R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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 '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
+ ,10.8
+ ,12.37
+ ,10.9
+ ,12.53
+ ,10.9
+ ,12.02
+ ,11.0
+ ,11.70
+ ,11.0
+ ,11.67
+ ,10.9
+ ,11.51
+ ,10.9
+ ,11.50
+ ,10.8
+ ,11.77
+ ,10.8
+ ,11.75
+ ,10.8
+ ,11.87
+ ,10.8
+ ,12.18
+ ,10.8
+ ,12.29
+ ,10.8
+ ,12.41
+ ,10.9
+ ,12.55
+ ,10.9
+ ,12.39
+ ,10.8
+ ,12.39
+ ,10.7
+ ,12.40
+ ,10.6
+ ,12.33
+ ,10.6
+ ,12.16
+ ,10.6
+ ,12.12
+ ,10.4
+ ,12.13
+ ,10.2
+ ,11.90
+ ,10.1
+ ,11.84
+ ,10.0
+ ,11.75
+ ,9.9
+ ,11.73
+ ,9.9
+ ,11.73
+ ,9.9
+ ,11.64
+ ,9.9
+ ,11.45
+ ,9.9
+ ,10.78
+ ,10.0
+ ,10.67
+ ,10.0
+ ,10.80
+ ,10.1
+ ,10.76
+ ,10.1
+ ,10.38
+ ,10.1
+ ,9.99
+ ,10.1
+ ,9.94
+ ,10.1
+ ,10.05
+ ,10.0
+ ,9.99
+ ,10.0
+ ,9.48
+ ,10.0
+ ,8.29
+ ,10.0
+ ,8.48
+ ,10.1
+ ,8.58
+ ,10.1
+ ,8.23
+ ,10.1
+ ,7.92
+ ,10.0
+ ,8.04
+ ,10.0
+ ,8.09
+ ,10.0
+ ,8.09
+ ,9.9
+ ,8.27
+ ,9.9
+ ,8.26
+ ,9.8
+ ,8.20
+ ,9.7
+ ,8.11
+ ,9.7
+ ,8.02
+ ,9.6
+ ,8.05
+ ,9.6
+ ,8.10
+ ,9.5
+ ,8.07
+ ,9.4
+ ,7.96
+ ,9.4
+ ,8.18
+ ,9.3
+ ,8.64
+ ,9.2
+ ,8.35
+ ,9.1
+ ,8.27
+ ,8.9
+ ,8.16
+ ,8.8
+ ,7.78
+ ,8.7
+ ,7.67
+ ,8.5
+ ,7.79
+ ,8.3
+ ,7.93
+ ,8.2
+ ,7.95
+ ,8.1
+ ,8.09
+ ,7.9
+ ,8.20
+ ,7.8
+ ,8.24
+ ,7.7
+ ,8.09
+ ,7.6
+ ,8.05
+ ,7.5
+ ,8.14
+ ,7.4
+ ,8.17
+ ,7.3
+ ,8.30
+ ,7.3
+ ,8.51
+ ,7.2
+ ,8.42
+ ,7.1
+ ,8.36
+ ,7.0
+ ,8.35
+ ,6.9
+ ,8.39
+ ,6.8
+ ,8.36
+ ,6.7
+ ,8.43
+ ,6.7
+ ,8.68
+ ,6.6
+ ,9.10
+ ,6.6
+ ,9.40
+ ,6.6
+ ,9.80
+ ,6.5
+ ,10.40
+ ,6.5
+ ,10.26
+ ,6.4
+ ,10.00
+ ,6.4
+ ,9.82
+ ,6.4
+ ,9.75
+ ,6.4
+ ,9.56
+ ,6.4
+ ,10.01
+ ,6.4
+ ,10.30
+ ,6.4
+ ,10.22
+ ,6.3
+ ,10.01
+ ,6.4
+ ,9.95
+ ,6.4
+ ,9.87
+ ,6.4
+ ,9.25
+ ,6.4
+ ,9.23
+ ,6.5
+ ,9.17
+ ,6.5
+ ,9.14
+ ,6.6
+ ,9.26
+ ,6.6
+ ,9.47
+ ,6.7
+ ,9.41
+ ,6.7
+ ,9.22
+ ,6.8
+ ,9.13
+ ,6.9
+ ,9.15
+ ,7.0
+ ,9.13
+ ,7.0
+ ,8.72
+ ,7.1
+ ,8.72
+ ,7.2
+ ,8.81
+ ,7.2
+ ,8.86
+ ,7.4
+ ,8.83
+ ,7.5
+ ,8.92
+ ,7.6
+ ,8.91
+ ,7.8
+ ,9.03
+ ,7.9
+ ,8.77
+ ,8.1
+ ,8.28
+ ,8.2
+ ,8.04
+ ,8.4
+ ,7.95
+ ,8.5
+ ,7.57
+ ,8.7
+ ,7.65
+ ,8.9
+ ,7.37
+ ,9.0
+ ,7.44
+ ,9.2
+ ,7.43
+ ,9.3
+ ,7.23
+ ,9.5
+ ,7.05
+ ,9.6
+ ,7.08
+ ,9.6
+ ,7.22
+ ,9.7
+ ,7.19
+ ,9.8
+ ,6.92
+ ,9.9
+ ,6.59
+ ,9.9
+ ,6.52
+ ,9.8
+ ,6.70
+ ,9.8
+ ,7.14
+ ,9.8
+ ,7.29
+ ,9.8
+ ,7.54
+ ,9.7
+ ,7.98
+ ,9.7
+ ,7.95
+ ,9.7
+ ,8.21
+ ,9.7
+ ,8.58
+ ,9.6
+ ,8.45
+ ,9.6
+ ,8.35
+ ,9.6
+ ,8.30
+ ,9.6
+ ,8.45
+ ,9.6
+ ,8.27
+ ,9.7
+ ,8.16
+ ,9.7
+ ,7.85
+ ,9.8
+ ,7.59
+ ,9.8
+ ,7.33
+ ,9.9
+ ,7.33
+ ,9.9
+ ,7.19
+ ,9.9
+ ,7.04
+ ,9.8
+ ,7.06
+ ,9.7
+ ,6.80
+ ,9.7
+ ,6.70
+ ,9.6
+ ,6.44
+ ,9.5
+ ,6.64
+ ,9.4
+ ,6.84
+ ,9.4
+ ,6.67
+ ,9.3
+ ,6.69
+ ,9.2
+ ,6.78
+ ,9.2
+ ,6.78
+ ,9.2
+ ,6.62
+ ,9.1
+ ,6.45
+ ,9.1
+ ,6.10
+ ,9.1
+ ,6.00
+ ,9.1
+ ,5.90
+ ,9.2
+ ,5.89
+ ,9.3
+ ,5.65
+ ,9.3
+ ,5.85
+ ,9.3
+ ,6.02
+ ,9.3
+ ,5.90
+ ,9.3
+ ,5.83
+ ,9.4
+ ,5.64
+ ,9.4
+ ,5.75
+ ,9.4
+ ,5.69
+ ,9.5
+ ,5.69
+ ,9.5
+ ,5.68
+ ,9.4
+ ,5.45
+ ,9.4
+ ,5.22
+ ,9.3
+ ,5.11
+ ,9.4
+ ,5.03
+ ,9.4
+ ,5.03
+ ,9.2
+ ,5.09
+ ,9.1
+ ,4.96
+ ,9.1
+ ,4.88
+ ,9.1
+ ,4.66
+ ,9.0
+ ,4.34
+ ,9.0
+ ,4.28
+ ,8.9
+ ,4.33
+ ,8.8
+ ,4.09
+ ,8.7
+ ,3.90
+ ,8.5
+ ,4.04
+ ,8.3
+ ,4.26
+ ,8.1
+ ,4.11
+ ,7.9
+ ,4.29
+ ,7.8
+ ,4.64
+ ,7.6
+ ,4.94
+ ,7.4
+ ,5.18
+ ,7.2
+ ,5.34
+ ,7.0
+ ,5.58
+ ,7.0
+ ,5.30
+ ,6.8
+ ,5.41
+ ,6.8
+ ,5.79
+ ,6.7
+ ,5.79
+ ,6.8
+ ,5.62
+ ,6.7
+ ,5.52
+ ,6.7
+ ,5.69
+ ,6.7
+ ,5.53
+ ,6.5
+ ,5.60
+ ,6.3
+ ,5.56
+ ,6.3
+ ,5.63
+ ,6.3
+ ,5.58
+ ,6.5
+ ,5.52
+ ,6.6
+ ,5.28
+ ,6.5
+ ,5.16
+ ,6.3
+ ,5.16
+ ,6.3
+ ,5.08
+ ,6.5
+ ,5.21
+ ,7.0
+ ,5.38
+ ,7.1
+ ,5.33
+ ,7.3
+ ,5.35
+ ,7.3
+ ,5.15
+ ,7.4
+ ,5.14
+ ,7.4
+ ,4.89
+ ,7.3
+ ,4.75
+ ,7.4
+ ,4.97
+ ,7.5
+ ,5.08
+ ,7.7
+ ,5.15
+ ,7.7
+ ,5.37
+ ,7.7
+ ,5.37
+ ,7.7
+ ,5.38
+ ,7.7
+ ,5.24
+ ,7.8
+ ,5.09
+ ,8.0
+ ,4.80
+ ,8.1
+ ,4.60
+ ,8.1
+ ,4.66
+ ,8.2
+ ,4.64
+ ,8.2
+ ,4.46
+ ,8.2
+ ,4.28
+ ,8.1
+ ,4.11
+ ,8.1
+ ,4.15
+ ,8.2
+ ,4.29
+ ,8.3
+ ,3.95
+ ,8.3
+ ,3.74
+ ,8.4
+ ,4.06
+ ,8.5
+ ,4.22
+ ,8.5
+ ,4.25
+ ,8.4
+ ,4.31
+ ,8.0
+ ,4.43
+ ,7.9
+ ,4.38
+ ,8.1
+ ,4.26
+ ,8.5
+ ,4.26
+ ,8.8
+ ,4.07
+ ,8.8
+ ,4.26
+ ,8.6
+ ,4.40
+ ,8.3
+ ,4.46
+ ,8.3
+ ,4.34
+ ,8.3
+ ,4.18
+ ,8.4
+ ,4.11
+ ,8.4
+ ,3.98
+ ,8.5
+ ,3.85
+ ,8.6
+ ,3.66
+ ,8.6
+ ,3.59
+ ,8.6
+ ,3.57
+ ,8.6
+ ,3.76
+ ,8.6
+ ,3.60
+ ,8.5
+ ,3.43
+ ,8.4
+ ,3.26
+ ,8.4
+ ,3.30
+ ,8.4
+ ,3.31
+ ,8.5
+ ,3.14
+ ,8.5
+ ,3.30
+ ,8.6
+ ,3.49
+ ,8.6
+ ,3.39
+ ,8.4
+ ,3.37
+ ,8.2
+ ,3.54
+ ,8.0
+ ,3.70
+ ,8.0
+ ,3.96
+ ,8.0
+ ,4.03
+ ,8.0
+ ,4.02
+ ,7.9
+ ,4.04
+ ,7.9
+ ,3.92
+ ,7.8
+ ,3.79
+ ,7.8
+ ,3.83
+ ,8.0)
+ ,dim=c(2
+ ,286)
+ ,dimnames=list(c('werkloosheid'
+ ,'rente')
+ ,1:286))
> y <- array(NA,dim=c(2,286),dimnames=list(c('werkloosheid','rente'),1:286))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
werkloosheid rente
1 12.42 10.8
2 12.37 10.9
3 12.53 10.9
4 12.02 11.0
5 11.70 11.0
6 11.67 10.9
7 11.51 10.9
8 11.50 10.8
9 11.77 10.8
10 11.75 10.8
11 11.87 10.8
12 12.18 10.8
13 12.29 10.8
14 12.41 10.9
15 12.55 10.9
16 12.39 10.8
17 12.39 10.7
18 12.40 10.6
19 12.33 10.6
20 12.16 10.6
21 12.12 10.4
22 12.13 10.2
23 11.90 10.1
24 11.84 10.0
25 11.75 9.9
26 11.73 9.9
27 11.73 9.9
28 11.64 9.9
29 11.45 9.9
30 10.78 10.0
31 10.67 10.0
32 10.80 10.1
33 10.76 10.1
34 10.38 10.1
35 9.99 10.1
36 9.94 10.1
37 10.05 10.0
38 9.99 10.0
39 9.48 10.0
40 8.29 10.0
41 8.48 10.1
42 8.58 10.1
43 8.23 10.1
44 7.92 10.0
45 8.04 10.0
46 8.09 10.0
47 8.09 9.9
48 8.27 9.9
49 8.26 9.8
50 8.20 9.7
51 8.11 9.7
52 8.02 9.6
53 8.05 9.6
54 8.10 9.5
55 8.07 9.4
56 7.96 9.4
57 8.18 9.3
58 8.64 9.2
59 8.35 9.1
60 8.27 8.9
61 8.16 8.8
62 7.78 8.7
63 7.67 8.5
64 7.79 8.3
65 7.93 8.2
66 7.95 8.1
67 8.09 7.9
68 8.20 7.8
69 8.24 7.7
70 8.09 7.6
71 8.05 7.5
72 8.14 7.4
73 8.17 7.3
74 8.30 7.3
75 8.51 7.2
76 8.42 7.1
77 8.36 7.0
78 8.35 6.9
79 8.39 6.8
80 8.36 6.7
81 8.43 6.7
82 8.68 6.6
83 9.10 6.6
84 9.40 6.6
85 9.80 6.5
86 10.40 6.5
87 10.26 6.4
88 10.00 6.4
89 9.82 6.4
90 9.75 6.4
91 9.56 6.4
92 10.01 6.4
93 10.30 6.4
94 10.22 6.3
95 10.01 6.4
96 9.95 6.4
97 9.87 6.4
98 9.25 6.4
99 9.23 6.5
100 9.17 6.5
101 9.14 6.6
102 9.26 6.6
103 9.47 6.7
104 9.41 6.7
105 9.22 6.8
106 9.13 6.9
107 9.15 7.0
108 9.13 7.0
109 8.72 7.1
110 8.72 7.2
111 8.81 7.2
112 8.86 7.4
113 8.83 7.5
114 8.92 7.6
115 8.91 7.8
116 9.03 7.9
117 8.77 8.1
118 8.28 8.2
119 8.04 8.4
120 7.95 8.5
121 7.57 8.7
122 7.65 8.9
123 7.37 9.0
124 7.44 9.2
125 7.43 9.3
126 7.23 9.5
127 7.05 9.6
128 7.08 9.6
129 7.22 9.7
130 7.19 9.8
131 6.92 9.9
132 6.59 9.9
133 6.52 9.8
134 6.70 9.8
135 7.14 9.8
136 7.29 9.8
137 7.54 9.7
138 7.98 9.7
139 7.95 9.7
140 8.21 9.7
141 8.58 9.6
142 8.45 9.6
143 8.35 9.6
144 8.30 9.6
145 8.45 9.6
146 8.27 9.7
147 8.16 9.7
148 7.85 9.8
149 7.59 9.8
150 7.33 9.9
151 7.33 9.9
152 7.19 9.9
153 7.04 9.8
154 7.06 9.7
155 6.80 9.7
156 6.70 9.6
157 6.44 9.5
158 6.64 9.4
159 6.84 9.4
160 6.67 9.3
161 6.69 9.2
162 6.78 9.2
163 6.78 9.2
164 6.62 9.1
165 6.45 9.1
166 6.10 9.1
167 6.00 9.1
168 5.90 9.2
169 5.89 9.3
170 5.65 9.3
171 5.85 9.3
172 6.02 9.3
173 5.90 9.3
174 5.83 9.4
175 5.64 9.4
176 5.75 9.4
177 5.69 9.5
178 5.69 9.5
179 5.68 9.4
180 5.45 9.4
181 5.22 9.3
182 5.11 9.4
183 5.03 9.4
184 5.03 9.2
185 5.09 9.1
186 4.96 9.1
187 4.88 9.1
188 4.66 9.0
189 4.34 9.0
190 4.28 8.9
191 4.33 8.8
192 4.09 8.7
193 3.90 8.5
194 4.04 8.3
195 4.26 8.1
196 4.11 7.9
197 4.29 7.8
198 4.64 7.6
199 4.94 7.4
200 5.18 7.2
201 5.34 7.0
202 5.58 7.0
203 5.30 6.8
204 5.41 6.8
205 5.79 6.7
206 5.79 6.8
207 5.62 6.7
208 5.52 6.7
209 5.69 6.7
210 5.53 6.5
211 5.60 6.3
212 5.56 6.3
213 5.63 6.3
214 5.58 6.5
215 5.52 6.6
216 5.28 6.5
217 5.16 6.3
218 5.16 6.3
219 5.08 6.5
220 5.21 7.0
221 5.38 7.1
222 5.33 7.3
223 5.35 7.3
224 5.15 7.4
225 5.14 7.4
226 4.89 7.3
227 4.75 7.4
228 4.97 7.5
229 5.08 7.7
230 5.15 7.7
231 5.37 7.7
232 5.37 7.7
233 5.38 7.7
234 5.24 7.8
235 5.09 8.0
236 4.80 8.1
237 4.60 8.1
238 4.66 8.2
239 4.64 8.2
240 4.46 8.2
241 4.28 8.1
242 4.11 8.1
243 4.15 8.2
244 4.29 8.3
245 3.95 8.3
246 3.74 8.4
247 4.06 8.5
248 4.22 8.5
249 4.25 8.4
250 4.31 8.0
251 4.43 7.9
252 4.38 8.1
253 4.26 8.5
254 4.26 8.8
255 4.07 8.8
256 4.26 8.6
257 4.40 8.3
258 4.46 8.3
259 4.34 8.3
260 4.18 8.4
261 4.11 8.4
262 3.98 8.5
263 3.85 8.6
264 3.66 8.6
265 3.59 8.6
266 3.57 8.6
267 3.76 8.6
268 3.60 8.5
269 3.43 8.4
270 3.26 8.4
271 3.30 8.4
272 3.31 8.5
273 3.14 8.5
274 3.30 8.6
275 3.49 8.6
276 3.39 8.4
277 3.37 8.2
278 3.54 8.0
279 3.70 8.0
280 3.96 8.0
281 4.03 8.0
282 4.02 7.9
283 4.04 7.9
284 3.92 7.8
285 3.79 7.8
286 3.83 8.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) rente
1.6958 0.6412
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.0057 -2.0728 -0.3175 2.2119 4.5366
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6958 0.9444 1.796 0.0736 .
rente 0.6412 0.1090 5.883 1.13e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.409 on 284 degrees of freedom
Multiple R-squared: 0.1086, Adjusted R-squared: 0.1055
F-statistic: 34.61 on 1 and 284 DF, p-value: 1.134e-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,] 1.420163e-03 2.840326e-03 9.985798e-01
[2,] 8.593449e-04 1.718690e-03 9.991407e-01
[3,] 4.384220e-04 8.768441e-04 9.995616e-01
[4,] 2.231233e-04 4.462466e-04 9.997769e-01
[5,] 4.435600e-05 8.871200e-05 9.999556e-01
[6,] 8.287186e-06 1.657437e-05 9.999917e-01
[7,] 1.373314e-06 2.746627e-06 9.999986e-01
[8,] 2.790698e-07 5.581396e-07 9.999997e-01
[9,] 6.566223e-08 1.313245e-07 9.999999e-01
[10,] 2.107080e-08 4.214160e-08 1.000000e+00
[11,] 9.381492e-09 1.876298e-08 1.000000e+00
[12,] 2.474881e-09 4.949762e-09 1.000000e+00
[13,] 5.661087e-10 1.132217e-09 1.000000e+00
[14,] 1.098836e-10 2.197673e-10 1.000000e+00
[15,] 1.996204e-11 3.992408e-11 1.000000e+00
[16,] 3.813588e-12 7.627176e-12 1.000000e+00
[17,] 8.588628e-13 1.717726e-12 1.000000e+00
[18,] 1.837699e-13 3.675399e-13 1.000000e+00
[19,] 5.112323e-14 1.022465e-13 1.000000e+00
[20,] 1.258664e-14 2.517329e-14 1.000000e+00
[21,] 3.078872e-15 6.157744e-15 1.000000e+00
[22,] 6.998266e-16 1.399653e-15 1.000000e+00
[23,] 1.517010e-16 3.034020e-16 1.000000e+00
[24,] 3.669711e-17 7.339423e-17 1.000000e+00
[25,] 1.333427e-17 2.666855e-17 1.000000e+00
[26,] 1.514916e-16 3.029831e-16 1.000000e+00
[27,] 8.977241e-16 1.795448e-15 1.000000e+00
[28,] 1.731436e-15 3.462871e-15 1.000000e+00
[29,] 2.753125e-15 5.506251e-15 1.000000e+00
[30,] 1.467975e-14 2.935950e-14 1.000000e+00
[31,] 2.094465e-13 4.188930e-13 1.000000e+00
[32,] 1.421851e-12 2.843702e-12 1.000000e+00
[33,] 2.857052e-12 5.714104e-12 1.000000e+00
[34,] 5.113331e-12 1.022666e-11 1.000000e+00
[35,] 2.613218e-11 5.226435e-11 1.000000e+00
[36,] 2.659282e-09 5.318565e-09 1.000000e+00
[37,] 4.341004e-08 8.682007e-08 1.000000e+00
[38,] 2.546225e-07 5.092451e-07 9.999997e-01
[39,] 1.674917e-06 3.349834e-06 9.999983e-01
[40,] 7.842983e-06 1.568597e-05 9.999922e-01
[41,] 2.117880e-05 4.235760e-05 9.999788e-01
[42,] 4.300342e-05 8.600683e-05 9.999570e-01
[43,] 6.099607e-05 1.219921e-04 9.999390e-01
[44,] 6.995215e-05 1.399043e-04 9.999300e-01
[45,] 6.579258e-05 1.315852e-04 9.999342e-01
[46,] 5.402603e-05 1.080521e-04 9.999460e-01
[47,] 4.463701e-05 8.927402e-05 9.999554e-01
[48,] 3.323331e-05 6.646663e-05 9.999668e-01
[49,] 2.411333e-05 4.822666e-05 9.999759e-01
[50,] 1.599455e-05 3.198911e-05 9.999840e-01
[51,] 1.021938e-05 2.043876e-05 9.999898e-01
[52,] 6.492492e-06 1.298498e-05 9.999935e-01
[53,] 4.224188e-06 8.448375e-06 9.999958e-01
[54,] 3.551021e-06 7.102042e-06 9.999964e-01
[55,] 2.759669e-06 5.519339e-06 9.999972e-01
[56,] 2.512465e-06 5.024931e-06 9.999975e-01
[57,] 2.262418e-06 4.524836e-06 9.999977e-01
[58,] 1.716074e-06 3.432147e-06 9.999983e-01
[59,] 1.448478e-06 2.896956e-06 9.999986e-01
[60,] 1.585554e-06 3.171107e-06 9.999984e-01
[61,] 1.917829e-06 3.835657e-06 9.999981e-01
[62,] 2.290924e-06 4.581849e-06 9.999977e-01
[63,] 3.417424e-06 6.834848e-06 9.999966e-01
[64,] 5.113986e-06 1.022797e-05 9.999949e-01
[65,] 7.240587e-06 1.448117e-05 9.999928e-01
[66,] 8.528322e-06 1.705664e-05 9.999915e-01
[67,] 9.393025e-06 1.878605e-05 9.999906e-01
[68,] 1.064891e-05 2.129782e-05 9.999894e-01
[69,] 1.190208e-05 2.380417e-05 9.999881e-01
[70,] 1.305489e-05 2.610977e-05 9.999869e-01
[71,] 1.606929e-05 3.213857e-05 9.999839e-01
[72,] 1.799929e-05 3.599859e-05 9.999820e-01
[73,] 1.898031e-05 3.796061e-05 9.999810e-01
[74,] 1.954824e-05 3.909649e-05 9.999805e-01
[75,] 2.027058e-05 4.054117e-05 9.999797e-01
[76,] 2.034018e-05 4.068036e-05 9.999797e-01
[77,] 2.005000e-05 4.009999e-05 9.999800e-01
[78,] 2.257058e-05 4.514115e-05 9.999774e-01
[79,] 3.109346e-05 6.218692e-05 9.999689e-01
[80,] 4.852087e-05 9.704174e-05 9.999515e-01
[81,] 9.696540e-05 1.939308e-04 9.999030e-01
[82,] 2.737647e-04 5.475294e-04 9.997262e-01
[83,] 6.053187e-04 1.210637e-03 9.993947e-01
[84,] 1.005298e-03 2.010596e-03 9.989947e-01
[85,] 1.423192e-03 2.846384e-03 9.985768e-01
[86,] 1.884931e-03 3.769861e-03 9.981151e-01
[87,] 2.250733e-03 4.501466e-03 9.977493e-01
[88,] 3.285259e-03 6.570518e-03 9.967147e-01
[89,] 5.451643e-03 1.090329e-02 9.945484e-01
[90,] 8.612489e-03 1.722498e-02 9.913875e-01
[91,] 1.205392e-02 2.410785e-02 9.879461e-01
[92,] 1.650125e-02 3.300250e-02 9.834987e-01
[93,] 2.208058e-02 4.416117e-02 9.779194e-01
[94,] 2.473025e-02 4.946049e-02 9.752698e-01
[95,] 2.790897e-02 5.581793e-02 9.720910e-01
[96,] 3.166566e-02 6.333132e-02 9.683343e-01
[97,] 3.619313e-02 7.238626e-02 9.638069e-01
[98,] 4.349615e-02 8.699230e-02 9.565039e-01
[99,] 5.596472e-02 1.119294e-01 9.440353e-01
[100,] 7.230697e-02 1.446139e-01 9.276930e-01
[101,] 9.016926e-02 1.803385e-01 9.098307e-01
[102,] 1.113902e-01 2.227804e-01 8.886098e-01
[103,] 1.392047e-01 2.784094e-01 8.607953e-01
[104,] 1.755960e-01 3.511921e-01 8.244040e-01
[105,] 2.079231e-01 4.158462e-01 7.920769e-01
[106,] 2.468061e-01 4.936122e-01 7.531939e-01
[107,] 2.983906e-01 5.967812e-01 7.016094e-01
[108,] 3.587116e-01 7.174233e-01 6.412884e-01
[109,] 4.260783e-01 8.521565e-01 5.739217e-01
[110,] 5.050023e-01 9.899954e-01 4.949977e-01
[111,] 5.848397e-01 8.303206e-01 4.151603e-01
[112,] 6.717867e-01 6.564267e-01 3.282133e-01
[113,] 7.402027e-01 5.195945e-01 2.597973e-01
[114,] 7.892899e-01 4.214203e-01 2.107101e-01
[115,] 8.285601e-01 3.428798e-01 1.714399e-01
[116,] 8.623153e-01 2.753695e-01 1.376847e-01
[117,] 8.901488e-01 2.197024e-01 1.098512e-01
[118,] 9.129852e-01 1.740297e-01 8.701483e-02
[119,] 9.327524e-01 1.344952e-01 6.724762e-02
[120,] 9.481940e-01 1.036119e-01 5.180596e-02
[121,] 9.603855e-01 7.922896e-02 3.961448e-02
[122,] 9.706558e-01 5.868830e-02 2.934415e-02
[123,] 9.789916e-01 4.201680e-02 2.100840e-02
[124,] 9.847819e-01 3.043623e-02 1.521811e-02
[125,] 9.886965e-01 2.260702e-02 1.130351e-02
[126,] 9.916233e-01 1.675347e-02 8.376737e-03
[127,] 9.940968e-01 1.180639e-02 5.903195e-03
[128,] 9.961428e-01 7.714349e-03 3.857174e-03
[129,] 9.974536e-01 5.092831e-03 2.546415e-03
[130,] 9.981913e-01 3.617322e-03 1.808661e-03
[131,] 9.985812e-01 2.837628e-03 1.418814e-03
[132,] 9.988637e-01 2.272594e-03 1.136297e-03
[133,] 9.990911e-01 1.817732e-03 9.088662e-04
[134,] 9.993182e-01 1.363508e-03 6.817538e-04
[135,] 9.994985e-01 1.003047e-03 5.015234e-04
[136,] 9.996676e-01 6.648271e-04 3.324136e-04
[137,] 9.998272e-01 3.456885e-04 1.728442e-04
[138,] 9.999127e-01 1.746881e-04 8.734405e-05
[139,] 9.999578e-01 8.440892e-05 4.220446e-05
[140,] 9.999811e-01 3.773255e-05 1.886628e-05
[141,] 9.999933e-01 1.330037e-05 6.650186e-06
[142,] 9.999977e-01 4.683167e-06 2.341584e-06
[143,] 9.999992e-01 1.522828e-06 7.614141e-07
[144,] 9.999997e-01 5.483905e-07 2.741952e-07
[145,] 9.999999e-01 2.081984e-07 1.040992e-07
[146,] 1.000000e+00 8.456678e-08 4.228339e-08
[147,] 1.000000e+00 3.112664e-08 1.556332e-08
[148,] 1.000000e+00 1.127451e-08 5.637254e-09
[149,] 1.000000e+00 4.062534e-09 2.031267e-09
[150,] 1.000000e+00 1.301876e-09 6.509380e-10
[151,] 1.000000e+00 4.552848e-10 2.276424e-10
[152,] 1.000000e+00 1.582464e-10 7.912322e-11
[153,] 1.000000e+00 6.137220e-11 3.068610e-11
[154,] 1.000000e+00 1.981238e-11 9.906191e-12
[155,] 1.000000e+00 4.747254e-12 2.373627e-12
[156,] 1.000000e+00 1.217840e-12 6.089200e-13
[157,] 1.000000e+00 2.748770e-13 1.374385e-13
[158,] 1.000000e+00 4.697844e-14 2.348922e-14
[159,] 1.000000e+00 6.532793e-15 3.266397e-15
[160,] 1.000000e+00 9.908286e-16 4.954143e-16
[161,] 1.000000e+00 1.656448e-16 8.282241e-17
[162,] 1.000000e+00 4.066694e-17 2.033347e-17
[163,] 1.000000e+00 1.052764e-17 5.263821e-18
[164,] 1.000000e+00 2.752583e-18 1.376292e-18
[165,] 1.000000e+00 6.388920e-19 3.194460e-19
[166,] 1.000000e+00 1.885242e-19 9.426211e-20
[167,] 1.000000e+00 3.888553e-20 1.944277e-20
[168,] 1.000000e+00 4.967585e-21 2.483793e-21
[169,] 1.000000e+00 6.591415e-22 3.295708e-22
[170,] 1.000000e+00 7.342874e-23 3.671437e-23
[171,] 1.000000e+00 9.974529e-24 4.987265e-24
[172,] 1.000000e+00 7.928767e-25 3.964384e-25
[173,] 1.000000e+00 4.384355e-26 2.192177e-26
[174,] 1.000000e+00 1.453603e-27 7.268014e-28
[175,] 1.000000e+00 3.300428e-29 1.650214e-29
[176,] 1.000000e+00 9.961257e-31 4.980628e-31
[177,] 1.000000e+00 5.793866e-32 2.896933e-32
[178,] 1.000000e+00 2.533178e-33 1.266589e-33
[179,] 1.000000e+00 9.215755e-35 4.607877e-35
[180,] 1.000000e+00 3.951108e-36 1.975554e-36
[181,] 1.000000e+00 1.084313e-37 5.421566e-38
[182,] 1.000000e+00 3.306320e-39 1.653160e-39
[183,] 1.000000e+00 8.646273e-41 4.323137e-41
[184,] 1.000000e+00 7.528197e-42 3.764098e-42
[185,] 1.000000e+00 2.179194e-42 1.089597e-42
[186,] 1.000000e+00 1.011899e-42 5.059493e-43
[187,] 1.000000e+00 5.278400e-43 2.639200e-43
[188,] 1.000000e+00 6.345166e-43 3.172583e-43
[189,] 1.000000e+00 1.070882e-42 5.354409e-43
[190,] 1.000000e+00 2.059833e-42 1.029917e-42
[191,] 1.000000e+00 4.626403e-42 2.313201e-42
[192,] 1.000000e+00 7.962126e-42 3.981063e-42
[193,] 1.000000e+00 1.756028e-41 8.780141e-42
[194,] 1.000000e+00 5.170845e-41 2.585422e-41
[195,] 1.000000e+00 1.696036e-40 8.480179e-41
[196,] 1.000000e+00 5.816316e-40 2.908158e-40
[197,] 1.000000e+00 2.130549e-39 1.065275e-39
[198,] 1.000000e+00 5.847834e-39 2.923917e-39
[199,] 1.000000e+00 2.301495e-38 1.150747e-38
[200,] 1.000000e+00 9.286519e-38 4.643260e-38
[201,] 1.000000e+00 2.917928e-37 1.458964e-37
[202,] 1.000000e+00 7.452942e-37 3.726471e-37
[203,] 1.000000e+00 2.831868e-36 1.415934e-36
[204,] 1.000000e+00 1.152846e-35 5.764228e-36
[205,] 1.000000e+00 3.955133e-35 1.977567e-35
[206,] 1.000000e+00 1.706142e-34 8.530709e-35
[207,] 1.000000e+00 7.323532e-34 3.661766e-34
[208,] 1.000000e+00 3.032808e-33 1.516404e-33
[209,] 1.000000e+00 1.305491e-32 6.527456e-33
[210,] 1.000000e+00 5.622338e-32 2.811169e-32
[211,] 1.000000e+00 2.376933e-31 1.188467e-31
[212,] 1.000000e+00 8.150068e-31 4.075034e-31
[213,] 1.000000e+00 1.310373e-30 6.551864e-31
[214,] 1.000000e+00 1.338661e-30 6.693307e-31
[215,] 1.000000e+00 1.072846e-30 5.364229e-31
[216,] 1.000000e+00 3.815138e-30 1.907569e-30
[217,] 1.000000e+00 1.638824e-29 8.194121e-30
[218,] 1.000000e+00 6.421030e-29 3.210515e-29
[219,] 1.000000e+00 2.433845e-28 1.216922e-28
[220,] 1.000000e+00 9.503814e-28 4.751907e-28
[221,] 1.000000e+00 3.695858e-27 1.847929e-27
[222,] 1.000000e+00 1.311955e-26 6.559776e-27
[223,] 1.000000e+00 4.231496e-26 2.115748e-26
[224,] 1.000000e+00 1.670915e-25 8.354576e-26
[225,] 1.000000e+00 4.902093e-25 2.451047e-25
[226,] 1.000000e+00 1.208414e-24 6.042070e-25
[227,] 1.000000e+00 1.486613e-24 7.433065e-25
[228,] 1.000000e+00 1.380109e-24 6.900543e-25
[229,] 1.000000e+00 8.058424e-25 4.029212e-25
[230,] 1.000000e+00 3.797766e-25 1.898883e-25
[231,] 1.000000e+00 1.331247e-25 6.656236e-26
[232,] 1.000000e+00 1.125899e-25 5.629497e-26
[233,] 1.000000e+00 1.857185e-25 9.285923e-26
[234,] 1.000000e+00 1.896239e-25 9.481195e-26
[235,] 1.000000e+00 1.871489e-25 9.357446e-26
[236,] 1.000000e+00 3.515214e-25 1.757607e-25
[237,] 1.000000e+00 1.183148e-24 5.915742e-25
[238,] 1.000000e+00 5.165958e-24 2.582979e-24
[239,] 1.000000e+00 2.047569e-23 1.023785e-23
[240,] 1.000000e+00 5.631244e-23 2.815622e-23
[241,] 1.000000e+00 2.607810e-22 1.303905e-22
[242,] 1.000000e+00 1.146322e-21 5.731610e-22
[243,] 1.000000e+00 4.523804e-21 2.261902e-21
[244,] 1.000000e+00 1.280659e-20 6.403296e-21
[245,] 1.000000e+00 3.557449e-20 1.778725e-20
[246,] 1.000000e+00 1.150883e-19 5.754413e-20
[247,] 1.000000e+00 2.612464e-19 1.306232e-19
[248,] 1.000000e+00 5.331445e-19 2.665722e-19
[249,] 1.000000e+00 1.215294e-18 6.076472e-19
[250,] 1.000000e+00 1.887938e-18 9.439688e-19
[251,] 1.000000e+00 4.653736e-18 2.326868e-18
[252,] 1.000000e+00 5.644702e-18 2.822351e-18
[253,] 1.000000e+00 5.118202e-18 2.559101e-18
[254,] 1.000000e+00 2.039895e-18 1.019948e-18
[255,] 1.000000e+00 1.141543e-18 5.707713e-19
[256,] 1.000000e+00 9.631892e-19 4.815946e-19
[257,] 1.000000e+00 9.285955e-19 4.642977e-19
[258,] 1.000000e+00 1.129267e-18 5.646337e-19
[259,] 1.000000e+00 1.621566e-18 8.107830e-19
[260,] 1.000000e+00 7.243603e-18 3.621802e-18
[261,] 1.000000e+00 4.179445e-17 2.089723e-17
[262,] 1.000000e+00 2.338690e-16 1.169345e-16
[263,] 1.000000e+00 1.231402e-16 6.157008e-17
[264,] 1.000000e+00 4.414925e-16 2.207463e-16
[265,] 1.000000e+00 5.749394e-15 2.874697e-15
[266,] 1.000000e+00 5.128366e-14 2.564183e-14
[267,] 1.000000e+00 5.186118e-13 2.593059e-13
[268,] 1.000000e+00 6.510380e-12 3.255190e-12
[269,] 1.000000e+00 3.570764e-11 1.785382e-11
[270,] 1.000000e+00 4.568475e-10 2.284238e-10
[271,] 1.000000e+00 2.747933e-09 1.373967e-09
[272,] 1.000000e+00 3.466728e-08 1.733364e-08
[273,] 9.999999e-01 1.802657e-07 9.013285e-08
[274,] 9.999998e-01 3.072348e-07 1.536174e-07
[275,] 9.999995e-01 9.242083e-07 4.621042e-07
[276,] 9.999890e-01 2.202176e-05 1.101088e-05
[277,] 9.997923e-01 4.153516e-04 2.076758e-04
> postscript(file="/var/www/rcomp/tmp/1ropr1292973210.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/22xpu1292973210.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/32xpu1292973210.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/42xpu1292973210.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5v7oe1292973210.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 = 286
Frequency = 1
1 2 3 4 5 6
3.79962555 3.68550956 3.84550956 3.27139356 2.95139356 2.98550956
7 8 9 10 11 12
2.82550956 2.87962555 3.14962555 3.12962555 3.24962555 3.55962555
13 14 15 16 17 18
3.66962555 3.72550956 3.86550956 3.76962555 3.83374155 3.90785754
19 20 21 22 23 24
3.83785754 3.66785754 3.75608953 3.89432152 3.72843752 3.73255351
25 26 27 28 29 30
3.70666951 3.68666951 3.68666951 3.59666951 3.40666951 2.67255351
31 32 33 34 35 36
2.56255351 2.62843752 2.58843752 2.20843752 1.81843752 1.76843752
37 38 39 40 41 42
1.94255351 1.88255351 1.37255351 0.18255351 0.30843752 0.40843752
43 44 45 46 47 48
0.05843752 -0.18744649 -0.06744649 -0.01744649 0.04666951 0.22666951
49 50 51 52 53 54
0.28078550 0.28490150 0.19490150 0.16901749 0.19901749 0.31313349
55 56 57 58 59 60
0.34724948 0.23724948 0.52136548 1.04548147 0.81959747 0.86782946
61 62 63 64 65 66
0.82194545 0.50606145 0.52429344 0.77252543 0.97664142 1.06075742
67 68 69 70 71 72
1.32898941 1.50310540 1.60722140 1.52133739 1.54545339 1.69956938
73 74 75 76 77 78
1.79368538 1.92368538 2.19780137 2.17191737 2.17603336 2.23014936
79 80 81 82 83 84
2.33426535 2.36838135 2.43838135 2.75249734 3.17249734 3.47249734
85 86 87 88 89 90
3.93661334 4.53661334 4.46072933 4.20072933 4.02072933 3.95072933
91 92 93 94 95 96
3.76072933 4.21072933 4.50072933 4.48484533 4.21072933 4.15072933
97 98 99 100 101 102
4.07072933 3.45072933 3.36661334 3.30661334 3.21249734 3.33249734
103 104 105 106 107 108
3.47838135 3.41838135 3.16426535 3.01014936 2.96603336 2.94603336
109 110 111 112 113 114
2.47191737 2.40780137 2.49780137 2.41956938 2.32545339 2.35133739
115 116 117 118 119 120
2.21310540 2.26898941 1.88075742 1.32664142 0.95840943 0.80429344
121 122 123 124 125 126
0.29606145 0.24782946 -0.09628654 -0.15451853 -0.22863452 -0.55686651
127 128 129 130 131 132
-0.80098251 -0.77098251 -0.69509850 -0.78921450 -1.12333049 -1.45333049
133 134 135 136 137 138
-1.45921450 -1.27921450 -0.83921450 -0.68921450 -0.37509850 0.06490150
139 140 141 142 143 144
0.03490150 0.29490150 0.72901749 0.59901749 0.49901749 0.44901749
145 146 147 148 149 150
0.59901749 0.35490150 0.24490150 -0.12921450 -0.38921450 -0.71333049
151 152 153 154 155 156
-0.71333049 -0.85333049 -0.93921450 -0.85509850 -1.11509850 -1.15098251
157 158 159 160 161 162
-1.34686651 -1.08275052 -0.88275052 -0.98863452 -0.90451853 -0.81451853
163 164 165 166 167 168
-0.81451853 -0.91040253 -1.08040253 -1.43040253 -1.53040253 -1.69451853
169 170 171 172 173 174
-1.76863452 -2.00863452 -1.80863452 -1.63863452 -1.75863452 -1.89275052
175 176 177 178 179 180
-2.08275052 -1.97275052 -2.09686651 -2.09686651 -2.04275052 -2.27275052
181 182 183 184 185 186
-2.43863452 -2.61275052 -2.69275052 -2.56451853 -2.44040253 -2.57040253
187 188 189 190 191 192
-2.65040253 -2.80628654 -3.12628654 -3.12217054 -3.00805455 -3.18393855
193 194 195 196 197 198
-3.24570656 -2.97747457 -2.62924258 -2.65101059 -2.40689460 -1.92866261
199 200 201 202 203 204
-1.50043062 -1.13219863 -0.84396664 -0.60396664 -0.75573465 -0.64573465
205 206 207 208 209 210
-0.20161865 -0.26573465 -0.37161865 -0.47161865 -0.30161865 -0.33338666
211 212 213 214 215 216
-0.13515467 -0.17515467 -0.10515467 -0.28338666 -0.40750266 -0.58338666
217 218 219 220 221 222
-0.57515467 -0.57515467 -0.78338666 -0.97396664 -0.86808263 -1.04631462
223 224 225 226 227 228
-1.02631462 -1.29043062 -1.30043062 -1.48631462 -1.69043062 -1.53454661
229 230 231 232 233 234
-1.55277860 -1.48277860 -1.26277860 -1.26277860 -1.25277860 -1.45689460
235 236 237 238 239 240
-1.73512659 -2.08924258 -2.28924258 -2.29335858 -2.31335858 -2.49335858
241 242 243 244 245 246
-2.60924258 -2.77924258 -2.80335858 -2.72747457 -3.06747457 -3.34159057
247 248 249 250 251 252
-3.08570656 -2.92570656 -2.83159057 -2.51512659 -2.33101059 -2.50924258
253 254 255 256 257 258
-2.88570656 -3.07805455 -3.26805455 -2.94982256 -2.61747457 -2.55747457
259 260 261 262 263 264
-2.67747457 -2.90159057 -2.97159057 -3.16570656 -3.35982256 -3.54982256
265 266 267 268 269 270
-3.61982256 -3.63982256 -3.44982256 -3.54570656 -3.65159057 -3.82159057
271 272 273 274 275 276
-3.78159057 -3.83570656 -4.00570656 -3.90982256 -3.71982256 -3.69159057
277 278 279 280 281 282
-3.58335858 -3.28512659 -3.12512659 -2.86512659 -2.79512659 -2.74101059
283 284 285 286
-2.72101059 -2.77689460 -2.90689460 -2.99512659
> postscript(file="/var/www/rcomp/tmp/6v7oe1292973210.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 = 286
Frequency = 1
lag(myerror, k = 1) myerror
0 3.79962555 NA
1 3.68550956 3.79962555
2 3.84550956 3.68550956
3 3.27139356 3.84550956
4 2.95139356 3.27139356
5 2.98550956 2.95139356
6 2.82550956 2.98550956
7 2.87962555 2.82550956
8 3.14962555 2.87962555
9 3.12962555 3.14962555
10 3.24962555 3.12962555
11 3.55962555 3.24962555
12 3.66962555 3.55962555
13 3.72550956 3.66962555
14 3.86550956 3.72550956
15 3.76962555 3.86550956
16 3.83374155 3.76962555
17 3.90785754 3.83374155
18 3.83785754 3.90785754
19 3.66785754 3.83785754
20 3.75608953 3.66785754
21 3.89432152 3.75608953
22 3.72843752 3.89432152
23 3.73255351 3.72843752
24 3.70666951 3.73255351
25 3.68666951 3.70666951
26 3.68666951 3.68666951
27 3.59666951 3.68666951
28 3.40666951 3.59666951
29 2.67255351 3.40666951
30 2.56255351 2.67255351
31 2.62843752 2.56255351
32 2.58843752 2.62843752
33 2.20843752 2.58843752
34 1.81843752 2.20843752
35 1.76843752 1.81843752
36 1.94255351 1.76843752
37 1.88255351 1.94255351
38 1.37255351 1.88255351
39 0.18255351 1.37255351
40 0.30843752 0.18255351
41 0.40843752 0.30843752
42 0.05843752 0.40843752
43 -0.18744649 0.05843752
44 -0.06744649 -0.18744649
45 -0.01744649 -0.06744649
46 0.04666951 -0.01744649
47 0.22666951 0.04666951
48 0.28078550 0.22666951
49 0.28490150 0.28078550
50 0.19490150 0.28490150
51 0.16901749 0.19490150
52 0.19901749 0.16901749
53 0.31313349 0.19901749
54 0.34724948 0.31313349
55 0.23724948 0.34724948
56 0.52136548 0.23724948
57 1.04548147 0.52136548
58 0.81959747 1.04548147
59 0.86782946 0.81959747
60 0.82194545 0.86782946
61 0.50606145 0.82194545
62 0.52429344 0.50606145
63 0.77252543 0.52429344
64 0.97664142 0.77252543
65 1.06075742 0.97664142
66 1.32898941 1.06075742
67 1.50310540 1.32898941
68 1.60722140 1.50310540
69 1.52133739 1.60722140
70 1.54545339 1.52133739
71 1.69956938 1.54545339
72 1.79368538 1.69956938
73 1.92368538 1.79368538
74 2.19780137 1.92368538
75 2.17191737 2.19780137
76 2.17603336 2.17191737
77 2.23014936 2.17603336
78 2.33426535 2.23014936
79 2.36838135 2.33426535
80 2.43838135 2.36838135
81 2.75249734 2.43838135
82 3.17249734 2.75249734
83 3.47249734 3.17249734
84 3.93661334 3.47249734
85 4.53661334 3.93661334
86 4.46072933 4.53661334
87 4.20072933 4.46072933
88 4.02072933 4.20072933
89 3.95072933 4.02072933
90 3.76072933 3.95072933
91 4.21072933 3.76072933
92 4.50072933 4.21072933
93 4.48484533 4.50072933
94 4.21072933 4.48484533
95 4.15072933 4.21072933
96 4.07072933 4.15072933
97 3.45072933 4.07072933
98 3.36661334 3.45072933
99 3.30661334 3.36661334
100 3.21249734 3.30661334
101 3.33249734 3.21249734
102 3.47838135 3.33249734
103 3.41838135 3.47838135
104 3.16426535 3.41838135
105 3.01014936 3.16426535
106 2.96603336 3.01014936
107 2.94603336 2.96603336
108 2.47191737 2.94603336
109 2.40780137 2.47191737
110 2.49780137 2.40780137
111 2.41956938 2.49780137
112 2.32545339 2.41956938
113 2.35133739 2.32545339
114 2.21310540 2.35133739
115 2.26898941 2.21310540
116 1.88075742 2.26898941
117 1.32664142 1.88075742
118 0.95840943 1.32664142
119 0.80429344 0.95840943
120 0.29606145 0.80429344
121 0.24782946 0.29606145
122 -0.09628654 0.24782946
123 -0.15451853 -0.09628654
124 -0.22863452 -0.15451853
125 -0.55686651 -0.22863452
126 -0.80098251 -0.55686651
127 -0.77098251 -0.80098251
128 -0.69509850 -0.77098251
129 -0.78921450 -0.69509850
130 -1.12333049 -0.78921450
131 -1.45333049 -1.12333049
132 -1.45921450 -1.45333049
133 -1.27921450 -1.45921450
134 -0.83921450 -1.27921450
135 -0.68921450 -0.83921450
136 -0.37509850 -0.68921450
137 0.06490150 -0.37509850
138 0.03490150 0.06490150
139 0.29490150 0.03490150
140 0.72901749 0.29490150
141 0.59901749 0.72901749
142 0.49901749 0.59901749
143 0.44901749 0.49901749
144 0.59901749 0.44901749
145 0.35490150 0.59901749
146 0.24490150 0.35490150
147 -0.12921450 0.24490150
148 -0.38921450 -0.12921450
149 -0.71333049 -0.38921450
150 -0.71333049 -0.71333049
151 -0.85333049 -0.71333049
152 -0.93921450 -0.85333049
153 -0.85509850 -0.93921450
154 -1.11509850 -0.85509850
155 -1.15098251 -1.11509850
156 -1.34686651 -1.15098251
157 -1.08275052 -1.34686651
158 -0.88275052 -1.08275052
159 -0.98863452 -0.88275052
160 -0.90451853 -0.98863452
161 -0.81451853 -0.90451853
162 -0.81451853 -0.81451853
163 -0.91040253 -0.81451853
164 -1.08040253 -0.91040253
165 -1.43040253 -1.08040253
166 -1.53040253 -1.43040253
167 -1.69451853 -1.53040253
168 -1.76863452 -1.69451853
169 -2.00863452 -1.76863452
170 -1.80863452 -2.00863452
171 -1.63863452 -1.80863452
172 -1.75863452 -1.63863452
173 -1.89275052 -1.75863452
174 -2.08275052 -1.89275052
175 -1.97275052 -2.08275052
176 -2.09686651 -1.97275052
177 -2.09686651 -2.09686651
178 -2.04275052 -2.09686651
179 -2.27275052 -2.04275052
180 -2.43863452 -2.27275052
181 -2.61275052 -2.43863452
182 -2.69275052 -2.61275052
183 -2.56451853 -2.69275052
184 -2.44040253 -2.56451853
185 -2.57040253 -2.44040253
186 -2.65040253 -2.57040253
187 -2.80628654 -2.65040253
188 -3.12628654 -2.80628654
189 -3.12217054 -3.12628654
190 -3.00805455 -3.12217054
191 -3.18393855 -3.00805455
192 -3.24570656 -3.18393855
193 -2.97747457 -3.24570656
194 -2.62924258 -2.97747457
195 -2.65101059 -2.62924258
196 -2.40689460 -2.65101059
197 -1.92866261 -2.40689460
198 -1.50043062 -1.92866261
199 -1.13219863 -1.50043062
200 -0.84396664 -1.13219863
201 -0.60396664 -0.84396664
202 -0.75573465 -0.60396664
203 -0.64573465 -0.75573465
204 -0.20161865 -0.64573465
205 -0.26573465 -0.20161865
206 -0.37161865 -0.26573465
207 -0.47161865 -0.37161865
208 -0.30161865 -0.47161865
209 -0.33338666 -0.30161865
210 -0.13515467 -0.33338666
211 -0.17515467 -0.13515467
212 -0.10515467 -0.17515467
213 -0.28338666 -0.10515467
214 -0.40750266 -0.28338666
215 -0.58338666 -0.40750266
216 -0.57515467 -0.58338666
217 -0.57515467 -0.57515467
218 -0.78338666 -0.57515467
219 -0.97396664 -0.78338666
220 -0.86808263 -0.97396664
221 -1.04631462 -0.86808263
222 -1.02631462 -1.04631462
223 -1.29043062 -1.02631462
224 -1.30043062 -1.29043062
225 -1.48631462 -1.30043062
226 -1.69043062 -1.48631462
227 -1.53454661 -1.69043062
228 -1.55277860 -1.53454661
229 -1.48277860 -1.55277860
230 -1.26277860 -1.48277860
231 -1.26277860 -1.26277860
232 -1.25277860 -1.26277860
233 -1.45689460 -1.25277860
234 -1.73512659 -1.45689460
235 -2.08924258 -1.73512659
236 -2.28924258 -2.08924258
237 -2.29335858 -2.28924258
238 -2.31335858 -2.29335858
239 -2.49335858 -2.31335858
240 -2.60924258 -2.49335858
241 -2.77924258 -2.60924258
242 -2.80335858 -2.77924258
243 -2.72747457 -2.80335858
244 -3.06747457 -2.72747457
245 -3.34159057 -3.06747457
246 -3.08570656 -3.34159057
247 -2.92570656 -3.08570656
248 -2.83159057 -2.92570656
249 -2.51512659 -2.83159057
250 -2.33101059 -2.51512659
251 -2.50924258 -2.33101059
252 -2.88570656 -2.50924258
253 -3.07805455 -2.88570656
254 -3.26805455 -3.07805455
255 -2.94982256 -3.26805455
256 -2.61747457 -2.94982256
257 -2.55747457 -2.61747457
258 -2.67747457 -2.55747457
259 -2.90159057 -2.67747457
260 -2.97159057 -2.90159057
261 -3.16570656 -2.97159057
262 -3.35982256 -3.16570656
263 -3.54982256 -3.35982256
264 -3.61982256 -3.54982256
265 -3.63982256 -3.61982256
266 -3.44982256 -3.63982256
267 -3.54570656 -3.44982256
268 -3.65159057 -3.54570656
269 -3.82159057 -3.65159057
270 -3.78159057 -3.82159057
271 -3.83570656 -3.78159057
272 -4.00570656 -3.83570656
273 -3.90982256 -4.00570656
274 -3.71982256 -3.90982256
275 -3.69159057 -3.71982256
276 -3.58335858 -3.69159057
277 -3.28512659 -3.58335858
278 -3.12512659 -3.28512659
279 -2.86512659 -3.12512659
280 -2.79512659 -2.86512659
281 -2.74101059 -2.79512659
282 -2.72101059 -2.74101059
283 -2.77689460 -2.72101059
284 -2.90689460 -2.77689460
285 -2.99512659 -2.90689460
286 NA -2.99512659
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.68550956 3.79962555
[2,] 3.84550956 3.68550956
[3,] 3.27139356 3.84550956
[4,] 2.95139356 3.27139356
[5,] 2.98550956 2.95139356
[6,] 2.82550956 2.98550956
[7,] 2.87962555 2.82550956
[8,] 3.14962555 2.87962555
[9,] 3.12962555 3.14962555
[10,] 3.24962555 3.12962555
[11,] 3.55962555 3.24962555
[12,] 3.66962555 3.55962555
[13,] 3.72550956 3.66962555
[14,] 3.86550956 3.72550956
[15,] 3.76962555 3.86550956
[16,] 3.83374155 3.76962555
[17,] 3.90785754 3.83374155
[18,] 3.83785754 3.90785754
[19,] 3.66785754 3.83785754
[20,] 3.75608953 3.66785754
[21,] 3.89432152 3.75608953
[22,] 3.72843752 3.89432152
[23,] 3.73255351 3.72843752
[24,] 3.70666951 3.73255351
[25,] 3.68666951 3.70666951
[26,] 3.68666951 3.68666951
[27,] 3.59666951 3.68666951
[28,] 3.40666951 3.59666951
[29,] 2.67255351 3.40666951
[30,] 2.56255351 2.67255351
[31,] 2.62843752 2.56255351
[32,] 2.58843752 2.62843752
[33,] 2.20843752 2.58843752
[34,] 1.81843752 2.20843752
[35,] 1.76843752 1.81843752
[36,] 1.94255351 1.76843752
[37,] 1.88255351 1.94255351
[38,] 1.37255351 1.88255351
[39,] 0.18255351 1.37255351
[40,] 0.30843752 0.18255351
[41,] 0.40843752 0.30843752
[42,] 0.05843752 0.40843752
[43,] -0.18744649 0.05843752
[44,] -0.06744649 -0.18744649
[45,] -0.01744649 -0.06744649
[46,] 0.04666951 -0.01744649
[47,] 0.22666951 0.04666951
[48,] 0.28078550 0.22666951
[49,] 0.28490150 0.28078550
[50,] 0.19490150 0.28490150
[51,] 0.16901749 0.19490150
[52,] 0.19901749 0.16901749
[53,] 0.31313349 0.19901749
[54,] 0.34724948 0.31313349
[55,] 0.23724948 0.34724948
[56,] 0.52136548 0.23724948
[57,] 1.04548147 0.52136548
[58,] 0.81959747 1.04548147
[59,] 0.86782946 0.81959747
[60,] 0.82194545 0.86782946
[61,] 0.50606145 0.82194545
[62,] 0.52429344 0.50606145
[63,] 0.77252543 0.52429344
[64,] 0.97664142 0.77252543
[65,] 1.06075742 0.97664142
[66,] 1.32898941 1.06075742
[67,] 1.50310540 1.32898941
[68,] 1.60722140 1.50310540
[69,] 1.52133739 1.60722140
[70,] 1.54545339 1.52133739
[71,] 1.69956938 1.54545339
[72,] 1.79368538 1.69956938
[73,] 1.92368538 1.79368538
[74,] 2.19780137 1.92368538
[75,] 2.17191737 2.19780137
[76,] 2.17603336 2.17191737
[77,] 2.23014936 2.17603336
[78,] 2.33426535 2.23014936
[79,] 2.36838135 2.33426535
[80,] 2.43838135 2.36838135
[81,] 2.75249734 2.43838135
[82,] 3.17249734 2.75249734
[83,] 3.47249734 3.17249734
[84,] 3.93661334 3.47249734
[85,] 4.53661334 3.93661334
[86,] 4.46072933 4.53661334
[87,] 4.20072933 4.46072933
[88,] 4.02072933 4.20072933
[89,] 3.95072933 4.02072933
[90,] 3.76072933 3.95072933
[91,] 4.21072933 3.76072933
[92,] 4.50072933 4.21072933
[93,] 4.48484533 4.50072933
[94,] 4.21072933 4.48484533
[95,] 4.15072933 4.21072933
[96,] 4.07072933 4.15072933
[97,] 3.45072933 4.07072933
[98,] 3.36661334 3.45072933
[99,] 3.30661334 3.36661334
[100,] 3.21249734 3.30661334
[101,] 3.33249734 3.21249734
[102,] 3.47838135 3.33249734
[103,] 3.41838135 3.47838135
[104,] 3.16426535 3.41838135
[105,] 3.01014936 3.16426535
[106,] 2.96603336 3.01014936
[107,] 2.94603336 2.96603336
[108,] 2.47191737 2.94603336
[109,] 2.40780137 2.47191737
[110,] 2.49780137 2.40780137
[111,] 2.41956938 2.49780137
[112,] 2.32545339 2.41956938
[113,] 2.35133739 2.32545339
[114,] 2.21310540 2.35133739
[115,] 2.26898941 2.21310540
[116,] 1.88075742 2.26898941
[117,] 1.32664142 1.88075742
[118,] 0.95840943 1.32664142
[119,] 0.80429344 0.95840943
[120,] 0.29606145 0.80429344
[121,] 0.24782946 0.29606145
[122,] -0.09628654 0.24782946
[123,] -0.15451853 -0.09628654
[124,] -0.22863452 -0.15451853
[125,] -0.55686651 -0.22863452
[126,] -0.80098251 -0.55686651
[127,] -0.77098251 -0.80098251
[128,] -0.69509850 -0.77098251
[129,] -0.78921450 -0.69509850
[130,] -1.12333049 -0.78921450
[131,] -1.45333049 -1.12333049
[132,] -1.45921450 -1.45333049
[133,] -1.27921450 -1.45921450
[134,] -0.83921450 -1.27921450
[135,] -0.68921450 -0.83921450
[136,] -0.37509850 -0.68921450
[137,] 0.06490150 -0.37509850
[138,] 0.03490150 0.06490150
[139,] 0.29490150 0.03490150
[140,] 0.72901749 0.29490150
[141,] 0.59901749 0.72901749
[142,] 0.49901749 0.59901749
[143,] 0.44901749 0.49901749
[144,] 0.59901749 0.44901749
[145,] 0.35490150 0.59901749
[146,] 0.24490150 0.35490150
[147,] -0.12921450 0.24490150
[148,] -0.38921450 -0.12921450
[149,] -0.71333049 -0.38921450
[150,] -0.71333049 -0.71333049
[151,] -0.85333049 -0.71333049
[152,] -0.93921450 -0.85333049
[153,] -0.85509850 -0.93921450
[154,] -1.11509850 -0.85509850
[155,] -1.15098251 -1.11509850
[156,] -1.34686651 -1.15098251
[157,] -1.08275052 -1.34686651
[158,] -0.88275052 -1.08275052
[159,] -0.98863452 -0.88275052
[160,] -0.90451853 -0.98863452
[161,] -0.81451853 -0.90451853
[162,] -0.81451853 -0.81451853
[163,] -0.91040253 -0.81451853
[164,] -1.08040253 -0.91040253
[165,] -1.43040253 -1.08040253
[166,] -1.53040253 -1.43040253
[167,] -1.69451853 -1.53040253
[168,] -1.76863452 -1.69451853
[169,] -2.00863452 -1.76863452
[170,] -1.80863452 -2.00863452
[171,] -1.63863452 -1.80863452
[172,] -1.75863452 -1.63863452
[173,] -1.89275052 -1.75863452
[174,] -2.08275052 -1.89275052
[175,] -1.97275052 -2.08275052
[176,] -2.09686651 -1.97275052
[177,] -2.09686651 -2.09686651
[178,] -2.04275052 -2.09686651
[179,] -2.27275052 -2.04275052
[180,] -2.43863452 -2.27275052
[181,] -2.61275052 -2.43863452
[182,] -2.69275052 -2.61275052
[183,] -2.56451853 -2.69275052
[184,] -2.44040253 -2.56451853
[185,] -2.57040253 -2.44040253
[186,] -2.65040253 -2.57040253
[187,] -2.80628654 -2.65040253
[188,] -3.12628654 -2.80628654
[189,] -3.12217054 -3.12628654
[190,] -3.00805455 -3.12217054
[191,] -3.18393855 -3.00805455
[192,] -3.24570656 -3.18393855
[193,] -2.97747457 -3.24570656
[194,] -2.62924258 -2.97747457
[195,] -2.65101059 -2.62924258
[196,] -2.40689460 -2.65101059
[197,] -1.92866261 -2.40689460
[198,] -1.50043062 -1.92866261
[199,] -1.13219863 -1.50043062
[200,] -0.84396664 -1.13219863
[201,] -0.60396664 -0.84396664
[202,] -0.75573465 -0.60396664
[203,] -0.64573465 -0.75573465
[204,] -0.20161865 -0.64573465
[205,] -0.26573465 -0.20161865
[206,] -0.37161865 -0.26573465
[207,] -0.47161865 -0.37161865
[208,] -0.30161865 -0.47161865
[209,] -0.33338666 -0.30161865
[210,] -0.13515467 -0.33338666
[211,] -0.17515467 -0.13515467
[212,] -0.10515467 -0.17515467
[213,] -0.28338666 -0.10515467
[214,] -0.40750266 -0.28338666
[215,] -0.58338666 -0.40750266
[216,] -0.57515467 -0.58338666
[217,] -0.57515467 -0.57515467
[218,] -0.78338666 -0.57515467
[219,] -0.97396664 -0.78338666
[220,] -0.86808263 -0.97396664
[221,] -1.04631462 -0.86808263
[222,] -1.02631462 -1.04631462
[223,] -1.29043062 -1.02631462
[224,] -1.30043062 -1.29043062
[225,] -1.48631462 -1.30043062
[226,] -1.69043062 -1.48631462
[227,] -1.53454661 -1.69043062
[228,] -1.55277860 -1.53454661
[229,] -1.48277860 -1.55277860
[230,] -1.26277860 -1.48277860
[231,] -1.26277860 -1.26277860
[232,] -1.25277860 -1.26277860
[233,] -1.45689460 -1.25277860
[234,] -1.73512659 -1.45689460
[235,] -2.08924258 -1.73512659
[236,] -2.28924258 -2.08924258
[237,] -2.29335858 -2.28924258
[238,] -2.31335858 -2.29335858
[239,] -2.49335858 -2.31335858
[240,] -2.60924258 -2.49335858
[241,] -2.77924258 -2.60924258
[242,] -2.80335858 -2.77924258
[243,] -2.72747457 -2.80335858
[244,] -3.06747457 -2.72747457
[245,] -3.34159057 -3.06747457
[246,] -3.08570656 -3.34159057
[247,] -2.92570656 -3.08570656
[248,] -2.83159057 -2.92570656
[249,] -2.51512659 -2.83159057
[250,] -2.33101059 -2.51512659
[251,] -2.50924258 -2.33101059
[252,] -2.88570656 -2.50924258
[253,] -3.07805455 -2.88570656
[254,] -3.26805455 -3.07805455
[255,] -2.94982256 -3.26805455
[256,] -2.61747457 -2.94982256
[257,] -2.55747457 -2.61747457
[258,] -2.67747457 -2.55747457
[259,] -2.90159057 -2.67747457
[260,] -2.97159057 -2.90159057
[261,] -3.16570656 -2.97159057
[262,] -3.35982256 -3.16570656
[263,] -3.54982256 -3.35982256
[264,] -3.61982256 -3.54982256
[265,] -3.63982256 -3.61982256
[266,] -3.44982256 -3.63982256
[267,] -3.54570656 -3.44982256
[268,] -3.65159057 -3.54570656
[269,] -3.82159057 -3.65159057
[270,] -3.78159057 -3.82159057
[271,] -3.83570656 -3.78159057
[272,] -4.00570656 -3.83570656
[273,] -3.90982256 -4.00570656
[274,] -3.71982256 -3.90982256
[275,] -3.69159057 -3.71982256
[276,] -3.58335858 -3.69159057
[277,] -3.28512659 -3.58335858
[278,] -3.12512659 -3.28512659
[279,] -2.86512659 -3.12512659
[280,] -2.79512659 -2.86512659
[281,] -2.74101059 -2.79512659
[282,] -2.72101059 -2.74101059
[283,] -2.77689460 -2.72101059
[284,] -2.90689460 -2.77689460
[285,] -2.99512659 -2.90689460
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.68550956 3.79962555
2 3.84550956 3.68550956
3 3.27139356 3.84550956
4 2.95139356 3.27139356
5 2.98550956 2.95139356
6 2.82550956 2.98550956
7 2.87962555 2.82550956
8 3.14962555 2.87962555
9 3.12962555 3.14962555
10 3.24962555 3.12962555
11 3.55962555 3.24962555
12 3.66962555 3.55962555
13 3.72550956 3.66962555
14 3.86550956 3.72550956
15 3.76962555 3.86550956
16 3.83374155 3.76962555
17 3.90785754 3.83374155
18 3.83785754 3.90785754
19 3.66785754 3.83785754
20 3.75608953 3.66785754
21 3.89432152 3.75608953
22 3.72843752 3.89432152
23 3.73255351 3.72843752
24 3.70666951 3.73255351
25 3.68666951 3.70666951
26 3.68666951 3.68666951
27 3.59666951 3.68666951
28 3.40666951 3.59666951
29 2.67255351 3.40666951
30 2.56255351 2.67255351
31 2.62843752 2.56255351
32 2.58843752 2.62843752
33 2.20843752 2.58843752
34 1.81843752 2.20843752
35 1.76843752 1.81843752
36 1.94255351 1.76843752
37 1.88255351 1.94255351
38 1.37255351 1.88255351
39 0.18255351 1.37255351
40 0.30843752 0.18255351
41 0.40843752 0.30843752
42 0.05843752 0.40843752
43 -0.18744649 0.05843752
44 -0.06744649 -0.18744649
45 -0.01744649 -0.06744649
46 0.04666951 -0.01744649
47 0.22666951 0.04666951
48 0.28078550 0.22666951
49 0.28490150 0.28078550
50 0.19490150 0.28490150
51 0.16901749 0.19490150
52 0.19901749 0.16901749
53 0.31313349 0.19901749
54 0.34724948 0.31313349
55 0.23724948 0.34724948
56 0.52136548 0.23724948
57 1.04548147 0.52136548
58 0.81959747 1.04548147
59 0.86782946 0.81959747
60 0.82194545 0.86782946
61 0.50606145 0.82194545
62 0.52429344 0.50606145
63 0.77252543 0.52429344
64 0.97664142 0.77252543
65 1.06075742 0.97664142
66 1.32898941 1.06075742
67 1.50310540 1.32898941
68 1.60722140 1.50310540
69 1.52133739 1.60722140
70 1.54545339 1.52133739
71 1.69956938 1.54545339
72 1.79368538 1.69956938
73 1.92368538 1.79368538
74 2.19780137 1.92368538
75 2.17191737 2.19780137
76 2.17603336 2.17191737
77 2.23014936 2.17603336
78 2.33426535 2.23014936
79 2.36838135 2.33426535
80 2.43838135 2.36838135
81 2.75249734 2.43838135
82 3.17249734 2.75249734
83 3.47249734 3.17249734
84 3.93661334 3.47249734
85 4.53661334 3.93661334
86 4.46072933 4.53661334
87 4.20072933 4.46072933
88 4.02072933 4.20072933
89 3.95072933 4.02072933
90 3.76072933 3.95072933
91 4.21072933 3.76072933
92 4.50072933 4.21072933
93 4.48484533 4.50072933
94 4.21072933 4.48484533
95 4.15072933 4.21072933
96 4.07072933 4.15072933
97 3.45072933 4.07072933
98 3.36661334 3.45072933
99 3.30661334 3.36661334
100 3.21249734 3.30661334
101 3.33249734 3.21249734
102 3.47838135 3.33249734
103 3.41838135 3.47838135
104 3.16426535 3.41838135
105 3.01014936 3.16426535
106 2.96603336 3.01014936
107 2.94603336 2.96603336
108 2.47191737 2.94603336
109 2.40780137 2.47191737
110 2.49780137 2.40780137
111 2.41956938 2.49780137
112 2.32545339 2.41956938
113 2.35133739 2.32545339
114 2.21310540 2.35133739
115 2.26898941 2.21310540
116 1.88075742 2.26898941
117 1.32664142 1.88075742
118 0.95840943 1.32664142
119 0.80429344 0.95840943
120 0.29606145 0.80429344
121 0.24782946 0.29606145
122 -0.09628654 0.24782946
123 -0.15451853 -0.09628654
124 -0.22863452 -0.15451853
125 -0.55686651 -0.22863452
126 -0.80098251 -0.55686651
127 -0.77098251 -0.80098251
128 -0.69509850 -0.77098251
129 -0.78921450 -0.69509850
130 -1.12333049 -0.78921450
131 -1.45333049 -1.12333049
132 -1.45921450 -1.45333049
133 -1.27921450 -1.45921450
134 -0.83921450 -1.27921450
135 -0.68921450 -0.83921450
136 -0.37509850 -0.68921450
137 0.06490150 -0.37509850
138 0.03490150 0.06490150
139 0.29490150 0.03490150
140 0.72901749 0.29490150
141 0.59901749 0.72901749
142 0.49901749 0.59901749
143 0.44901749 0.49901749
144 0.59901749 0.44901749
145 0.35490150 0.59901749
146 0.24490150 0.35490150
147 -0.12921450 0.24490150
148 -0.38921450 -0.12921450
149 -0.71333049 -0.38921450
150 -0.71333049 -0.71333049
151 -0.85333049 -0.71333049
152 -0.93921450 -0.85333049
153 -0.85509850 -0.93921450
154 -1.11509850 -0.85509850
155 -1.15098251 -1.11509850
156 -1.34686651 -1.15098251
157 -1.08275052 -1.34686651
158 -0.88275052 -1.08275052
159 -0.98863452 -0.88275052
160 -0.90451853 -0.98863452
161 -0.81451853 -0.90451853
162 -0.81451853 -0.81451853
163 -0.91040253 -0.81451853
164 -1.08040253 -0.91040253
165 -1.43040253 -1.08040253
166 -1.53040253 -1.43040253
167 -1.69451853 -1.53040253
168 -1.76863452 -1.69451853
169 -2.00863452 -1.76863452
170 -1.80863452 -2.00863452
171 -1.63863452 -1.80863452
172 -1.75863452 -1.63863452
173 -1.89275052 -1.75863452
174 -2.08275052 -1.89275052
175 -1.97275052 -2.08275052
176 -2.09686651 -1.97275052
177 -2.09686651 -2.09686651
178 -2.04275052 -2.09686651
179 -2.27275052 -2.04275052
180 -2.43863452 -2.27275052
181 -2.61275052 -2.43863452
182 -2.69275052 -2.61275052
183 -2.56451853 -2.69275052
184 -2.44040253 -2.56451853
185 -2.57040253 -2.44040253
186 -2.65040253 -2.57040253
187 -2.80628654 -2.65040253
188 -3.12628654 -2.80628654
189 -3.12217054 -3.12628654
190 -3.00805455 -3.12217054
191 -3.18393855 -3.00805455
192 -3.24570656 -3.18393855
193 -2.97747457 -3.24570656
194 -2.62924258 -2.97747457
195 -2.65101059 -2.62924258
196 -2.40689460 -2.65101059
197 -1.92866261 -2.40689460
198 -1.50043062 -1.92866261
199 -1.13219863 -1.50043062
200 -0.84396664 -1.13219863
201 -0.60396664 -0.84396664
202 -0.75573465 -0.60396664
203 -0.64573465 -0.75573465
204 -0.20161865 -0.64573465
205 -0.26573465 -0.20161865
206 -0.37161865 -0.26573465
207 -0.47161865 -0.37161865
208 -0.30161865 -0.47161865
209 -0.33338666 -0.30161865
210 -0.13515467 -0.33338666
211 -0.17515467 -0.13515467
212 -0.10515467 -0.17515467
213 -0.28338666 -0.10515467
214 -0.40750266 -0.28338666
215 -0.58338666 -0.40750266
216 -0.57515467 -0.58338666
217 -0.57515467 -0.57515467
218 -0.78338666 -0.57515467
219 -0.97396664 -0.78338666
220 -0.86808263 -0.97396664
221 -1.04631462 -0.86808263
222 -1.02631462 -1.04631462
223 -1.29043062 -1.02631462
224 -1.30043062 -1.29043062
225 -1.48631462 -1.30043062
226 -1.69043062 -1.48631462
227 -1.53454661 -1.69043062
228 -1.55277860 -1.53454661
229 -1.48277860 -1.55277860
230 -1.26277860 -1.48277860
231 -1.26277860 -1.26277860
232 -1.25277860 -1.26277860
233 -1.45689460 -1.25277860
234 -1.73512659 -1.45689460
235 -2.08924258 -1.73512659
236 -2.28924258 -2.08924258
237 -2.29335858 -2.28924258
238 -2.31335858 -2.29335858
239 -2.49335858 -2.31335858
240 -2.60924258 -2.49335858
241 -2.77924258 -2.60924258
242 -2.80335858 -2.77924258
243 -2.72747457 -2.80335858
244 -3.06747457 -2.72747457
245 -3.34159057 -3.06747457
246 -3.08570656 -3.34159057
247 -2.92570656 -3.08570656
248 -2.83159057 -2.92570656
249 -2.51512659 -2.83159057
250 -2.33101059 -2.51512659
251 -2.50924258 -2.33101059
252 -2.88570656 -2.50924258
253 -3.07805455 -2.88570656
254 -3.26805455 -3.07805455
255 -2.94982256 -3.26805455
256 -2.61747457 -2.94982256
257 -2.55747457 -2.61747457
258 -2.67747457 -2.55747457
259 -2.90159057 -2.67747457
260 -2.97159057 -2.90159057
261 -3.16570656 -2.97159057
262 -3.35982256 -3.16570656
263 -3.54982256 -3.35982256
264 -3.61982256 -3.54982256
265 -3.63982256 -3.61982256
266 -3.44982256 -3.63982256
267 -3.54570656 -3.44982256
268 -3.65159057 -3.54570656
269 -3.82159057 -3.65159057
270 -3.78159057 -3.82159057
271 -3.83570656 -3.78159057
272 -4.00570656 -3.83570656
273 -3.90982256 -4.00570656
274 -3.71982256 -3.90982256
275 -3.69159057 -3.71982256
276 -3.58335858 -3.69159057
277 -3.28512659 -3.58335858
278 -3.12512659 -3.28512659
279 -2.86512659 -3.12512659
280 -2.79512659 -2.86512659
281 -2.74101059 -2.79512659
282 -2.72101059 -2.74101059
283 -2.77689460 -2.72101059
284 -2.90689460 -2.77689460
285 -2.99512659 -2.90689460
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/75y5h1292973210.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/85y5h1292973210.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9gp4k1292973210.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10gp4k1292973210.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1118l81292973210.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/125q1w1292973210.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13ury81292973210.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1440xb1292973210.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/1581ez1292973210.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16mtcp1292973210.tab")
+ }
>
> try(system("convert tmp/1ropr1292973210.ps tmp/1ropr1292973210.png",intern=TRUE))
character(0)
> try(system("convert tmp/22xpu1292973210.ps tmp/22xpu1292973210.png",intern=TRUE))
character(0)
> try(system("convert tmp/32xpu1292973210.ps tmp/32xpu1292973210.png",intern=TRUE))
character(0)
> try(system("convert tmp/42xpu1292973210.ps tmp/42xpu1292973210.png",intern=TRUE))
character(0)
> try(system("convert tmp/5v7oe1292973210.ps tmp/5v7oe1292973210.png",intern=TRUE))
character(0)
> try(system("convert tmp/6v7oe1292973210.ps tmp/6v7oe1292973210.png",intern=TRUE))
character(0)
> try(system("convert tmp/75y5h1292973210.ps tmp/75y5h1292973210.png",intern=TRUE))
character(0)
> try(system("convert tmp/85y5h1292973210.ps tmp/85y5h1292973210.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gp4k1292973210.ps tmp/9gp4k1292973210.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gp4k1292973210.ps tmp/10gp4k1292973210.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.730 1.330 8.067