145217994 -1.009244238
75 -1.229800670 -1.145217994
76 -1.319522454 -1.229800670
77 -1.417809418 -1.319522454
78 -1.524661562 -1.417809418
79 -1.519522454 -1.524661562
80 -1.631513706 -1.519522454
81 -1.774339606 -1.631513706
82 -1.946287119 -1.774339606
83 -2.097678199 -1.946287119
84 -2.266199640 -2.097678199
85 -2.468981801 -2.266199640
86 -2.444999297 -2.468981801
87 -2.500460360 -2.444999297
88 -2.469625712 -2.500460360
89 -2.457634460 -2.469625712
90 -2.525086776 -2.457634460
91 -2.602173396 -2.525086776
92 -2.751851441 -2.602173396
93 -2.738147153 -2.751851441
94 -2.702173396 -2.738147153
95 -2.691895180 -2.702173396
96 -2.678190892 -2.691895180
97 -2.571982659 -2.678190892
98 -2.568556587 -2.571982659
99 -2.658278371 -2.568556587
100 -2.553139263 -2.658278371
101 -2.573695695 -2.553139263
102 -2.609669452 -2.573695695
103 -2.599391235 -2.609669452
104 -2.466843551 -2.599391235
105 -2.451426227 -2.466843551
106 -2.354852299 -2.451426227
107 -2.351426227 -2.354852299
108 -2.181191750 -2.351426227
109 -2.181191750 -2.181191750
110 -2.096609074 -2.181191750
111 -2.005174254 -2.096609074
112 -1.900035146 -2.005174254
113 -1.915452471 -1.900035146
114 -1.813739435 -1.915452471
115 -1.734295867 -1.813739435
116 -1.689756930 -1.734295867
117 -1.405818166 -1.689756930
118 -1.264705301 -1.405818166
119 -1.149287977 -1.264705301
120 -0.884192608 -1.149287977
121 -0.797896896 -0.884192608
122 -0.549931888 -0.797896896
123 -0.461923140 -0.549931888
124 -0.260210104 -0.461923140
125 -0.125949384 -0.260210104
126 0.104885265 -0.125949384
127 0.299746156 0.104885265
128 0.375763652 0.299746156
129 0.580902760 0.375763652
130 0.727154733 0.580902760
131 0.983684921 0.727154733
132 1.095676173 0.983684921
133 1.064841525 1.095676173
134 1.089467940 1.064841525
135 1.163772400 1.089467940
136 1.220946500 1.163772400
137 1.145572915 1.220946500
138 1.050712023 1.145572915
139 1.006173087 1.050712023
140 0.942790754 1.006173087
141 0.965060222 0.942790754
142 0.882190582 0.965060222
143 0.890755762 0.882190582
144 0.865060222 0.890755762
145 0.895894871 0.865060222
146 0.814738267 0.895894871
147 0.867842383 0.814738267
148 0.912381320 0.867842383
149 0.956920256 0.912381320
150 0.956920256 0.956920256
151 1.080902760 0.956920256
152 1.106598301 1.080902760
153 1.203172229 1.106598301
154 1.247711165 1.203172229
155 1.364841525 1.247711165
156 1.409380462 1.364841525
157 1.375119741 1.409380462
158 1.240859021 1.375119741
159 1.169980633 1.240859021
160 1.166554561 1.169980633
161 1.051137237 1.166554561
162 0.951137237 1.051137237
163 0.878545813 0.951137237
164 0.907667426 0.878545813
165 0.867623686 0.907667426
166 0.784754046 0.867623686
167 0.801884406 0.784754046
168 0.803597442 0.801884406
169 0.744710307 0.803597442
170 0.710449587 0.744710307
171 0.681327974 0.710449587
172 0.701884406 0.681327974
173 0.813875659 0.701884406
174 0.946423343 0.813875659
175 0.927579947 0.946423343
176 0.937858163 0.927579947
177 0.937858163 0.937858163
178 0.939571199 0.937858163
179 1.078971027 0.939571199
180 1.118370856 1.078971027
181 1.137214252 1.118370856
182 1.250918540 1.137214252
183 1.250918540 1.250918540
184 1.140640324 1.250918540
185 1.162909792 1.140640324
186 1.076614080 1.162909792
187 1.214300873 1.076614080
188 1.269118025 1.214300873
189 1.079396241 1.269118025
190 0.970831061 1.079396241
191 1.011943925 0.970831061
192 1.044491610 1.011943925
193 0.920509106 1.044491610
194 0.882822313 0.920509106
195 0.808517853 0.882822313
196 0.677683205 0.808517853
197 0.517726945 0.677683205
198 0.266335864 0.517726945
199 0.025223000 0.266335864
200 -0.202185577 0.025223000
201 -0.443298441 -0.202185577
202 -0.495333433 -0.443298441
203 -0.714176829 -0.495333433
204 -0.979272197 -0.714176829
205 -1.179272197 -0.979272197
206 -1.350150585 -1.179272197
207 -1.333020225 -1.350150585
208 -1.562141837 -1.333020225
209 -1.534733261 -1.562141837
210 -1.646724513 -1.534733261
211 -1.539872369 -1.646724513
212 -1.651863621 -1.539872369
213 -1.643298441 -1.651863621
214 -1.633020225 -1.643298441
215 -1.791907360 -1.633020225
216 -1.971350928 -1.791907360
217 -1.971350928 -1.971350928
218 -1.957646640 -1.971350928
219 -1.779916108 -1.957646640
220 -1.709037721 -1.779916108
221 -1.800472541 -1.709037721
222 -2.003898613 -1.800472541
223 -1.969637892 -2.003898613
224 -1.767924856 -1.969637892
225 -1.225098956 -1.767924856
226 -1.101116452 -1.225098956
227 -0.938803244 -1.101116452
228 -0.957646640 -0.938803244
229 -0.869637892 -0.957646640
230 -0.907324685 -0.869637892
231 -1.007324685 -0.907324685
232 -0.909037721 -1.007324685
233 -0.785055216 -0.909037721
234 -0.559359676 -0.785055216
235 -0.509681632 -0.559359676
236 -0.475420911 -0.509681632
237 -0.485699127 -0.475420911
238 -0.482273055 -0.485699127
239 -0.351438407 -0.482273055
240 -0.120603759 -0.351438407
241 0.008517853 -0.120603759
242 0.001665709 0.008517853
243 0.077683205 0.001665709
244 0.135926430 0.077683205
245 0.171900186 0.135926430
246 0.017083034 0.171900186
247 -0.010325543 0.017083034
248 0.084535349 -0.010325543
249 0.174257133 0.084535349
250 0.153700701 0.174257133
251 0.262265881 0.153700701
252 0.382822313 0.262265881
253 0.382822313 0.382822313
254 0.315369998 0.382822313
255 -0.117177687 0.315369998
256 -0.241160191 -0.117177687
257 -0.051438407 -0.241160191
258 0.369118025 -0.051438407
259 0.696526601 0.369118025
260 0.708517853 0.696526601
261 0.530787322 0.708517853
262 0.253056790 0.530787322
263 0.285604474 0.253056790
264 0.297595726 0.285604474
265 0.401021798 0.297595726
266 0.368474114 0.401021798
267 0.495882690 0.368474114
268 0.625004303 0.495882690
269 0.654125915 0.625004303
270 0.647273771 0.654125915
271 0.645560735 0.647273771
272 0.674682347 0.645560735
273 0.547273771 0.674682347
274 0.414726087 0.547273771
275 0.431856447 0.414726087
276 0.435282519 0.431856447
277 0.506160906 0.435282519
278 0.478752330 0.506160906
279 0.534213394 0.478752330
280 0.522222142 0.534213394
281 0.323935178 0.522222142
282 0.120509106 0.323935178
283 -0.058934462 0.120509106
284 -0.036664994 -0.058934462
285 -0.043517138 -0.036664994
286 -0.031525886 -0.043517138
287 -0.141804102 -0.031525886
288 -0.182916966 -0.141804102
289 -0.291482147 -0.182916966
290 -0.274351786 -0.291482147
291 -0.110325543 -0.274351786
> 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/7gurf1292972349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qkq01292972349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qkq01292972349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101u731292972349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mu6q1292972349.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/12qd4e1292972349.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/13m5kn1292972349.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/14p5jb1292972349.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/15tozh1292972349.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/16woy51292972349.tab")
+ }
>
> try(system("convert tmp/1utsr1292972349.ps tmp/1utsr1292972349.png",intern=TRUE))
character(0)
> try(system("convert tmp/2utsr1292972349.ps tmp/2utsr1292972349.png",intern=TRUE))
character(0)
> try(system("convert tmp/352au1292972349.ps tmp/352au1292972349.png",intern=TRUE))
character(0)
> try(system("convert tmp/452au1292972349.ps tmp/452au1292972349.png",intern=TRUE))
character(0)
> try(system("convert tmp/552au1292972349.ps tmp/552au1292972349.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gurf1292972349.ps tmp/6gurf1292972349.png",intern=TRUE))
character(0)
> try(system("convert tmp/7gurf1292972349.ps tmp/7gurf1292972349.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qkq01292972349.ps tmp/8qkq01292972349.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qkq01292972349.ps tmp/9qkq01292972349.png",intern=TRUE))
character(0)
> try(system("convert tmp/101u731292972349.ps tmp/101u731292972349.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.483 1.860 18.133