R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
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+ ,0
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+ ,6)
+ ,dim=c(9
+ ,275)
+ ,dimnames=list(c('illness'
+ ,'sports.competition'
+ ,'addictive.drugs'
+ ,'fruits'
+ ,'fish'
+ ,'high.alcohol'
+ ,'milk'
+ ,'Gender'
+ ,'score')
+ ,1:275))
> y <- array(NA,dim=c(9,275),dimnames=list(c('illness','sports.competition','addictive.drugs','fruits','fish','high.alcohol','milk','Gender','score'),1:275))
> 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 = '9'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '9'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
score illness sports.competition addictive.drugs fruits fish high.alcohol
1 11 0 1 1 4 4 2
2 13 0 1 1 3 2 3
3 7 0 0 1 4 4 3
4 10 0 0 1 3 3 2
5 10 0 0 1 5 4 1
6 5 0 0 1 3 3 3
7 7 1 0 1 5 3 2
8 9 0 1 1 3 3 2
9 14 0 0 2 5 4 3
10 8 1 0 1 3 3 4
11 14 0 1 1 4 4 1
12 11 0 1 1 4 3 2
13 9 0 1 2 4 4 4
14 12 0 0 1 3 2 2
15 16 0 1 1 5 4 3
16 10 0 0 1 4 3 3
17 8 0 0 1 3 3 1
18 9 0 1 1 3 4 3
19 7 0 0 2 3 3 3
20 13 0 0 1 4 3 2
21 10 0 1 2 5 5 2
22 12 1 1 1 4 4 2
23 10 1 1 4 5 4 2
24 3 0 1 1 3 3 4
25 8 1 1 1 4 5 3
26 15 1 0 1 2 2 1
27 13 1 1 1 5 4 2
28 9 0 0 1 2 3 1
29 12 0 1 1 3 2 3
30 12 0 1 1 3 3 2
31 9 0 1 1 3 4 1
32 9 0 1 1 3 4 3
33 10 0 0 1 4 1 2
34 8 0 0 1 3 2 3
35 8 0 1 1 5 5 1
36 8 0 0 1 2 2 3
37 12 0 1 1 3 4 3
38 9 1 1 1 3 3 2
39 8 1 1 1 4 1 3
40 15 0 1 1 5 3 3
41 9 0 1 1 3 4 3
42 6 0 0 3 3 3 3
43 13 0 0 2 3 4 3
44 9 0 1 1 3 1 3
45 12 1 1 1 3 4 2
46 17 0 0 1 4 3 1
47 13 0 0 1 3 4 3
48 11 0 0 1 2 3 3
49 10 0 1 1 3 3 1
50 7 1 0 1 3 1 1
51 14 0 1 1 3 2 1
52 11 0 1 1 4 5 4
53 9 0 1 1 2 4 3
54 8 0 0 1 5 3 1
55 12 0 1 1 4 3 2
56 13 0 0 2 2 3 4
57 2 0 1 1 3 4 3
58 18 0 0 1 4 3 2
59 11 0 1 1 3 4 3
60 10 0 0 1 3 3 3
61 13 0 1 1 4 3 2
62 6 0 0 1 3 4 3
63 8 0 0 1 3 4 1
64 12 0 1 1 4 3 3
65 12 0 0 1 2 3 2
66 14 0 0 1 2 4 2
67 8 0 0 1 4 3 4
68 7 0 0 1 2 4 2
69 10 0 1 1 4 3 3
70 10 1 1 1 3 4 2
71 14 0 1 1 5 5 4
72 16 0 1 1 3 2 2
73 14 0 1 1 3 2 3
74 6 0 1 2 4 4 3
75 10 0 0 1 3 2 1
76 8 0 0 1 3 4 2
77 9 0 0 1 4 2 2
78 7 0 1 1 3 4 3
79 11 0 0 1 3 3 1
80 13 0 1 3 3 3 2
81 12 0 0 1 3 4 3
82 12 0 1 2 5 3 2
83 11 0 1 1 3 3 3
84 6 0 1 1 5 4 4
85 10 0 0 1 4 4 3
86 5 0 1 1 3 4 3
87 8 1 0 1 3 4 3
88 13 0 1 1 5 4 2
89 18 0 0 1 4 4 3
90 15 0 1 2 5 4 3
91 13 0 1 3 4 4 3
92 7 0 0 1 2 4 3
93 9 0 1 1 5 4 2
94 9 0 1 1 4 3 2
95 7 0 0 1 2 4 4
96 13 0 1 1 2 2 2
97 6 1 0 1 3 4 1
98 7 0 0 1 4 4 3
99 15 0 0 1 2 5 2
100 11 0 0 1 4 1 1
101 11 0 1 1 3 2 3
102 7 0 1 1 3 3 2
103 8 0 1 3 3 3 2
104 7 0 1 1 4 4 3
105 13 0 1 1 3 3 2
106 9 0 0 3 3 4 3
107 15 1 1 2 4 4 1
108 10 0 1 1 3 3 3
109 11 0 0 1 4 4 2
110 15 0 1 1 3 4 3
111 8 0 1 2 4 3 2
112 9 0 1 3 4 4 2
113 8 1 1 1 3 3 3
114 19 0 1 1 5 4 2
115 8 0 0 1 3 3 2
116 10 0 1 1 4 2 2
117 15 0 1 1 4 4 3
118 8 0 1 1 2 4 2
119 7 0 1 1 4 3 4
120 15 0 1 1 4 4 1
121 9 0 1 1 4 4 3
122 8 0 0 1 3 4 1
123 11 0 1 1 5 3 2
124 9 0 1 1 4 3 4
125 11 1 0 3 3 4 3
126 10 1 0 2 3 4 2
127 8 0 0 1 4 4 1
128 7 0 1 1 5 4 2
129 8 0 0 1 3 4 3
130 9 0 1 1 3 4 2
131 11 0 1 1 2 3 3
132 12 1 1 1 5 4 3
133 14 0 0 1 3 4 3
134 10 0 1 1 3 3 3
135 10 0 1 1 4 3 3
136 9 0 1 1 2 3 1
137 12 0 0 2 4 4 3
138 5 1 1 1 3 3 4
139 14 0 0 1 3 1 3
140 14 1 0 1 5 1 2
141 13 0 1 1 5 3 5
142 17 0 1 1 3 3 2
143 11 0 1 1 4 4 1
144 8 0 1 1 5 3 2
145 10 0 1 1 2 4 2
146 5 0 0 1 4 3 1
147 6 1 0 2 2 3 1
148 5 0 0 1 4 4 2
149 10 0 0 1 3 2 2
150 9 0 0 1 4 4 4
151 10 0 0 1 4 4 1
152 9 0 1 1 4 4 1
153 8 0 1 1 5 5 3
154 7 0 0 1 5 5 3
155 13 0 1 1 4 3 4
156 11 0 0 1 3 4 2
157 11 0 0 1 4 4 3
158 11 0 0 2 5 1 1
159 12 0 1 1 4 3 4
160 11 0 1 1 5 5 5
161 10 0 1 1 4 4 2
162 9 0 0 1 2 2 3
163 13 0 1 1 2 1 2
164 10 1 1 1 5 4 2
165 7 0 1 1 3 4 4
166 10 0 1 1 3 3 3
167 11 1 1 1 3 3 2
168 14 0 0 1 3 4 2
169 10 0 1 1 4 4 1
170 12 0 0 1 4 2 3
171 13 0 0 1 3 4 2
172 8 0 0 1 5 2 2
173 8 0 1 1 3 4 3
174 8 0 0 1 4 4 2
175 11 0 0 1 3 4 2
176 11 0 0 1 4 4 2
177 9 0 1 1 5 4 1
178 12 0 0 1 3 4 1
179 7 0 1 1 5 4 1
180 7 1 1 1 3 3 3
181 9 0 0 1 4 2 3
182 11 1 0 1 5 3 2
183 8 0 0 1 4 3 2
184 10 0 1 1 4 2 5
185 7 1 0 1 3 4 3
186 6 0 1 1 5 1 2
187 8 0 1 1 4 4 1
188 11 0 1 1 4 4 3
189 11 0 1 1 3 3 2
190 13 0 0 1 5 4 2
191 13 0 1 1 3 3 2
192 11 1 1 1 4 4 3
193 7 0 1 3 4 4 3
194 7 0 1 1 3 4 2
195 10 0 1 1 3 4 4
196 8 0 0 1 5 1 1
197 8 0 0 1 4 4 1
198 9 0 0 1 4 3 1
199 9 0 0 1 3 1 1
200 17 0 0 1 5 3 1
201 11 0 1 1 2 3 3
202 11 0 0 1 3 4 1
203 11 0 0 1 3 4 1
204 12 1 0 1 4 4 3
205 10 0 0 2 2 2 2
206 8 0 1 1 4 4 4
207 6 0 0 1 2 4 2
208 10 0 0 1 4 4 3
209 10 0 1 1 4 4 3
210 4 0 0 1 5 4 2
211 10 0 1 1 5 4 2
212 7 1 0 1 2 2 1
213 12 0 1 1 3 4 3
214 7 0 0 1 4 3 2
215 12 1 1 1 4 2 3
216 9 0 1 1 4 4 3
217 10 0 0 1 3 4 2
218 11 0 1 1 3 2 3
219 12 0 1 1 3 4 2
220 12 0 1 1 3 2 1
221 2 0 1 2 2 2 3
222 9 0 1 3 3 3 3
223 9 0 0 1 4 3 3
224 4 0 1 1 3 3 2
225 9 0 1 1 5 5 4
226 8 0 0 1 2 3 2
227 7 1 1 1 2 4 4
228 10 0 0 1 4 3 3
229 14 0 0 1 4 3 2
230 11 0 1 1 4 3 4
231 8 0 0 1 4 2 1
232 8 0 1 1 5 4 2
233 13 0 1 4 5 4 3
234 6 0 0 1 5 4 3
235 10 0 0 1 3 3 1
236 9 0 1 1 3 3 1
237 11 0 0 1 4 4 2
238 8 0 0 1 4 4 4
239 6 0 0 1 4 4 2
240 9 0 0 1 4 3 2
241 8 0 0 1 3 2 1
242 11 1 0 1 5 2 3
243 10 0 0 1 2 4 2
244 7 0 1 1 4 4 3
245 7 0 0 1 5 2 2
246 8 0 1 1 2 4 3
247 7 0 0 1 2 4 4
248 10 0 0 3 3 1 3
249 9 0 1 1 5 4 2
250 13 1 1 1 4 1 3
251 11 0 0 1 4 5 3
252 6 0 0 1 5 5 1
253 8 0 1 1 2 3 3
254 8 0 1 1 3 4 2
255 7 0 0 1 5 3 1
256 11 0 1 1 3 4 4
257 10 0 0 1 4 3 2
258 8 0 1 1 3 3 3
259 7 0 0 1 4 4 3
260 7 0 0 1 5 5 1
261 9 0 1 1 4 3 3
262 11 0 1 1 4 4 4
263 11 0 0 2 4 4 3
264 11 0 1 1 4 4 3
265 10 0 0 1 3 3 3
266 10 0 1 1 3 4 2
267 10 0 1 1 4 3 4
268 8 0 0 1 5 5 5
269 11 0 1 1 3 3 3
270 8 0 1 1 4 4 4
271 4 0 1 1 3 1 3
272 6 0 1 1 3 4 4
273 11 0 0 3 3 3 4
274 7 0 0 3 3 3 2
275 6 0 1 1 3 3 3
milk Gender
1 5 1
2 4 1
3 5 0
4 2 0
5 3 0
6 2 0
7 2 1
8 4 1
9 3 0
10 3 1
11 5 1
12 5 1
13 2 0
14 2 0
15 5 1
16 4 0
17 2 0
18 2 1
19 4 0
20 5 0
21 5 1
22 1 0
23 4 1
24 5 1
25 4 1
26 1 0
27 5 0
28 2 0
29 4 1
30 1 0
31 4 1
32 4 1
33 3 0
34 4 1
35 5 0
36 2 1
37 4 0
38 5 1
39 4 0
40 3 1
41 4 1
42 3 0
43 4 1
44 4 1
45 4 1
46 3 0
47 3 0
48 2 1
49 4 1
50 2 1
51 4 0
52 4 1
53 2 0
54 5 0
55 5 1
56 5 1
57 5 1
58 4 0
59 2 1
60 5 1
61 4 1
62 4 1
63 3 0
64 3 1
65 2 1
66 2 1
67 1 0
68 2 0
69 2 1
70 2 1
71 4 1
72 4 1
73 2 1
74 4 1
75 5 0
76 5 1
77 5 1
78 3 1
79 2 0
80 3 1
81 4 0
82 2 0
83 5 1
84 5 1
85 4 0
86 3 1
87 4 1
88 3 1
89 3 0
90 4 1
91 1 1
92 4 0
93 1 0
94 4 1
95 3 1
96 1 1
97 1 0
98 3 0
99 3 1
100 1 0
101 1 1
102 1 1
103 3 1
104 4 1
105 2 1
106 3 1
107 1 1
108 3 1
109 3 0
110 5 0
111 4 0
112 4 0
113 1 0
114 3 1
115 2 1
116 2 0
117 4 1
118 4 1
119 5 0
120 4 1
121 1 1
122 5 0
123 2 0
124 2 1
125 4 1
126 1 1
127 2 0
128 5 0
129 2 0
130 3 1
131 3 1
132 5 1
133 4 1
134 2 0
135 2 0
136 2 1
137 1 1
138 3 1
139 1 0
140 4 0
141 5 0
142 3 1
143 2 0
144 3 0
145 5 1
146 1 0
147 3 0
148 1 0
149 2 1
150 1 0
151 5 0
152 5 1
153 3 0
154 4 1
155 5 1
156 3 1
157 3 0
158 2 0
159 5 1
160 2 0
161 2 1
162 5 0
163 3 1
164 3 1
165 3 1
166 2 1
167 3 1
168 1 0
169 2 1
170 2 0
171 3 0
172 4 0
173 4 0
174 4 0
175 1 1
176 3 1
177 4 0
178 5 0
179 5 1
180 3 1
181 3 0
182 2 0
183 4 0
184 5 1
185 1 0
186 4 1
187 2 0
188 4 1
189 2 1
190 3 0
191 4 1
192 2 0
193 2 1
194 5 0
195 5 1
196 3 0
197 3 0
198 5 0
199 3 0
200 4 0
201 2 1
202 5 0
203 3 0
204 4 1
205 2 0
206 3 1
207 4 0
208 5 0
209 4 0
210 5 0
211 2 0
212 4 0
213 5 1
214 4 0
215 3 0
216 5 1
217 3 0
218 5 1
219 3 0
220 3 1
221 4 1
222 3 1
223 2 0
224 2 0
225 5 1
226 1 1
227 1 0
228 4 0
229 3 1
230 2 1
231 2 0
232 5 0
233 4 0
234 4 0
235 2 0
236 4 0
237 2 0
238 5 1
239 2 0
240 2 1
241 4 0
242 4 1
243 3 1
244 5 0
245 3 0
246 4 1
247 1 1
248 3 0
249 1 1
250 1 1
251 3 0
252 5 0
253 2 0
254 1 1
255 1 0
256 1 1
257 4 0
258 3 1
259 4 1
260 4 1
261 2 1
262 2 1
263 5 1
264 2 0
265 3 1
266 4 0
267 2 1
268 5 1
269 2 1
270 4 1
271 2 1
272 2 1
273 2 1
274 2 0
275 3 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) illness sports.competition addictive.drugs
9.46356 -0.20271 0.25877 0.07930
fruits fish high.alcohol milk
0.37434 -0.18276 -0.27845 -0.06312
Gender
0.62518
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.8014 -1.8357 -0.1358 1.6226 8.7155
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.46356 1.03514 9.142 <2e-16 ***
illness -0.20271 0.52264 -0.388 0.6984
sports.competition 0.25877 0.37503 0.690 0.4908
addictive.drugs 0.07930 0.32607 0.243 0.8080
fruits 0.37434 0.19855 1.885 0.0605 .
fish -0.18276 0.18835 -0.970 0.3328
high.alcohol -0.27845 0.19007 -1.465 0.1441
milk -0.06312 0.13689 -0.461 0.6451
Gender 0.62518 0.38820 1.610 0.1085
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.822 on 266 degrees of freedom
Multiple R-squared: 0.03146, Adjusted R-squared: 0.002336
F-statistic: 1.08 on 8 and 266 DF, p-value: 0.3772
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.5678189 0.864362189 0.432181094
[2,] 0.3981983 0.796396646 0.601801677
[3,] 0.2643209 0.528641880 0.735679060
[4,] 0.6490427 0.701914511 0.350957256
[5,] 0.5374024 0.925195179 0.462597590
[6,] 0.4286927 0.857385429 0.571307286
[7,] 0.3320214 0.664042824 0.667978588
[8,] 0.3725333 0.745066600 0.627466700
[9,] 0.3759384 0.751876897 0.624061552
[10,] 0.3397666 0.679533219 0.660233390
[11,] 0.3525096 0.705019161 0.647490420
[12,] 0.2785486 0.557097296 0.721451352
[13,] 0.6232250 0.753550026 0.376775013
[14,] 0.5529884 0.894023210 0.447011605
[15,] 0.7162840 0.567431958 0.283715979
[16,] 0.6771144 0.645771122 0.322885561
[17,] 0.6138883 0.772223339 0.386111670
[18,] 0.5561805 0.887638957 0.443819478
[19,] 0.4946054 0.989210862 0.505394569
[20,] 0.4393145 0.878628975 0.560685513
[21,] 0.3882845 0.776568950 0.611715525
[22,] 0.4149502 0.829900475 0.585049763
[23,] 0.3586283 0.717256686 0.641371657
[24,] 0.4424385 0.884876955 0.557561522
[25,] 0.3876096 0.775219194 0.612390403
[26,] 0.3769798 0.753959588 0.623020206
[27,] 0.3414893 0.682978533 0.658510733
[28,] 0.4218214 0.843642880 0.578178560
[29,] 0.4412781 0.882556134 0.558721933
[30,] 0.3883611 0.776722153 0.611638923
[31,] 0.3613564 0.722712768 0.638643616
[32,] 0.5028963 0.994207375 0.497103687
[33,] 0.4646589 0.929317838 0.535341081
[34,] 0.4486752 0.897350429 0.551324785
[35,] 0.6027416 0.794516769 0.397258385
[36,] 0.6552587 0.689482575 0.344741288
[37,] 0.6320706 0.735858733 0.367929366
[38,] 0.5941577 0.811684640 0.405842320
[39,] 0.6168081 0.766383768 0.383191884
[40,] 0.6204587 0.759082542 0.379541271
[41,] 0.5824949 0.835010176 0.417505088
[42,] 0.5393485 0.921302918 0.460651459
[43,] 0.5603553 0.879289369 0.439644685
[44,] 0.5223706 0.955258837 0.477629418
[45,] 0.6231389 0.753722291 0.376861146
[46,] 0.8404432 0.319113646 0.159556823
[47,] 0.9462385 0.107522979 0.053761489
[48,] 0.9348800 0.130240016 0.065120008
[49,] 0.9206657 0.158668671 0.079334336
[50,] 0.9124414 0.175117200 0.087558600
[51,] 0.9172538 0.165492429 0.082746215
[52,] 0.9111694 0.177661188 0.088830594
[53,] 0.8964393 0.207121407 0.103560703
[54,] 0.8900265 0.219947094 0.109973547
[55,] 0.9112479 0.177504228 0.088752114
[56,] 0.9023704 0.195259247 0.097629624
[57,] 0.8971399 0.205720185 0.102860092
[58,] 0.8797969 0.240406117 0.120203059
[59,] 0.8580332 0.283933527 0.141966764
[60,] 0.8707202 0.258559562 0.129279781
[61,] 0.9136028 0.172794352 0.086397176
[62,] 0.9174471 0.165105861 0.082552931
[63,] 0.9350774 0.129845285 0.064922642
[64,] 0.9221778 0.155644407 0.077822203
[65,] 0.9114500 0.177100000 0.088550000
[66,] 0.9001489 0.199702293 0.099851146
[67,] 0.9019373 0.196125392 0.098062696
[68,] 0.8860307 0.227938576 0.113969288
[69,] 0.8797520 0.240496040 0.120248020
[70,] 0.8836913 0.232617353 0.116308676
[71,] 0.8670174 0.265965207 0.132982603
[72,] 0.8487766 0.302446752 0.151223376
[73,] 0.8718954 0.256209160 0.128104580
[74,] 0.8518458 0.296308376 0.148154188
[75,] 0.8901796 0.219640746 0.109820373
[76,] 0.8735975 0.252804983 0.126402492
[77,] 0.8602869 0.279426204 0.139713102
[78,] 0.9623353 0.075329497 0.037664748
[79,] 0.9699890 0.060022042 0.030011021
[80,] 0.9670521 0.065895779 0.032947889
[81,] 0.9613498 0.077300368 0.038650184
[82,] 0.9616387 0.076722513 0.038361256
[83,] 0.9573310 0.085338084 0.042669042
[84,] 0.9514511 0.097097800 0.048548900
[85,] 0.9492428 0.101514327 0.050757164
[86,] 0.9549782 0.090043618 0.045021809
[87,] 0.9534985 0.093003020 0.046501510
[88,] 0.9749435 0.050113081 0.025056540
[89,] 0.9705747 0.058850645 0.029425323
[90,] 0.9644245 0.071151023 0.035575511
[91,] 0.9695139 0.060972244 0.030486122
[92,] 0.9685602 0.062879614 0.031439807
[93,] 0.9701696 0.059660829 0.029830415
[94,] 0.9689147 0.062170547 0.031085273
[95,] 0.9624251 0.075149705 0.037574852
[96,] 0.9700043 0.059991358 0.029995679
[97,] 0.9633707 0.073258643 0.036629321
[98,] 0.9572274 0.085545205 0.042772603
[99,] 0.9778019 0.044396271 0.022198135
[100,] 0.9766194 0.046761169 0.023380584
[101,] 0.9724191 0.055161765 0.027580882
[102,] 0.9674903 0.065019491 0.032509746
[103,] 0.9930334 0.013933214 0.006966607
[104,] 0.9924475 0.015105048 0.007552524
[105,] 0.9907504 0.018499105 0.009249552
[106,] 0.9943656 0.011268747 0.005634374
[107,] 0.9933340 0.013332074 0.006666037
[108,] 0.9927577 0.014484576 0.007242288
[109,] 0.9951021 0.009795840 0.004897920
[110,] 0.9942309 0.011538234 0.005769117
[111,] 0.9932396 0.013520816 0.006760408
[112,] 0.9918930 0.016213984 0.008106992
[113,] 0.9900901 0.019819861 0.009909930
[114,] 0.9888801 0.022239789 0.011119894
[115,] 0.9860509 0.027898239 0.013949120
[116,] 0.9853444 0.029311277 0.014655638
[117,] 0.9862806 0.027438890 0.013719445
[118,] 0.9834661 0.033067834 0.016533917
[119,] 0.9802550 0.039489924 0.019744962
[120,] 0.9771124 0.045775106 0.022887553
[121,] 0.9746622 0.050675567 0.025337783
[122,] 0.9819044 0.036191173 0.018095586
[123,] 0.9778347 0.044330547 0.022165273
[124,] 0.9728657 0.054268622 0.027134311
[125,] 0.9682451 0.063509795 0.031754897
[126,] 0.9652350 0.069530050 0.034765025
[127,] 0.9743001 0.051399850 0.025699925
[128,] 0.9809575 0.038085010 0.019042505
[129,] 0.9837413 0.032517390 0.016258695
[130,] 0.9860443 0.027911314 0.013955657
[131,] 0.9965508 0.006898311 0.003449155
[132,] 0.9959276 0.008144845 0.004072423
[133,] 0.9956725 0.008655037 0.004327519
[134,] 0.9945172 0.010965533 0.005482767
[135,] 0.9971912 0.005617682 0.002808841
[136,] 0.9974797 0.005040640 0.002520320
[137,] 0.9985737 0.002852675 0.001426338
[138,] 0.9981239 0.003752109 0.001876054
[139,] 0.9975189 0.004962225 0.002481113
[140,] 0.9968021 0.006395766 0.003197883
[141,] 0.9961236 0.007752805 0.003876402
[142,] 0.9953751 0.009249731 0.004624866
[143,] 0.9956209 0.008758135 0.004379068
[144,] 0.9963066 0.007386854 0.003693427
[145,] 0.9954818 0.009036360 0.004518180
[146,] 0.9946846 0.010630765 0.005315382
[147,] 0.9934784 0.013043172 0.006521586
[148,] 0.9933933 0.013213461 0.006606731
[149,] 0.9924774 0.015045285 0.007522643
[150,] 0.9905274 0.018945199 0.009472599
[151,] 0.9879965 0.024006962 0.012003481
[152,] 0.9901239 0.019752216 0.009876108
[153,] 0.9875457 0.024908651 0.012454325
[154,] 0.9865446 0.026910822 0.013455411
[155,] 0.9833577 0.033284641 0.016642320
[156,] 0.9799262 0.040147502 0.020073751
[157,] 0.9873221 0.025355722 0.012677861
[158,] 0.9843465 0.031307023 0.015653511
[159,] 0.9845451 0.030909733 0.015454867
[160,] 0.9884525 0.023095049 0.011547525
[161,] 0.9869986 0.026002831 0.013001416
[162,] 0.9838636 0.032272893 0.016136446
[163,] 0.9807604 0.038479300 0.019239650
[164,] 0.9775217 0.044956531 0.022478265
[165,] 0.9735309 0.052938242 0.026469121
[166,] 0.9683035 0.063392937 0.031696468
[167,] 0.9702698 0.059460460 0.029730230
[168,] 0.9736199 0.052760137 0.026380068
[169,] 0.9749720 0.050055916 0.025027958
[170,] 0.9694066 0.061186852 0.030593426
[171,] 0.9626761 0.074647801 0.037323900
[172,] 0.9562260 0.087547960 0.043773980
[173,] 0.9480941 0.103811712 0.051905856
[174,] 0.9475613 0.104877416 0.052438708
[175,] 0.9624314 0.075137118 0.037568559
[176,] 0.9575307 0.084938619 0.042469310
[177,] 0.9505746 0.098850824 0.049425412
[178,] 0.9432180 0.113563957 0.056781979
[179,] 0.9512035 0.097592925 0.048796462
[180,] 0.9587773 0.082445409 0.041222704
[181,] 0.9506064 0.098787247 0.049393623
[182,] 0.9556865 0.088627097 0.044313548
[183,] 0.9502543 0.099491483 0.049745742
[184,] 0.9415857 0.116828529 0.058414264
[185,] 0.9355804 0.128839277 0.064419639
[186,] 0.9258224 0.148355160 0.074177580
[187,] 0.9110259 0.177948161 0.088974081
[188,] 0.8947635 0.210472982 0.105236491
[189,] 0.9767221 0.046555736 0.023277868
[190,] 0.9748075 0.050384996 0.025192498
[191,] 0.9750565 0.049887036 0.024943518
[192,] 0.9751696 0.049660823 0.024830412
[193,] 0.9715129 0.056974211 0.028487106
[194,] 0.9670084 0.065983193 0.032991596
[195,] 0.9612840 0.077432035 0.038716017
[196,] 0.9560989 0.087802142 0.043901071
[197,] 0.9503921 0.099215720 0.049607860
[198,] 0.9415770 0.116845974 0.058422987
[199,] 0.9664983 0.067003388 0.033501694
[200,] 0.9578302 0.084339545 0.042169772
[201,] 0.9552698 0.089460412 0.044730206
[202,] 0.9609110 0.078178081 0.039089040
[203,] 0.9550554 0.089889265 0.044944633
[204,] 0.9490326 0.101934816 0.050967408
[205,] 0.9362914 0.127417101 0.063708550
[206,] 0.9278528 0.144294381 0.072147191
[207,] 0.9299383 0.140123406 0.070061703
[208,] 0.9478537 0.104292542 0.052146271
[209,] 0.9615648 0.076870363 0.038435181
[210,] 0.9927234 0.014553260 0.007276630
[211,] 0.9907850 0.018429947 0.009214973
[212,] 0.9874755 0.025048970 0.012524485
[213,] 0.9935549 0.012890281 0.006445141
[214,] 0.9908687 0.018262598 0.009131299
[215,] 0.9876120 0.024775963 0.012387981
[216,] 0.9921597 0.015680661 0.007840331
[217,] 0.9908496 0.018300719 0.009150360
[218,] 0.9988127 0.002374511 0.001187255
[219,] 0.9988149 0.002370294 0.001185147
[220,] 0.9982151 0.003569815 0.001784907
[221,] 0.9972723 0.005455497 0.002727748
[222,] 0.9966667 0.006666628 0.003333314
[223,] 0.9968200 0.006360097 0.003180048
[224,] 0.9960520 0.007896083 0.003948041
[225,] 0.9946240 0.010752067 0.005376034
[226,] 0.9946534 0.010693191 0.005346596
[227,] 0.9919012 0.016197502 0.008098751
[228,] 0.9929932 0.014013525 0.007006763
[229,] 0.9903200 0.019359917 0.009679959
[230,] 0.9868459 0.026308218 0.013154109
[231,] 0.9817839 0.036432171 0.018216085
[232,] 0.9819181 0.036163805 0.018081903
[233,] 0.9804476 0.039104703 0.019552352
[234,] 0.9721413 0.055717446 0.027858723
[235,] 0.9586388 0.082722363 0.041361182
[236,] 0.9534900 0.093020005 0.046510003
[237,] 0.9384503 0.123099357 0.061549678
[238,] 0.9150849 0.169830236 0.084915118
[239,] 0.8812380 0.237523926 0.118761963
[240,] 0.8427101 0.314579802 0.157289901
[241,] 0.8314635 0.337073094 0.168536547
[242,] 0.7904788 0.419042313 0.209521156
[243,] 0.7279004 0.544199180 0.272099590
[244,] 0.6750015 0.649996943 0.324998472
[245,] 0.6023038 0.795392366 0.397696183
[246,] 0.5468802 0.906239522 0.453119761
[247,] 0.4461950 0.892389964 0.553805018
[248,] 0.3636772 0.727354404 0.636322798
[249,] 0.4637189 0.927437751 0.536281125
[250,] 0.3859351 0.771870226 0.614064887
[251,] 0.2616548 0.523309503 0.738345249
[252,] 0.1543774 0.308754728 0.845622636
> postscript(file="/var/fisher/rcomp/tmp/1xrvl1387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2x9891387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3yxkm1387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/43wrp1387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5o3es1387448176.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 = 275
Frequency = 1
1 2 3 4 5 6
0.67938170 2.90352210 -2.15821729 0.56554669 -0.21569237 -4.15600593
7 8 9 10 11 12
-3.60559630 -1.19216391 4.26189945 -1.23690546 3.40093432 0.49662032
13 14 15 16 17 18
-0.40721035 2.38278531 5.58349180 0.59589982 -1.71290069 -0.85719817
19 20 21 22 23 24
-2.10906585 3.38057395 -0.59149715 2.25478856 -0.79327370 -6.57214763
25 26 27 28 29 30
-1.71981881 5.61826594 3.13293732 -0.33856341 1.90352210 2.24365222
31 32 33 34 35 36
-1.28784991 -0.73095515 -0.11119184 -1.83770494 -2.16546092 -1.58961069
37 38 39 40 41 42
2.89422552 -0.92633015 -1.82568366 4.27448740 -0.73095515 -3.25149031
43 44 45 46 47 48
3.44851486 -1.27923928 2.19330971 6.97588354 4.08987697 1.59315069
49 50 51 52 53 54
-0.47061129 -3.50089187 3.97180800 1.35591633 0.14231978 -2.27221072
55 56 57 58 59 60
1.49662032 3.98165966 -7.66783363 8.31745243 1.14280183 0.40817795
61 62 63 64 65 66
2.43349881 -3.47218219 -1.46701780 1.64882468 2.31470331 4.49746469
67 68 69 70 71 72
-1.31501734 -1.87735465 -0.41429683 0.06706669 3.98157905 5.62507472
73 74 75 76 77 78
3.77727907 -4.18459538 0.29370247 -1.68750806 -1.42736810 -2.79407666
79 80 81 82 83 84
1.28709931 2.58610868 3.15299848 1.47879622 1.14940499 -4.13806082
85 86 87 88 89 90
0.77866119 -4.79407666 -1.26946995 2.17880139 8.71553968 4.44106734
91 92 93 94 95 96
2.54673714 -1.47266424 -1.32226097 -1.56650119 -1.88251903 2.81004746
97 98 99 100 101 102
-3.39054858 -2.28446032 5.74334758 0.48411776 0.71415756 -3.38152844
103 104 105 106 107 108
-2.41389132 -3.10529243 2.68159307 -0.69390960 4.27185756 0.02316196
109 110 111 112 113 114
1.43709230 5.95734703 -2.02062347 -0.91716504 -1.27518816 8.17880139
115 116 117 118 119 120
-2.05963397 -0.25032493 4.89470757 -1.63506524 -2.32130425 4.33781281
121 122 123 124 125 126
-1.29465697 -1.34077477 0.55809917 -1.13584945 1.57192415 0.18341518
127 128 129 130 131 132
-1.90447659 -3.06977492 -0.97324455 -1.07252404 1.39749925 1.78620404
133 134 135 136 137 138
4.52781781 0.58522112 0.21088383 -1.22251703 1.88481304 -4.49567841
139 140 141 142 143 144
4.41534980 3.78030463 3.58280585 6.74471458 0.83675045 -2.37877932
145 146 147 148 149 150
0.42805627 -5.15035949 -3.15203260 -4.68915073 -0.24239535 -0.13225596
151 152 153 154 155 156
0.28488794 -1.59906568 -1.73480918 -3.03809538 3.05351509 1.18624892
157 158 159 160 161 162
1.71553968 0.09359904 2.05351509 1.75896407 -0.50998284 0.22493452
163 164 165 166 167 168
2.75352911 -0.61848637 -2.51562928 -0.03995955 0.94742682 4.68518656
169 170 171 172 173 174
-0.78843022 2.28689541 3.81142958 -2.23964623 -1.10577448 -1.49978619
175 176 177 178 179 180
1.06000589 0.81191163 -1.41134381 2.65922523 -3.97340296 -2.77412580
181 182 183 184 185 186
-0.64998308 1.01958437 -1.68254757 0.14920109 -1.83365382 -5.30636123
187 188 189 190 191 192
-2.16324955 0.89470757 0.68159307 3.06275502 2.80783609 1.59635745
193 194 195 196 197 198
-3.39014135 -2.32110035 0.61061375 -2.76397650 -1.84135508 -0.89787344
199 200 201 202 203 204
-1.01530194 6.66466777 1.33437773 1.65922523 1.53298220 2.35619277
205 206 207 208 209 210
0.67781965 -1.88996656 -2.75111162 0.84178271 0.51988824 -5.81100196
211 212 213 214 215 216
-0.25913945 -2.19236952 2.33216637 -2.68254757 2.29395621 -1.04217092
217 218 219 220 221 222
0.81142958 0.96664361 2.55265663 1.28350582 -7.80144357 -1.13544393
223 224 225 226 227 228
-0.53034321 -5.69322627 -0.95529944 -1.74841820 -1.43964211 0.59589982
229 230 231 232 233 234
3.62915026 0.86415055 -2.26999935 -2.06977492 2.90764211 -3.59567609
235 236 237 238 239 240
0.28709931 -0.84543062 1.37397079 -1.50495058 -3.62602921 -1.43397126
241 242 243 244 245 246
-1.76941904 0.61633273 0.56058620 -2.41699025 -3.30276774 -1.35661786
247 248 249 250 251 252
-2.00876206 0.38298693 -1.94744163 2.35977114 1.89830106 -3.90668796
253 254 255 256 257 258
-1.04044160 -2.19876707 -3.52469677 1.35812770 0.31745243 -1.97683804
259 260 261 262 263 264
-2.84651947 -3.59499014 -1.41429683 1.04691193 1.13729909 1.39364521
265 266 267 268 269 270
0.28193492 0.61577814 -0.13584945 -1.41807910 0.96004045 -1.82684505
271 272 273 274 275
-6.40548231 -3.57875079 1.33865489 -2.59305921 -3.97683804
> postscript(file="/var/fisher/rcomp/tmp/632lk1387448176.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 = 275
Frequency = 1
lag(myerror, k = 1) myerror
0 0.67938170 NA
1 2.90352210 0.67938170
2 -2.15821729 2.90352210
3 0.56554669 -2.15821729
4 -0.21569237 0.56554669
5 -4.15600593 -0.21569237
6 -3.60559630 -4.15600593
7 -1.19216391 -3.60559630
8 4.26189945 -1.19216391
9 -1.23690546 4.26189945
10 3.40093432 -1.23690546
11 0.49662032 3.40093432
12 -0.40721035 0.49662032
13 2.38278531 -0.40721035
14 5.58349180 2.38278531
15 0.59589982 5.58349180
16 -1.71290069 0.59589982
17 -0.85719817 -1.71290069
18 -2.10906585 -0.85719817
19 3.38057395 -2.10906585
20 -0.59149715 3.38057395
21 2.25478856 -0.59149715
22 -0.79327370 2.25478856
23 -6.57214763 -0.79327370
24 -1.71981881 -6.57214763
25 5.61826594 -1.71981881
26 3.13293732 5.61826594
27 -0.33856341 3.13293732
28 1.90352210 -0.33856341
29 2.24365222 1.90352210
30 -1.28784991 2.24365222
31 -0.73095515 -1.28784991
32 -0.11119184 -0.73095515
33 -1.83770494 -0.11119184
34 -2.16546092 -1.83770494
35 -1.58961069 -2.16546092
36 2.89422552 -1.58961069
37 -0.92633015 2.89422552
38 -1.82568366 -0.92633015
39 4.27448740 -1.82568366
40 -0.73095515 4.27448740
41 -3.25149031 -0.73095515
42 3.44851486 -3.25149031
43 -1.27923928 3.44851486
44 2.19330971 -1.27923928
45 6.97588354 2.19330971
46 4.08987697 6.97588354
47 1.59315069 4.08987697
48 -0.47061129 1.59315069
49 -3.50089187 -0.47061129
50 3.97180800 -3.50089187
51 1.35591633 3.97180800
52 0.14231978 1.35591633
53 -2.27221072 0.14231978
54 1.49662032 -2.27221072
55 3.98165966 1.49662032
56 -7.66783363 3.98165966
57 8.31745243 -7.66783363
58 1.14280183 8.31745243
59 0.40817795 1.14280183
60 2.43349881 0.40817795
61 -3.47218219 2.43349881
62 -1.46701780 -3.47218219
63 1.64882468 -1.46701780
64 2.31470331 1.64882468
65 4.49746469 2.31470331
66 -1.31501734 4.49746469
67 -1.87735465 -1.31501734
68 -0.41429683 -1.87735465
69 0.06706669 -0.41429683
70 3.98157905 0.06706669
71 5.62507472 3.98157905
72 3.77727907 5.62507472
73 -4.18459538 3.77727907
74 0.29370247 -4.18459538
75 -1.68750806 0.29370247
76 -1.42736810 -1.68750806
77 -2.79407666 -1.42736810
78 1.28709931 -2.79407666
79 2.58610868 1.28709931
80 3.15299848 2.58610868
81 1.47879622 3.15299848
82 1.14940499 1.47879622
83 -4.13806082 1.14940499
84 0.77866119 -4.13806082
85 -4.79407666 0.77866119
86 -1.26946995 -4.79407666
87 2.17880139 -1.26946995
88 8.71553968 2.17880139
89 4.44106734 8.71553968
90 2.54673714 4.44106734
91 -1.47266424 2.54673714
92 -1.32226097 -1.47266424
93 -1.56650119 -1.32226097
94 -1.88251903 -1.56650119
95 2.81004746 -1.88251903
96 -3.39054858 2.81004746
97 -2.28446032 -3.39054858
98 5.74334758 -2.28446032
99 0.48411776 5.74334758
100 0.71415756 0.48411776
101 -3.38152844 0.71415756
102 -2.41389132 -3.38152844
103 -3.10529243 -2.41389132
104 2.68159307 -3.10529243
105 -0.69390960 2.68159307
106 4.27185756 -0.69390960
107 0.02316196 4.27185756
108 1.43709230 0.02316196
109 5.95734703 1.43709230
110 -2.02062347 5.95734703
111 -0.91716504 -2.02062347
112 -1.27518816 -0.91716504
113 8.17880139 -1.27518816
114 -2.05963397 8.17880139
115 -0.25032493 -2.05963397
116 4.89470757 -0.25032493
117 -1.63506524 4.89470757
118 -2.32130425 -1.63506524
119 4.33781281 -2.32130425
120 -1.29465697 4.33781281
121 -1.34077477 -1.29465697
122 0.55809917 -1.34077477
123 -1.13584945 0.55809917
124 1.57192415 -1.13584945
125 0.18341518 1.57192415
126 -1.90447659 0.18341518
127 -3.06977492 -1.90447659
128 -0.97324455 -3.06977492
129 -1.07252404 -0.97324455
130 1.39749925 -1.07252404
131 1.78620404 1.39749925
132 4.52781781 1.78620404
133 0.58522112 4.52781781
134 0.21088383 0.58522112
135 -1.22251703 0.21088383
136 1.88481304 -1.22251703
137 -4.49567841 1.88481304
138 4.41534980 -4.49567841
139 3.78030463 4.41534980
140 3.58280585 3.78030463
141 6.74471458 3.58280585
142 0.83675045 6.74471458
143 -2.37877932 0.83675045
144 0.42805627 -2.37877932
145 -5.15035949 0.42805627
146 -3.15203260 -5.15035949
147 -4.68915073 -3.15203260
148 -0.24239535 -4.68915073
149 -0.13225596 -0.24239535
150 0.28488794 -0.13225596
151 -1.59906568 0.28488794
152 -1.73480918 -1.59906568
153 -3.03809538 -1.73480918
154 3.05351509 -3.03809538
155 1.18624892 3.05351509
156 1.71553968 1.18624892
157 0.09359904 1.71553968
158 2.05351509 0.09359904
159 1.75896407 2.05351509
160 -0.50998284 1.75896407
161 0.22493452 -0.50998284
162 2.75352911 0.22493452
163 -0.61848637 2.75352911
164 -2.51562928 -0.61848637
165 -0.03995955 -2.51562928
166 0.94742682 -0.03995955
167 4.68518656 0.94742682
168 -0.78843022 4.68518656
169 2.28689541 -0.78843022
170 3.81142958 2.28689541
171 -2.23964623 3.81142958
172 -1.10577448 -2.23964623
173 -1.49978619 -1.10577448
174 1.06000589 -1.49978619
175 0.81191163 1.06000589
176 -1.41134381 0.81191163
177 2.65922523 -1.41134381
178 -3.97340296 2.65922523
179 -2.77412580 -3.97340296
180 -0.64998308 -2.77412580
181 1.01958437 -0.64998308
182 -1.68254757 1.01958437
183 0.14920109 -1.68254757
184 -1.83365382 0.14920109
185 -5.30636123 -1.83365382
186 -2.16324955 -5.30636123
187 0.89470757 -2.16324955
188 0.68159307 0.89470757
189 3.06275502 0.68159307
190 2.80783609 3.06275502
191 1.59635745 2.80783609
192 -3.39014135 1.59635745
193 -2.32110035 -3.39014135
194 0.61061375 -2.32110035
195 -2.76397650 0.61061375
196 -1.84135508 -2.76397650
197 -0.89787344 -1.84135508
198 -1.01530194 -0.89787344
199 6.66466777 -1.01530194
200 1.33437773 6.66466777
201 1.65922523 1.33437773
202 1.53298220 1.65922523
203 2.35619277 1.53298220
204 0.67781965 2.35619277
205 -1.88996656 0.67781965
206 -2.75111162 -1.88996656
207 0.84178271 -2.75111162
208 0.51988824 0.84178271
209 -5.81100196 0.51988824
210 -0.25913945 -5.81100196
211 -2.19236952 -0.25913945
212 2.33216637 -2.19236952
213 -2.68254757 2.33216637
214 2.29395621 -2.68254757
215 -1.04217092 2.29395621
216 0.81142958 -1.04217092
217 0.96664361 0.81142958
218 2.55265663 0.96664361
219 1.28350582 2.55265663
220 -7.80144357 1.28350582
221 -1.13544393 -7.80144357
222 -0.53034321 -1.13544393
223 -5.69322627 -0.53034321
224 -0.95529944 -5.69322627
225 -1.74841820 -0.95529944
226 -1.43964211 -1.74841820
227 0.59589982 -1.43964211
228 3.62915026 0.59589982
229 0.86415055 3.62915026
230 -2.26999935 0.86415055
231 -2.06977492 -2.26999935
232 2.90764211 -2.06977492
233 -3.59567609 2.90764211
234 0.28709931 -3.59567609
235 -0.84543062 0.28709931
236 1.37397079 -0.84543062
237 -1.50495058 1.37397079
238 -3.62602921 -1.50495058
239 -1.43397126 -3.62602921
240 -1.76941904 -1.43397126
241 0.61633273 -1.76941904
242 0.56058620 0.61633273
243 -2.41699025 0.56058620
244 -3.30276774 -2.41699025
245 -1.35661786 -3.30276774
246 -2.00876206 -1.35661786
247 0.38298693 -2.00876206
248 -1.94744163 0.38298693
249 2.35977114 -1.94744163
250 1.89830106 2.35977114
251 -3.90668796 1.89830106
252 -1.04044160 -3.90668796
253 -2.19876707 -1.04044160
254 -3.52469677 -2.19876707
255 1.35812770 -3.52469677
256 0.31745243 1.35812770
257 -1.97683804 0.31745243
258 -2.84651947 -1.97683804
259 -3.59499014 -2.84651947
260 -1.41429683 -3.59499014
261 1.04691193 -1.41429683
262 1.13729909 1.04691193
263 1.39364521 1.13729909
264 0.28193492 1.39364521
265 0.61577814 0.28193492
266 -0.13584945 0.61577814
267 -1.41807910 -0.13584945
268 0.96004045 -1.41807910
269 -1.82684505 0.96004045
270 -6.40548231 -1.82684505
271 -3.57875079 -6.40548231
272 1.33865489 -3.57875079
273 -2.59305921 1.33865489
274 -3.97683804 -2.59305921
275 NA -3.97683804
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.90352210 0.67938170
[2,] -2.15821729 2.90352210
[3,] 0.56554669 -2.15821729
[4,] -0.21569237 0.56554669
[5,] -4.15600593 -0.21569237
[6,] -3.60559630 -4.15600593
[7,] -1.19216391 -3.60559630
[8,] 4.26189945 -1.19216391
[9,] -1.23690546 4.26189945
[10,] 3.40093432 -1.23690546
[11,] 0.49662032 3.40093432
[12,] -0.40721035 0.49662032
[13,] 2.38278531 -0.40721035
[14,] 5.58349180 2.38278531
[15,] 0.59589982 5.58349180
[16,] -1.71290069 0.59589982
[17,] -0.85719817 -1.71290069
[18,] -2.10906585 -0.85719817
[19,] 3.38057395 -2.10906585
[20,] -0.59149715 3.38057395
[21,] 2.25478856 -0.59149715
[22,] -0.79327370 2.25478856
[23,] -6.57214763 -0.79327370
[24,] -1.71981881 -6.57214763
[25,] 5.61826594 -1.71981881
[26,] 3.13293732 5.61826594
[27,] -0.33856341 3.13293732
[28,] 1.90352210 -0.33856341
[29,] 2.24365222 1.90352210
[30,] -1.28784991 2.24365222
[31,] -0.73095515 -1.28784991
[32,] -0.11119184 -0.73095515
[33,] -1.83770494 -0.11119184
[34,] -2.16546092 -1.83770494
[35,] -1.58961069 -2.16546092
[36,] 2.89422552 -1.58961069
[37,] -0.92633015 2.89422552
[38,] -1.82568366 -0.92633015
[39,] 4.27448740 -1.82568366
[40,] -0.73095515 4.27448740
[41,] -3.25149031 -0.73095515
[42,] 3.44851486 -3.25149031
[43,] -1.27923928 3.44851486
[44,] 2.19330971 -1.27923928
[45,] 6.97588354 2.19330971
[46,] 4.08987697 6.97588354
[47,] 1.59315069 4.08987697
[48,] -0.47061129 1.59315069
[49,] -3.50089187 -0.47061129
[50,] 3.97180800 -3.50089187
[51,] 1.35591633 3.97180800
[52,] 0.14231978 1.35591633
[53,] -2.27221072 0.14231978
[54,] 1.49662032 -2.27221072
[55,] 3.98165966 1.49662032
[56,] -7.66783363 3.98165966
[57,] 8.31745243 -7.66783363
[58,] 1.14280183 8.31745243
[59,] 0.40817795 1.14280183
[60,] 2.43349881 0.40817795
[61,] -3.47218219 2.43349881
[62,] -1.46701780 -3.47218219
[63,] 1.64882468 -1.46701780
[64,] 2.31470331 1.64882468
[65,] 4.49746469 2.31470331
[66,] -1.31501734 4.49746469
[67,] -1.87735465 -1.31501734
[68,] -0.41429683 -1.87735465
[69,] 0.06706669 -0.41429683
[70,] 3.98157905 0.06706669
[71,] 5.62507472 3.98157905
[72,] 3.77727907 5.62507472
[73,] -4.18459538 3.77727907
[74,] 0.29370247 -4.18459538
[75,] -1.68750806 0.29370247
[76,] -1.42736810 -1.68750806
[77,] -2.79407666 -1.42736810
[78,] 1.28709931 -2.79407666
[79,] 2.58610868 1.28709931
[80,] 3.15299848 2.58610868
[81,] 1.47879622 3.15299848
[82,] 1.14940499 1.47879622
[83,] -4.13806082 1.14940499
[84,] 0.77866119 -4.13806082
[85,] -4.79407666 0.77866119
[86,] -1.26946995 -4.79407666
[87,] 2.17880139 -1.26946995
[88,] 8.71553968 2.17880139
[89,] 4.44106734 8.71553968
[90,] 2.54673714 4.44106734
[91,] -1.47266424 2.54673714
[92,] -1.32226097 -1.47266424
[93,] -1.56650119 -1.32226097
[94,] -1.88251903 -1.56650119
[95,] 2.81004746 -1.88251903
[96,] -3.39054858 2.81004746
[97,] -2.28446032 -3.39054858
[98,] 5.74334758 -2.28446032
[99,] 0.48411776 5.74334758
[100,] 0.71415756 0.48411776
[101,] -3.38152844 0.71415756
[102,] -2.41389132 -3.38152844
[103,] -3.10529243 -2.41389132
[104,] 2.68159307 -3.10529243
[105,] -0.69390960 2.68159307
[106,] 4.27185756 -0.69390960
[107,] 0.02316196 4.27185756
[108,] 1.43709230 0.02316196
[109,] 5.95734703 1.43709230
[110,] -2.02062347 5.95734703
[111,] -0.91716504 -2.02062347
[112,] -1.27518816 -0.91716504
[113,] 8.17880139 -1.27518816
[114,] -2.05963397 8.17880139
[115,] -0.25032493 -2.05963397
[116,] 4.89470757 -0.25032493
[117,] -1.63506524 4.89470757
[118,] -2.32130425 -1.63506524
[119,] 4.33781281 -2.32130425
[120,] -1.29465697 4.33781281
[121,] -1.34077477 -1.29465697
[122,] 0.55809917 -1.34077477
[123,] -1.13584945 0.55809917
[124,] 1.57192415 -1.13584945
[125,] 0.18341518 1.57192415
[126,] -1.90447659 0.18341518
[127,] -3.06977492 -1.90447659
[128,] -0.97324455 -3.06977492
[129,] -1.07252404 -0.97324455
[130,] 1.39749925 -1.07252404
[131,] 1.78620404 1.39749925
[132,] 4.52781781 1.78620404
[133,] 0.58522112 4.52781781
[134,] 0.21088383 0.58522112
[135,] -1.22251703 0.21088383
[136,] 1.88481304 -1.22251703
[137,] -4.49567841 1.88481304
[138,] 4.41534980 -4.49567841
[139,] 3.78030463 4.41534980
[140,] 3.58280585 3.78030463
[141,] 6.74471458 3.58280585
[142,] 0.83675045 6.74471458
[143,] -2.37877932 0.83675045
[144,] 0.42805627 -2.37877932
[145,] -5.15035949 0.42805627
[146,] -3.15203260 -5.15035949
[147,] -4.68915073 -3.15203260
[148,] -0.24239535 -4.68915073
[149,] -0.13225596 -0.24239535
[150,] 0.28488794 -0.13225596
[151,] -1.59906568 0.28488794
[152,] -1.73480918 -1.59906568
[153,] -3.03809538 -1.73480918
[154,] 3.05351509 -3.03809538
[155,] 1.18624892 3.05351509
[156,] 1.71553968 1.18624892
[157,] 0.09359904 1.71553968
[158,] 2.05351509 0.09359904
[159,] 1.75896407 2.05351509
[160,] -0.50998284 1.75896407
[161,] 0.22493452 -0.50998284
[162,] 2.75352911 0.22493452
[163,] -0.61848637 2.75352911
[164,] -2.51562928 -0.61848637
[165,] -0.03995955 -2.51562928
[166,] 0.94742682 -0.03995955
[167,] 4.68518656 0.94742682
[168,] -0.78843022 4.68518656
[169,] 2.28689541 -0.78843022
[170,] 3.81142958 2.28689541
[171,] -2.23964623 3.81142958
[172,] -1.10577448 -2.23964623
[173,] -1.49978619 -1.10577448
[174,] 1.06000589 -1.49978619
[175,] 0.81191163 1.06000589
[176,] -1.41134381 0.81191163
[177,] 2.65922523 -1.41134381
[178,] -3.97340296 2.65922523
[179,] -2.77412580 -3.97340296
[180,] -0.64998308 -2.77412580
[181,] 1.01958437 -0.64998308
[182,] -1.68254757 1.01958437
[183,] 0.14920109 -1.68254757
[184,] -1.83365382 0.14920109
[185,] -5.30636123 -1.83365382
[186,] -2.16324955 -5.30636123
[187,] 0.89470757 -2.16324955
[188,] 0.68159307 0.89470757
[189,] 3.06275502 0.68159307
[190,] 2.80783609 3.06275502
[191,] 1.59635745 2.80783609
[192,] -3.39014135 1.59635745
[193,] -2.32110035 -3.39014135
[194,] 0.61061375 -2.32110035
[195,] -2.76397650 0.61061375
[196,] -1.84135508 -2.76397650
[197,] -0.89787344 -1.84135508
[198,] -1.01530194 -0.89787344
[199,] 6.66466777 -1.01530194
[200,] 1.33437773 6.66466777
[201,] 1.65922523 1.33437773
[202,] 1.53298220 1.65922523
[203,] 2.35619277 1.53298220
[204,] 0.67781965 2.35619277
[205,] -1.88996656 0.67781965
[206,] -2.75111162 -1.88996656
[207,] 0.84178271 -2.75111162
[208,] 0.51988824 0.84178271
[209,] -5.81100196 0.51988824
[210,] -0.25913945 -5.81100196
[211,] -2.19236952 -0.25913945
[212,] 2.33216637 -2.19236952
[213,] -2.68254757 2.33216637
[214,] 2.29395621 -2.68254757
[215,] -1.04217092 2.29395621
[216,] 0.81142958 -1.04217092
[217,] 0.96664361 0.81142958
[218,] 2.55265663 0.96664361
[219,] 1.28350582 2.55265663
[220,] -7.80144357 1.28350582
[221,] -1.13544393 -7.80144357
[222,] -0.53034321 -1.13544393
[223,] -5.69322627 -0.53034321
[224,] -0.95529944 -5.69322627
[225,] -1.74841820 -0.95529944
[226,] -1.43964211 -1.74841820
[227,] 0.59589982 -1.43964211
[228,] 3.62915026 0.59589982
[229,] 0.86415055 3.62915026
[230,] -2.26999935 0.86415055
[231,] -2.06977492 -2.26999935
[232,] 2.90764211 -2.06977492
[233,] -3.59567609 2.90764211
[234,] 0.28709931 -3.59567609
[235,] -0.84543062 0.28709931
[236,] 1.37397079 -0.84543062
[237,] -1.50495058 1.37397079
[238,] -3.62602921 -1.50495058
[239,] -1.43397126 -3.62602921
[240,] -1.76941904 -1.43397126
[241,] 0.61633273 -1.76941904
[242,] 0.56058620 0.61633273
[243,] -2.41699025 0.56058620
[244,] -3.30276774 -2.41699025
[245,] -1.35661786 -3.30276774
[246,] -2.00876206 -1.35661786
[247,] 0.38298693 -2.00876206
[248,] -1.94744163 0.38298693
[249,] 2.35977114 -1.94744163
[250,] 1.89830106 2.35977114
[251,] -3.90668796 1.89830106
[252,] -1.04044160 -3.90668796
[253,] -2.19876707 -1.04044160
[254,] -3.52469677 -2.19876707
[255,] 1.35812770 -3.52469677
[256,] 0.31745243 1.35812770
[257,] -1.97683804 0.31745243
[258,] -2.84651947 -1.97683804
[259,] -3.59499014 -2.84651947
[260,] -1.41429683 -3.59499014
[261,] 1.04691193 -1.41429683
[262,] 1.13729909 1.04691193
[263,] 1.39364521 1.13729909
[264,] 0.28193492 1.39364521
[265,] 0.61577814 0.28193492
[266,] -0.13584945 0.61577814
[267,] -1.41807910 -0.13584945
[268,] 0.96004045 -1.41807910
[269,] -1.82684505 0.96004045
[270,] -6.40548231 -1.82684505
[271,] -3.57875079 -6.40548231
[272,] 1.33865489 -3.57875079
[273,] -2.59305921 1.33865489
[274,] -3.97683804 -2.59305921
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.90352210 0.67938170
2 -2.15821729 2.90352210
3 0.56554669 -2.15821729
4 -0.21569237 0.56554669
5 -4.15600593 -0.21569237
6 -3.60559630 -4.15600593
7 -1.19216391 -3.60559630
8 4.26189945 -1.19216391
9 -1.23690546 4.26189945
10 3.40093432 -1.23690546
11 0.49662032 3.40093432
12 -0.40721035 0.49662032
13 2.38278531 -0.40721035
14 5.58349180 2.38278531
15 0.59589982 5.58349180
16 -1.71290069 0.59589982
17 -0.85719817 -1.71290069
18 -2.10906585 -0.85719817
19 3.38057395 -2.10906585
20 -0.59149715 3.38057395
21 2.25478856 -0.59149715
22 -0.79327370 2.25478856
23 -6.57214763 -0.79327370
24 -1.71981881 -6.57214763
25 5.61826594 -1.71981881
26 3.13293732 5.61826594
27 -0.33856341 3.13293732
28 1.90352210 -0.33856341
29 2.24365222 1.90352210
30 -1.28784991 2.24365222
31 -0.73095515 -1.28784991
32 -0.11119184 -0.73095515
33 -1.83770494 -0.11119184
34 -2.16546092 -1.83770494
35 -1.58961069 -2.16546092
36 2.89422552 -1.58961069
37 -0.92633015 2.89422552
38 -1.82568366 -0.92633015
39 4.27448740 -1.82568366
40 -0.73095515 4.27448740
41 -3.25149031 -0.73095515
42 3.44851486 -3.25149031
43 -1.27923928 3.44851486
44 2.19330971 -1.27923928
45 6.97588354 2.19330971
46 4.08987697 6.97588354
47 1.59315069 4.08987697
48 -0.47061129 1.59315069
49 -3.50089187 -0.47061129
50 3.97180800 -3.50089187
51 1.35591633 3.97180800
52 0.14231978 1.35591633
53 -2.27221072 0.14231978
54 1.49662032 -2.27221072
55 3.98165966 1.49662032
56 -7.66783363 3.98165966
57 8.31745243 -7.66783363
58 1.14280183 8.31745243
59 0.40817795 1.14280183
60 2.43349881 0.40817795
61 -3.47218219 2.43349881
62 -1.46701780 -3.47218219
63 1.64882468 -1.46701780
64 2.31470331 1.64882468
65 4.49746469 2.31470331
66 -1.31501734 4.49746469
67 -1.87735465 -1.31501734
68 -0.41429683 -1.87735465
69 0.06706669 -0.41429683
70 3.98157905 0.06706669
71 5.62507472 3.98157905
72 3.77727907 5.62507472
73 -4.18459538 3.77727907
74 0.29370247 -4.18459538
75 -1.68750806 0.29370247
76 -1.42736810 -1.68750806
77 -2.79407666 -1.42736810
78 1.28709931 -2.79407666
79 2.58610868 1.28709931
80 3.15299848 2.58610868
81 1.47879622 3.15299848
82 1.14940499 1.47879622
83 -4.13806082 1.14940499
84 0.77866119 -4.13806082
85 -4.79407666 0.77866119
86 -1.26946995 -4.79407666
87 2.17880139 -1.26946995
88 8.71553968 2.17880139
89 4.44106734 8.71553968
90 2.54673714 4.44106734
91 -1.47266424 2.54673714
92 -1.32226097 -1.47266424
93 -1.56650119 -1.32226097
94 -1.88251903 -1.56650119
95 2.81004746 -1.88251903
96 -3.39054858 2.81004746
97 -2.28446032 -3.39054858
98 5.74334758 -2.28446032
99 0.48411776 5.74334758
100 0.71415756 0.48411776
101 -3.38152844 0.71415756
102 -2.41389132 -3.38152844
103 -3.10529243 -2.41389132
104 2.68159307 -3.10529243
105 -0.69390960 2.68159307
106 4.27185756 -0.69390960
107 0.02316196 4.27185756
108 1.43709230 0.02316196
109 5.95734703 1.43709230
110 -2.02062347 5.95734703
111 -0.91716504 -2.02062347
112 -1.27518816 -0.91716504
113 8.17880139 -1.27518816
114 -2.05963397 8.17880139
115 -0.25032493 -2.05963397
116 4.89470757 -0.25032493
117 -1.63506524 4.89470757
118 -2.32130425 -1.63506524
119 4.33781281 -2.32130425
120 -1.29465697 4.33781281
121 -1.34077477 -1.29465697
122 0.55809917 -1.34077477
123 -1.13584945 0.55809917
124 1.57192415 -1.13584945
125 0.18341518 1.57192415
126 -1.90447659 0.18341518
127 -3.06977492 -1.90447659
128 -0.97324455 -3.06977492
129 -1.07252404 -0.97324455
130 1.39749925 -1.07252404
131 1.78620404 1.39749925
132 4.52781781 1.78620404
133 0.58522112 4.52781781
134 0.21088383 0.58522112
135 -1.22251703 0.21088383
136 1.88481304 -1.22251703
137 -4.49567841 1.88481304
138 4.41534980 -4.49567841
139 3.78030463 4.41534980
140 3.58280585 3.78030463
141 6.74471458 3.58280585
142 0.83675045 6.74471458
143 -2.37877932 0.83675045
144 0.42805627 -2.37877932
145 -5.15035949 0.42805627
146 -3.15203260 -5.15035949
147 -4.68915073 -3.15203260
148 -0.24239535 -4.68915073
149 -0.13225596 -0.24239535
150 0.28488794 -0.13225596
151 -1.59906568 0.28488794
152 -1.73480918 -1.59906568
153 -3.03809538 -1.73480918
154 3.05351509 -3.03809538
155 1.18624892 3.05351509
156 1.71553968 1.18624892
157 0.09359904 1.71553968
158 2.05351509 0.09359904
159 1.75896407 2.05351509
160 -0.50998284 1.75896407
161 0.22493452 -0.50998284
162 2.75352911 0.22493452
163 -0.61848637 2.75352911
164 -2.51562928 -0.61848637
165 -0.03995955 -2.51562928
166 0.94742682 -0.03995955
167 4.68518656 0.94742682
168 -0.78843022 4.68518656
169 2.28689541 -0.78843022
170 3.81142958 2.28689541
171 -2.23964623 3.81142958
172 -1.10577448 -2.23964623
173 -1.49978619 -1.10577448
174 1.06000589 -1.49978619
175 0.81191163 1.06000589
176 -1.41134381 0.81191163
177 2.65922523 -1.41134381
178 -3.97340296 2.65922523
179 -2.77412580 -3.97340296
180 -0.64998308 -2.77412580
181 1.01958437 -0.64998308
182 -1.68254757 1.01958437
183 0.14920109 -1.68254757
184 -1.83365382 0.14920109
185 -5.30636123 -1.83365382
186 -2.16324955 -5.30636123
187 0.89470757 -2.16324955
188 0.68159307 0.89470757
189 3.06275502 0.68159307
190 2.80783609 3.06275502
191 1.59635745 2.80783609
192 -3.39014135 1.59635745
193 -2.32110035 -3.39014135
194 0.61061375 -2.32110035
195 -2.76397650 0.61061375
196 -1.84135508 -2.76397650
197 -0.89787344 -1.84135508
198 -1.01530194 -0.89787344
199 6.66466777 -1.01530194
200 1.33437773 6.66466777
201 1.65922523 1.33437773
202 1.53298220 1.65922523
203 2.35619277 1.53298220
204 0.67781965 2.35619277
205 -1.88996656 0.67781965
206 -2.75111162 -1.88996656
207 0.84178271 -2.75111162
208 0.51988824 0.84178271
209 -5.81100196 0.51988824
210 -0.25913945 -5.81100196
211 -2.19236952 -0.25913945
212 2.33216637 -2.19236952
213 -2.68254757 2.33216637
214 2.29395621 -2.68254757
215 -1.04217092 2.29395621
216 0.81142958 -1.04217092
217 0.96664361 0.81142958
218 2.55265663 0.96664361
219 1.28350582 2.55265663
220 -7.80144357 1.28350582
221 -1.13544393 -7.80144357
222 -0.53034321 -1.13544393
223 -5.69322627 -0.53034321
224 -0.95529944 -5.69322627
225 -1.74841820 -0.95529944
226 -1.43964211 -1.74841820
227 0.59589982 -1.43964211
228 3.62915026 0.59589982
229 0.86415055 3.62915026
230 -2.26999935 0.86415055
231 -2.06977492 -2.26999935
232 2.90764211 -2.06977492
233 -3.59567609 2.90764211
234 0.28709931 -3.59567609
235 -0.84543062 0.28709931
236 1.37397079 -0.84543062
237 -1.50495058 1.37397079
238 -3.62602921 -1.50495058
239 -1.43397126 -3.62602921
240 -1.76941904 -1.43397126
241 0.61633273 -1.76941904
242 0.56058620 0.61633273
243 -2.41699025 0.56058620
244 -3.30276774 -2.41699025
245 -1.35661786 -3.30276774
246 -2.00876206 -1.35661786
247 0.38298693 -2.00876206
248 -1.94744163 0.38298693
249 2.35977114 -1.94744163
250 1.89830106 2.35977114
251 -3.90668796 1.89830106
252 -1.04044160 -3.90668796
253 -2.19876707 -1.04044160
254 -3.52469677 -2.19876707
255 1.35812770 -3.52469677
256 0.31745243 1.35812770
257 -1.97683804 0.31745243
258 -2.84651947 -1.97683804
259 -3.59499014 -2.84651947
260 -1.41429683 -3.59499014
261 1.04691193 -1.41429683
262 1.13729909 1.04691193
263 1.39364521 1.13729909
264 0.28193492 1.39364521
265 0.61577814 0.28193492
266 -0.13584945 0.61577814
267 -1.41807910 -0.13584945
268 0.96004045 -1.41807910
269 -1.82684505 0.96004045
270 -6.40548231 -1.82684505
271 -3.57875079 -6.40548231
272 1.33865489 -3.57875079
273 -2.59305921 1.33865489
274 -3.97683804 -2.59305921
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/7zipb1387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8v5921387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9vkwv1387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10l9dk1387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/119mby1387448176.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/127bb71387448176.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13h4901387448177.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14luay1387448177.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/153clw1387448177.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16s9ls1387448177.tab")
+ }
>
> try(system("convert tmp/1xrvl1387448176.ps tmp/1xrvl1387448176.png",intern=TRUE))
character(0)
> try(system("convert tmp/2x9891387448176.ps tmp/2x9891387448176.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yxkm1387448176.ps tmp/3yxkm1387448176.png",intern=TRUE))
character(0)
> try(system("convert tmp/43wrp1387448176.ps tmp/43wrp1387448176.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o3es1387448176.ps tmp/5o3es1387448176.png",intern=TRUE))
character(0)
> try(system("convert tmp/632lk1387448176.ps tmp/632lk1387448176.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zipb1387448176.ps tmp/7zipb1387448176.png",intern=TRUE))
character(0)
> try(system("convert tmp/8v5921387448176.ps tmp/8v5921387448176.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vkwv1387448176.ps tmp/9vkwv1387448176.png",intern=TRUE))
character(0)
> try(system("convert tmp/10l9dk1387448176.ps tmp/10l9dk1387448176.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
19.852 3.391 23.256