R version 3.3.0 (2016-05-03) -- "Supposedly Educational"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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Type 'contributors()' for more information and
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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(-0.24
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+ ,-0.02
+ ,0.83
+ ,0.22
+ ,0.15
+ ,0.78
+ ,0.29
+ ,0.70
+ ,0.72
+ ,0.32
+ ,-0.01
+ ,0.77
+ ,0.29
+ ,0.01
+ ,0.81
+ ,0.29
+ ,-0.15
+ ,0.68
+ ,0.09
+ ,-0.36
+ ,0.58
+ ,0.07
+ ,0.59
+ ,0.64
+ ,0.08
+ ,0.49
+ ,0.71
+ ,0.09
+ ,-0.13
+ ,0.67
+ ,0.09
+ ,0.11
+ ,0.67
+ ,0.09
+ ,0.65
+ ,0.68
+ ,0.08
+ ,0.22
+ ,0.65
+ ,0.08
+ ,0.07
+ ,0.66
+ ,0.08
+ ,-0.38
+ ,0.86
+ ,0.20
+ ,-0.42
+ ,0.83
+ ,0.13
+ ,-0.46
+ ,0.87
+ ,-0.53
+ ,-0.07
+ ,0.83
+ ,-3.07
+ ,-0.02
+ ,0.77
+ ,-3.61
+ ,-0.59
+ ,0.67
+ ,-3.72
+ ,-0.29
+ ,0.35
+ ,-4.72
+ ,-0.27
+ ,0.38
+ ,-5.30
+ ,1.12
+ ,-0.12
+ ,-7.03
+ ,0.03
+ ,0.66
+ ,0.09
+ ,-0.55
+ ,0.61
+ ,0.10
+ ,1.21
+ ,0.63
+ ,0.03
+ ,0.29
+ ,0.47
+ ,0.11
+ ,-0.27
+ ,0.53
+ ,0.06
+ ,-0.16
+ ,0.45
+ ,-0.11
+ ,-0.07
+ ,0.42
+ ,0.05
+ ,-0.12
+ ,0.45
+ ,0.04
+ ,-0.10
+ ,0.31
+ ,0.03
+ ,-0.11
+ ,0.75
+ ,0.13
+ ,0.19
+ ,0.75
+ ,0.15
+ ,-0.46
+ ,0.79
+ ,0.16
+ ,1.03
+ ,0.79
+ ,0.17
+ ,0.07
+ ,0.79
+ ,0.18
+ ,-0.22
+ ,0.72
+ ,0.12
+ ,0.62
+ ,0.75
+ ,0.13
+ ,-0.16
+ ,0.76
+ ,0.14
+ ,-0.03
+ ,0.70
+ ,0.11
+ ,-0.34
+ ,0.88
+ ,-7.30
+ ,-0.67
+ ,0.82
+ ,-4.69
+ ,1.33
+ ,0.82
+ ,-2.25
+ ,-0.39
+ ,0.88
+ ,-3.58
+ ,1.18
+ ,0.88
+ ,-0.26
+ ,-0.34
+ ,0.92
+ ,-1.34
+ ,-0.59
+ ,0.93
+ ,-4.06
+ ,-0.11
+ ,0.90
+ ,-1.09
+ ,-0.61
+ ,0.91
+ ,-0.45
+ ,-0.09
+ ,0.78
+ ,0.10
+ ,-0.48
+ ,0.82
+ ,0.08
+ ,0.38
+ ,0.84
+ ,0.06
+ ,-0.01
+ ,0.84
+ ,0.12
+ ,-0.03
+ ,0.85
+ ,0.16
+ ,0.23
+ ,0.85
+ ,0.11
+ ,1.03
+ ,0.78
+ ,0.13
+ ,0.25
+ ,0.63
+ ,0.03
+ ,-0.34
+ ,0.64
+ ,-0.96
+ ,-0.43
+ ,0.25
+ ,0.01
+ ,-0.23
+ ,0.20
+ ,0.03
+ ,-0.17
+ ,0.17
+ ,0.00
+ ,0.47
+ ,0.20
+ ,0.03
+ ,-0.44
+ ,0.23
+ ,0.03
+ ,0.27
+ ,0.30
+ ,0.02
+ ,1.06
+ ,0.36
+ ,0.03
+ ,0.23
+ ,0.38
+ ,0.03
+ ,-0.01
+ ,0.44
+ ,0.03
+ ,-0.48
+ ,0.61
+ ,0.01
+ ,-0.49
+ ,0.60
+ ,-0.03
+ ,0.77
+ ,0.70
+ ,0.02
+ ,0.31
+ ,0.72
+ ,0.02
+ ,0.10
+ ,0.76
+ ,0.03
+ ,-0.08
+ ,0.78
+ ,0.04
+ ,0.32
+ ,0.79
+ ,0.02
+ ,-0.12
+ ,0.75
+ ,0.01
+ ,-0.32
+ ,0.76
+ ,0.01
+ ,99.2
+ ,96.7
+ ,101.0
+ ,99.0
+ ,98.1
+ ,100.1
+ ,100.0
+ ,100.0
+ ,100.0
+ ,111.6
+ ,104.9
+ ,90.6
+ ,122.2
+ ,104.9
+ ,86.5
+ ,117.6
+ ,109.5
+ ,89.7
+ ,121.1
+ ,110.8
+ ,90.6
+ ,136.0
+ ,112.3
+ ,82.8
+ ,154.2
+ ,109.3
+ ,70.1
+ ,153.6
+ ,105.3
+ ,65.4
+ ,158.5
+ ,101.7
+ ,61.3
+ ,140.6
+ ,95.4
+ ,62.5
+ ,136.2
+ ,96.4
+ ,63.6
+ ,168.0
+ ,97.6
+ ,52.6
+ ,154.3
+ ,102.4
+ ,59.7
+ ,149.0
+ ,101.6
+ ,59.5
+ ,165.5
+ ,103.8
+ ,61.3)
+ ,dim=c(3
+ ,462)
+ ,dimnames=list(c('stock'
+ ,'asset'
+ ,'income')
+ ,1:462))
> y <- array(NA,dim=c(3,462),dimnames=list(c('stock','asset','income'),1:462))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par5 = '0'
> par4 = '0'
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par5 <- '0'
> par4 <- '0'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 (Sun, 06 Dec 2015 16:18:54 +0000)
> #Author: root
> #To cite this work: Wessa P., (2015), Multiple Regression (v1.0.38) 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
> mywarning <- ''
> par1 <- as.numeric(par1)
> if(is.na(par1)) {
+ par1 <- 1
+ mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
+ }
> if (par4=='') par4 <- 0
> par4 <- as.numeric(par4)
> if (par5=='') par5 <- 0
> par5 <- as.numeric(par5)
> x <- na.omit(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'){
+ (n <- n -1)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par3 == 'Seasonal Differences (s=12)'){
+ (n <- n - 12)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+12,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par3 == 'First and Seasonal Differences (s=12)'){
+ (n <- n -1)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ (n <- n - 12)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+12,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if(par4 > 0) {
+ x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
+ for (i in 1:(n-par4)) {
+ for (j in 1:par4) {
+ x2[i,j] <- x[i+par4-j,par1]
+ }
+ }
+ x <- cbind(x[(par4+1):n,], x2)
+ n <- n - par4
+ }
> if(par5 > 0) {
+ x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
+ for (i in 1:(n-par5*12)) {
+ for (j in 1:par5) {
+ x2[i,j] <- x[i+par5*12-j*12,par1]
+ }
+ }
+ x <- cbind(x[(par5*12+1):n,], x2)
+ n <- n - par5*12
+ }
> 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[n,]))
[1] 3
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
stock asset income
1 -0.24 0.13 0.08
2 -0.51 0.10 0.01
3 0.78 0.21 0.08
4 -0.30 0.16 0.02
5 0.11 0.12 0.03
6 1.37 0.17 0.05
7 -0.07 0.18 0.03
8 -0.23 0.13 0.01
9 0.74 0.20 0.05
10 0.14 0.83 0.04
11 0.06 0.83 0.02
12 -0.39 0.80 -0.02
13 -0.15 0.81 -0.02
14 0.11 0.86 0.01
15 -0.23 0.85 -0.01
16 -0.12 0.86 -0.06
17 -0.12 0.86 -0.09
18 -0.35 0.83 -0.05
19 -0.45 0.88 -0.10
20 -0.05 0.87 0.12
21 -0.21 0.91 0.08
22 0.20 0.92 0.10
23 0.06 0.93 0.06
24 -0.23 0.73 -0.01
25 -0.10 0.67 0.02
26 0.32 0.67 0.04
27 0.12 0.70 0.00
28 -0.30 0.07 0.01
29 -0.15 0.24 0.08
30 -0.41 0.23 0.06
31 -0.23 0.26 0.04
32 0.21 0.37 0.08
33 0.40 0.45 0.06
34 1.86 0.49 0.01
35 -0.01 0.53 0.01
36 0.07 0.54 0.06
37 -0.46 0.51 -0.03
38 -0.66 0.37 0.03
39 0.45 0.57 0.00
40 1.33 0.57 0.07
41 -0.34 0.55 0.05
42 -0.17 0.59 -0.03
43 1.21 0.60 0.02
44 -0.36 0.67 0.02
45 -0.14 0.62 0.02
46 -0.46 0.25 0.18
47 -0.65 0.18 0.19
48 0.98 0.21 0.18
49 0.68 0.25 0.23
50 -0.47 0.29 0.25
51 0.55 0.32 0.27
52 0.79 0.35 0.15
53 -0.41 0.42 0.09
54 -0.30 0.43 0.13
55 1.29 0.72 0.34
56 -0.75 0.68 0.10
57 0.22 0.76 0.08
58 -0.08 0.80 -0.16
59 -0.12 0.82 -0.27
60 -0.18 0.82 -0.41
61 0.08 0.84 -0.37
62 -0.38 0.85 -0.54
63 0.00 0.79 -1.02
64 -0.68 0.51 -0.21
65 -0.83 0.33 -0.46
66 4.62 0.34 -0.09
67 0.09 0.32 -0.02
68 0.41 0.49 0.05
69 1.55 0.78 0.09
70 1.93 0.74 0.08
71 -0.40 0.75 0.10
72 0.18 0.51 0.03
73 -0.12 0.53 0.03
74 0.20 0.57 0.03
75 0.45 0.59 0.04
76 0.01 0.25 0.03
77 0.44 0.29 0.03
78 0.03 0.30 0.03
79 0.19 0.31 0.03
80 0.20 0.35 0.04
81 0.11 0.67 0.13
82 -0.28 0.67 0.12
83 -0.25 0.70 0.09
84 0.95 0.71 0.13
85 0.15 0.70 0.15
86 0.26 0.69 0.17
87 -0.06 0.65 0.15
88 0.19 0.62 0.14
89 0.10 0.41 0.13
90 -0.12 0.80 0.18
91 -0.47 0.83 0.18
92 1.51 0.79 0.19
93 0.62 0.78 0.19
94 -0.12 0.72 0.19
95 0.15 0.74 0.19
96 0.37 0.75 0.19
97 0.04 0.67 0.18
98 0.14 0.71 0.17
99 0.41 0.73 0.15
100 -0.38 0.71 0.11
101 0.77 0.71 0.11
102 0.64 0.69 0.17
103 -0.11 0.67 0.19
104 0.08 0.65 0.15
105 -0.04 0.64 0.13
106 0.01 0.39 0.10
107 0.19 0.36 0.11
108 -0.35 0.73 0.06
109 -0.38 0.55 0.06
110 0.13 0.69 0.03
111 -0.15 0.72 0.00
112 -0.34 0.72 0.01
113 -0.14 0.68 -0.06
114 1.08 0.65 -0.02
115 0.25 0.40 -0.02
116 0.31 0.39 0.03
117 1.19 1.29 0.00
118 -0.32 2.20 -0.14
119 1.64 2.23 -0.22
120 0.76 1.90 0.10
121 -0.09 0.97 0.15
122 -0.38 0.27 -0.02
123 -0.02 0.30 -0.08
124 0.42 0.37 0.03
125 -0.33 2.10 -0.21
126 0.05 0.89 0.12
127 -0.28 0.85 0.10
128 -0.16 0.89 0.02
129 0.11 0.90 0.05
130 0.16 0.92 0.08
131 -0.08 0.92 0.06
132 -0.02 0.91 0.04
133 -0.25 0.91 0.00
134 0.57 0.92 0.03
135 1.58 0.75 -0.14
136 -0.61 0.71 -0.20
137 0.42 0.70 -0.30
138 -0.12 0.65 -0.27
139 -0.52 0.59 -0.07
140 -0.50 0.68 -0.08
141 -0.12 0.64 -0.27
142 -0.21 0.60 -0.39
143 -0.67 0.76 -0.35
144 -0.20 0.82 0.07
145 -0.75 0.56 0.01
146 1.41 0.69 -0.12
147 0.41 0.77 0.15
148 0.17 0.84 0.17
149 0.05 0.86 0.14
150 0.26 0.86 0.14
151 0.14 0.42 0.02
152 0.00 0.48 0.11
153 1.04 0.13 0.05
154 -0.25 0.20 0.06
155 0.57 0.30 0.06
156 0.25 0.34 0.05
157 -0.17 0.16 0.05
158 0.21 0.40 0.03
159 0.30 0.41 0.03
160 0.21 0.37 0.04
161 0.03 0.33 0.04
162 -0.89 0.18 -0.05
163 -0.74 0.19 -0.19
164 3.80 0.05 -0.20
165 0.41 0.16 -0.21
166 -0.05 0.27 -0.23
167 0.06 0.16 -0.53
168 0.72 0.34 0.06
169 -0.49 0.03 -0.01
170 -0.80 0.09 -0.06
171 0.78 0.71 0.29
172 -0.80 0.76 0.25
173 0.60 0.74 0.27
174 -0.02 0.76 0.24
175 0.31 0.73 0.20
176 0.02 0.73 0.22
177 0.15 0.75 0.22
178 0.15 0.73 0.24
179 -0.31 0.74 0.19
180 -0.20 0.49 -6.39
181 0.17 0.30 -4.03
182 -0.75 -0.15 -3.17
183 -0.37 0.81 0.23
184 0.27 0.91 -4.45
185 0.03 0.64 0.12
186 -0.40 0.55 0.09
187 -0.29 0.58 0.09
188 0.56 0.63 0.09
189 0.53 0.64 0.11
190 0.51 0.65 0.13
191 0.09 0.63 0.14
192 0.13 0.73 0.13
193 -0.22 0.72 0.13
194 0.53 0.42 0.16
195 -0.16 0.60 0.19
196 0.21 0.60 0.19
197 1.06 0.65 0.23
198 -0.52 0.49 0.19
199 0.80 0.51 0.17
200 1.02 0.51 0.09
201 0.71 0.44 0.28
202 0.02 0.51 0.28
203 0.04 0.90 -0.02
204 -0.44 0.91 0.02
205 -0.44 0.82 -1.57
206 0.26 0.80 -0.07
207 0.21 0.82 0.12
208 -0.03 0.86 0.04
209 0.04 0.86 -0.02
210 0.60 0.88 0.06
211 0.66 0.86 0.19
212 -0.31 0.82 -0.04
213 -0.70 0.72 -0.04
214 -0.15 0.54 -0.08
215 0.20 0.51 -0.02
216 0.14 0.59 -0.02
217 0.29 0.61 -0.03
218 -0.14 0.53 -0.19
219 -0.13 0.70 -0.28
220 -0.27 0.60 -0.24
221 1.36 0.67 0.08
222 -0.21 0.67 0.08
223 -0.53 0.64 -0.01
224 0.15 0.62 0.05
225 -0.13 0.62 0.02
226 -0.14 0.69 0.01
227 0.43 0.64 0.04
228 -0.04 0.69 -0.07
229 -0.11 0.64 -0.07
230 0.62 0.79 0.04
231 -0.41 0.77 0.06
232 -0.16 0.83 0.05
233 -0.09 0.66 0.03
234 -0.06 0.67 0.03
235 0.21 0.55 0.05
236 0.96 0.62 0.06
237 -0.13 0.65 0.04
238 0.19 0.64 0.03
239 0.05 0.75 0.10
240 -0.55 0.81 0.06
241 -0.33 0.85 -0.06
242 -0.10 0.83 0.01
243 0.63 0.86 0.06
244 -0.10 0.85 0.02
245 0.14 0.84 0.00
246 -0.01 0.81 0.01
247 0.17 0.78 0.02
248 0.07 0.38 0.04
249 -0.59 0.35 0.04
250 1.14 0.39 0.05
251 0.25 0.35 0.04
252 -0.12 0.32 0.04
253 -0.10 0.35 0.05
254 0.38 0.24 0.05
255 -0.37 0.32 0.04
256 -0.19 0.29 0.07
257 -0.21 0.47 -0.15
258 -0.38 0.43 -0.02
259 0.23 0.66 0.04
260 0.27 0.80 0.09
261 0.59 0.85 0.11
262 -0.31 0.86 0.12
263 -0.29 0.88 -0.13
264 0.23 0.88 -0.04
265 0.29 0.90 0.08
266 -0.31 0.45 0.34
267 -0.22 0.36 0.34
268 -0.02 0.40 0.28
269 0.40 0.42 0.27
270 -0.11 0.44 0.26
271 0.19 0.47 0.25
272 0.98 0.51 0.16
273 -0.02 0.41 0.22
274 -0.23 0.39 0.07
275 0.18 0.77 0.14
276 -0.07 0.83 0.13
277 -0.04 0.84 0.13
278 0.17 0.83 0.15
279 0.12 0.72 0.05
280 0.23 0.65 0.15
281 1.01 0.69 0.17
282 0.15 0.78 0.14
283 0.51 -0.71 0.20
284 0.97 -0.13 0.14
285 -0.94 -0.26 0.14
286 6.94 -0.28 0.03
287 0.48 -0.29 0.22
288 0.31 -0.32 0.16
289 0.35 0.00 0.26
290 4.26 -0.01 0.21
291 -0.07 0.04 0.27
292 0.13 0.05 0.23
293 0.46 0.48 -0.02
294 -0.60 0.44 -0.18
295 1.30 0.39 -0.02
296 -0.41 0.40 -0.03
297 -0.38 0.50 0.04
298 0.29 0.53 0.00
299 0.35 0.34 -0.03
300 0.01 0.32 -0.03
301 -0.26 0.27 -0.02
302 0.46 0.47 0.06
303 0.25 0.45 0.07
304 -0.45 0.34 0.06
305 0.51 0.36 0.06
306 0.09 0.40 0.06
307 0.20 0.40 0.05
308 0.63 0.30 0.07
309 0.15 0.31 0.09
310 0.22 0.36 0.09
311 0.23 0.81 0.03
312 -0.12 0.80 0.03
313 -0.61 0.85 -0.06
314 0.28 0.83 -0.07
315 0.45 0.79 0.07
316 -0.14 0.78 0.10
317 0.43 0.77 0.11
318 0.62 0.78 0.05
319 -0.17 0.64 0.00
320 -0.99 0.38 0.46
321 -0.52 0.30 0.47
322 0.58 0.28 0.50
323 0.40 0.27 0.43
324 -0.07 0.30 0.47
325 0.02 0.22 0.47
326 0.00 0.27 0.41
327 -0.04 0.28 0.18
328 -0.15 0.28 0.39
329 -0.65 0.38 0.31
330 -0.81 0.28 0.24
331 5.73 0.24 -0.02
332 -0.62 0.23 0.05
333 -0.17 0.29 0.07
334 -0.24 0.25 0.17
335 4.28 0.21 0.16
336 -0.57 0.17 0.17
337 0.01 -0.04 0.08
338 -0.17 0.43 0.27
339 -0.61 0.47 0.19
340 1.53 0.56 0.23
341 -0.09 0.39 0.11
342 0.27 0.37 0.03
343 -0.14 0.42 0.06
344 0.33 0.53 0.03
345 0.31 0.58 0.01
346 0.10 0.69 0.25
347 0.20 0.78 -0.08
348 -0.30 0.76 -0.04
349 -0.16 0.74 0.05
350 0.09 0.67 0.01
351 0.28 0.69 0.01
352 -0.13 0.71 -0.01
353 0.55 0.76 -0.03
354 0.07 0.80 -0.01
355 -0.41 0.81 -0.23
356 0.64 0.83 0.16
357 -0.58 0.83 0.17
358 0.60 0.85 0.06
359 0.89 0.85 0.11
360 -0.07 0.83 0.19
361 1.64 0.91 0.08
362 0.68 0.90 -0.06
363 -0.38 0.87 -0.20
364 -0.72 0.84 -1.97
365 0.44 0.76 0.19
366 -0.40 0.77 0.24
367 0.65 0.85 0.30
368 0.26 0.83 0.23
369 -0.02 0.83 0.22
370 0.15 0.78 0.29
371 0.70 0.72 0.32
372 -0.01 0.77 0.29
373 0.01 0.81 0.29
374 -0.15 0.68 0.09
375 -0.36 0.58 0.07
376 0.59 0.64 0.08
377 0.49 0.71 0.09
378 -0.13 0.67 0.09
379 0.11 0.67 0.09
380 0.65 0.68 0.08
381 0.22 0.65 0.08
382 0.07 0.66 0.08
383 -0.38 0.86 0.20
384 -0.42 0.83 0.13
385 -0.46 0.87 -0.53
386 -0.07 0.83 -3.07
387 -0.02 0.77 -3.61
388 -0.59 0.67 -3.72
389 -0.29 0.35 -4.72
390 -0.27 0.38 -5.30
391 1.12 -0.12 -7.03
392 0.03 0.66 0.09
393 -0.55 0.61 0.10
394 1.21 0.63 0.03
395 0.29 0.47 0.11
396 -0.27 0.53 0.06
397 -0.16 0.45 -0.11
398 -0.07 0.42 0.05
399 -0.12 0.45 0.04
400 -0.10 0.31 0.03
401 -0.11 0.75 0.13
402 0.19 0.75 0.15
403 -0.46 0.79 0.16
404 1.03 0.79 0.17
405 0.07 0.79 0.18
406 -0.22 0.72 0.12
407 0.62 0.75 0.13
408 -0.16 0.76 0.14
409 -0.03 0.70 0.11
410 -0.34 0.88 -7.30
411 -0.67 0.82 -4.69
412 1.33 0.82 -2.25
413 -0.39 0.88 -3.58
414 1.18 0.88 -0.26
415 -0.34 0.92 -1.34
416 -0.59 0.93 -4.06
417 -0.11 0.90 -1.09
418 -0.61 0.91 -0.45
419 -0.09 0.78 0.10
420 -0.48 0.82 0.08
421 0.38 0.84 0.06
422 -0.01 0.84 0.12
423 -0.03 0.85 0.16
424 0.23 0.85 0.11
425 1.03 0.78 0.13
426 0.25 0.63 0.03
427 -0.34 0.64 -0.96
428 -0.43 0.25 0.01
429 -0.23 0.20 0.03
430 -0.17 0.17 0.00
431 0.47 0.20 0.03
432 -0.44 0.23 0.03
433 0.27 0.30 0.02
434 1.06 0.36 0.03
435 0.23 0.38 0.03
436 -0.01 0.44 0.03
437 -0.48 0.61 0.01
438 -0.49 0.60 -0.03
439 0.77 0.70 0.02
440 0.31 0.72 0.02
441 0.10 0.76 0.03
442 -0.08 0.78 0.04
443 0.32 0.79 0.02
444 -0.12 0.75 0.01
445 -0.32 0.76 0.01
446 99.20 96.70 101.00
447 99.00 98.10 100.10
448 100.00 100.00 100.00
449 111.60 104.90 90.60
450 122.20 104.90 86.50
451 117.60 109.50 89.70
452 121.10 110.80 90.60
453 136.00 112.30 82.80
454 154.20 109.30 70.10
455 153.60 105.30 65.40
456 158.50 101.70 61.30
457 140.60 95.40 62.50
458 136.20 96.40 63.60
459 168.00 97.60 52.60
460 154.30 102.40 59.70
461 149.00 101.60 59.50
462 165.50 103.80 61.30
> (k <- length(x[n,]))
[1] 3
> head(x)
stock asset income
1 -0.24 0.13 0.08
2 -0.51 0.10 0.01
3 0.78 0.21 0.08
4 -0.30 0.16 0.02
5 0.11 0.12 0.03
6 1.37 0.17 0.05
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) asset income
-1.296 2.233 -1.239
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.4943 -0.4833 0.0625 0.6712 16.5261
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.29637 0.10915 -11.88 <2e-16 ***
asset 2.23276 0.02491 89.62 <2e-16 ***
income -1.23853 0.03260 -37.99 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.257 on 459 degrees of freedom
Multiple R-squared: 0.9923, Adjusted R-squared: 0.9923
F-statistic: 2.967e+04 on 2 and 459 DF, p-value: < 2.2e-16
> 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,] 3.409916e-02 6.819832e-02 0.9659008
[2,] 1.123067e-02 2.246134e-02 0.9887693
[3,] 2.660412e-03 5.320824e-03 0.9973396
[4,] 5.944152e-04 1.188830e-03 0.9994056
[5,] 4.071291e-04 8.142582e-04 0.9995929
[6,] 9.350713e-05 1.870143e-04 0.9999065
[7,] 2.036783e-05 4.073567e-05 0.9999796
[8,] 4.637104e-06 9.274208e-06 0.9999954
[9,] 9.401047e-07 1.880209e-06 0.9999991
[10,] 1.805492e-07 3.610983e-07 0.9999998
[11,] 6.084077e-08 1.216815e-07 0.9999999
[12,] 2.037393e-08 4.074785e-08 1.0000000
[13,] 3.895123e-09 7.790246e-09 1.0000000
[14,] 7.324962e-10 1.464992e-09 1.0000000
[15,] 2.851399e-10 5.702799e-10 1.0000000
[16,] 7.100164e-11 1.420033e-10 1.0000000
[17,] 1.278602e-11 2.557204e-11 1.0000000
[18,] 2.233504e-12 4.467007e-12 1.0000000
[19,] 3.962435e-13 7.924870e-13 1.0000000
[20,] 6.737139e-14 1.347428e-13 1.0000000
[21,] 1.408559e-14 2.817118e-14 1.0000000
[22,] 2.565253e-15 5.130507e-15 1.0000000
[23,] 6.649819e-16 1.329964e-15 1.0000000
[24,] 1.707162e-16 3.414324e-16 1.0000000
[25,] 7.196052e-17 1.439210e-16 1.0000000
[26,] 1.492770e-17 2.985540e-17 1.0000000
[27,] 2.437215e-18 4.874430e-18 1.0000000
[28,] 5.389927e-19 1.077985e-18 1.0000000
[29,] 7.757145e-16 1.551429e-15 1.0000000
[30,] 1.617710e-16 3.235420e-16 1.0000000
[31,] 3.343002e-17 6.686003e-17 1.0000000
[32,] 8.938868e-18 1.787774e-17 1.0000000
[33,] 5.119191e-18 1.023838e-17 1.0000000
[34,] 1.706522e-18 3.413045e-18 1.0000000
[35,] 4.514067e-18 9.028134e-18 1.0000000
[36,] 1.457985e-18 2.915969e-18 1.0000000
[37,] 3.153266e-19 6.306533e-19 1.0000000
[38,] 7.292253e-19 1.458451e-18 1.0000000
[39,] 2.176242e-19 4.352484e-19 1.0000000
[40,] 5.127769e-20 1.025554e-19 1.0000000
[41,] 4.204624e-20 8.409247e-20 1.0000000
[42,] 3.365858e-20 6.731716e-20 1.0000000
[43,] 2.041765e-20 4.083530e-20 1.0000000
[44,] 5.600719e-21 1.120144e-20 1.0000000
[45,] 3.604792e-21 7.209584e-21 1.0000000
[46,] 9.332927e-22 1.866585e-21 1.0000000
[47,] 3.726523e-22 7.453046e-22 1.0000000
[48,] 1.342261e-22 2.684522e-22 1.0000000
[49,] 4.313587e-23 8.627174e-23 1.0000000
[50,] 2.390349e-23 4.780698e-23 1.0000000
[51,] 1.989186e-23 3.978373e-23 1.0000000
[52,] 4.707290e-24 9.414581e-24 1.0000000
[53,] 1.171021e-24 2.342042e-24 1.0000000
[54,] 3.138565e-25 6.277130e-25 1.0000000
[55,] 8.888160e-26 1.777632e-25 1.0000000
[56,] 2.839661e-26 5.679322e-26 1.0000000
[57,] 6.731625e-27 1.346325e-26 1.0000000
[58,] 3.129905e-27 6.259810e-27 1.0000000
[59,] 1.285967e-27 2.571935e-27 1.0000000
[60,] 4.860331e-28 9.720662e-28 1.0000000
[61,] 4.534308e-19 9.068617e-19 1.0000000
[62,] 1.490516e-19 2.981032e-19 1.0000000
[63,] 5.128807e-20 1.025761e-19 1.0000000
[64,] 7.259202e-20 1.451840e-19 1.0000000
[65,] 2.005529e-19 4.011058e-19 1.0000000
[66,] 8.559177e-20 1.711835e-19 1.0000000
[67,] 2.866566e-20 5.733132e-20 1.0000000
[68,] 1.000671e-20 2.001341e-20 1.0000000
[69,] 3.299401e-21 6.598802e-21 1.0000000
[70,] 1.144822e-21 2.289644e-21 1.0000000
[71,] 3.815348e-22 7.630696e-22 1.0000000
[72,] 1.317294e-22 2.634588e-22 1.0000000
[73,] 4.303466e-23 8.606932e-23 1.0000000
[74,] 1.380006e-23 2.760012e-23 1.0000000
[75,] 4.386054e-24 8.772107e-24 1.0000000
[76,] 1.379959e-24 2.759917e-24 1.0000000
[77,] 5.103479e-25 1.020696e-24 1.0000000
[78,] 1.805682e-25 3.611364e-25 1.0000000
[79,] 8.729064e-26 1.745813e-25 1.0000000
[80,] 2.682681e-26 5.365362e-26 1.0000000
[81,] 8.170729e-27 1.634146e-26 1.0000000
[82,] 2.622565e-27 5.245129e-27 1.0000000
[83,] 7.880828e-28 1.576166e-27 1.0000000
[84,] 2.393316e-28 4.786631e-28 1.0000000
[85,] 7.773264e-29 1.554653e-28 1.0000000
[86,] 3.322584e-29 6.645168e-29 1.0000000
[87,] 4.092923e-29 8.185846e-29 1.0000000
[88,] 1.371640e-29 2.743280e-29 1.0000000
[89,] 4.519871e-30 9.039742e-30 1.0000000
[90,] 1.334880e-30 2.669759e-30 1.0000000
[91,] 3.955749e-31 7.911499e-31 1.0000000
[92,] 1.183944e-31 2.367888e-31 1.0000000
[93,] 3.429262e-32 6.858524e-32 1.0000000
[94,] 1.016821e-32 2.033641e-32 1.0000000
[95,] 3.857320e-33 7.714639e-33 1.0000000
[96,] 1.490753e-33 2.981507e-33 1.0000000
[97,] 4.928613e-34 9.857227e-34 1.0000000
[98,] 1.552892e-34 3.105784e-34 1.0000000
[99,] 4.423306e-35 8.846612e-35 1.0000000
[100,] 1.299309e-35 2.598618e-35 1.0000000
[101,] 3.742613e-36 7.485225e-36 1.0000000
[102,] 1.046498e-36 2.092996e-36 1.0000000
[103,] 3.653020e-37 7.306039e-37 1.0000000
[104,] 1.309711e-37 2.619421e-37 1.0000000
[105,] 3.554334e-38 7.108668e-38 1.0000000
[106,] 1.026656e-38 2.053312e-38 1.0000000
[107,] 3.379807e-39 6.759613e-39 1.0000000
[108,] 9.460062e-40 1.892012e-39 1.0000000
[109,] 6.571400e-40 1.314280e-39 1.0000000
[110,] 1.800992e-40 3.601985e-40 1.0000000
[111,] 4.976277e-41 9.952553e-41 1.0000000
[112,] 4.087180e-41 8.174360e-41 1.0000000
[113,] 1.825723e-41 3.651445e-41 1.0000000
[114,] 4.546139e-41 9.092277e-41 1.0000000
[115,] 1.460920e-41 2.921840e-41 1.0000000
[116,] 4.607983e-42 9.215967e-42 1.0000000
[117,] 1.485372e-42 2.970744e-42 1.0000000
[118,] 3.989739e-43 7.979479e-43 1.0000000
[119,] 1.190795e-43 2.381591e-43 1.0000000
[120,] 6.889802e-44 1.377960e-43 1.0000000
[121,] 1.898168e-44 3.796336e-44 1.0000000
[122,] 6.504817e-45 1.300963e-44 1.0000000
[123,] 1.943925e-45 3.887851e-45 1.0000000
[124,] 5.135132e-46 1.027026e-45 1.0000000
[125,] 1.341781e-46 2.683563e-46 1.0000000
[126,] 3.804839e-47 7.609678e-47 1.0000000
[127,] 1.032581e-47 2.065162e-47 1.0000000
[128,] 3.222552e-48 6.445104e-48 1.0000000
[129,] 9.605314e-49 1.921063e-48 1.0000000
[130,] 2.513443e-48 5.026885e-48 1.0000000
[131,] 1.042493e-48 2.084986e-48 1.0000000
[132,] 3.193790e-49 6.387579e-49 1.0000000
[133,] 8.395878e-50 1.679176e-49 1.0000000
[134,] 3.185391e-50 6.370782e-50 1.0000000
[135,] 1.188019e-50 2.376039e-50 1.0000000
[136,] 3.075273e-51 6.150546e-51 1.0000000
[137,] 8.000977e-52 1.600195e-51 1.0000000
[138,] 3.175885e-52 6.351770e-52 1.0000000
[139,] 9.415011e-53 1.883002e-52 1.0000000
[140,] 5.177854e-53 1.035571e-52 1.0000000
[141,] 8.745224e-53 1.749045e-52 1.0000000
[142,] 2.284829e-53 4.569658e-53 1.0000000
[143,] 5.772482e-54 1.154496e-53 1.0000000
[144,] 1.497626e-54 2.995252e-54 1.0000000
[145,] 3.727910e-55 7.455820e-55 1.0000000
[146,] 9.309554e-56 1.861911e-55 1.0000000
[147,] 2.364419e-56 4.728837e-56 1.0000000
[148,] 1.732866e-56 3.465733e-56 1.0000000
[149,] 4.958151e-57 9.916303e-57 1.0000000
[150,] 1.583614e-57 3.167228e-57 1.0000000
[151,] 4.026923e-58 8.053847e-58 1.0000000
[152,] 1.085555e-58 2.171111e-58 1.0000000
[153,] 2.689830e-59 5.379661e-59 1.0000000
[154,] 6.831165e-60 1.366233e-59 1.0000000
[155,] 1.683213e-60 3.366425e-60 1.0000000
[156,] 4.112403e-61 8.224805e-61 1.0000000
[157,] 2.549250e-61 5.098500e-61 1.0000000
[158,] 1.016398e-61 2.032796e-61 1.0000000
[159,] 8.965998e-56 1.793200e-55 1.0000000
[160,] 2.733600e-56 5.467200e-56 1.0000000
[161,] 7.266451e-57 1.453290e-56 1.0000000
[162,] 1.916352e-57 3.832704e-57 1.0000000
[163,] 7.006588e-58 1.401318e-57 1.0000000
[164,] 2.642886e-58 5.285771e-58 1.0000000
[165,] 1.433476e-58 2.866952e-58 1.0000000
[166,] 4.875942e-59 9.751884e-59 1.0000000
[167,] 3.410909e-59 6.821818e-59 1.0000000
[168,] 1.002181e-59 2.004362e-59 1.0000000
[169,] 2.721658e-60 5.443317e-60 1.0000000
[170,] 7.029502e-61 1.405900e-60 1.0000000
[171,] 1.854077e-61 3.708154e-61 1.0000000
[172,] 4.715030e-62 9.430059e-62 1.0000000
[173,] 1.195373e-62 2.390747e-62 1.0000000
[174,] 3.855013e-63 7.710026e-63 1.0000000
[175,] 1.416680e-62 2.833360e-62 1.0000000
[176,] 7.886806e-63 1.577361e-62 1.0000000
[177,] 4.213000e-63 8.426000e-63 1.0000000
[178,] 1.418813e-63 2.837626e-63 1.0000000
[179,] 1.912236e-63 3.824472e-63 1.0000000
[180,] 4.919393e-64 9.838786e-64 1.0000000
[181,] 1.642799e-64 3.285598e-64 1.0000000
[182,] 4.937769e-65 9.875539e-65 1.0000000
[183,] 1.485673e-65 2.971347e-65 1.0000000
[184,] 4.337662e-66 8.675324e-66 1.0000000
[185,] 1.240417e-66 2.480835e-66 1.0000000
[186,] 3.095770e-67 6.191539e-67 1.0000000
[187,] 7.661591e-68 1.532318e-67 1.0000000
[188,] 2.172743e-68 4.345486e-68 1.0000000
[189,] 6.525651e-69 1.305130e-68 1.0000000
[190,] 1.754436e-69 3.508872e-69 1.0000000
[191,] 4.321073e-70 8.642147e-70 1.0000000
[192,] 2.508459e-70 5.016919e-70 1.0000000
[193,] 9.457423e-71 1.891485e-70 1.0000000
[194,] 3.720588e-71 7.441176e-71 1.0000000
[195,] 2.066173e-71 4.132346e-71 1.0000000
[196,] 7.361461e-72 1.472292e-71 1.0000000
[197,] 1.839141e-72 3.678281e-72 1.0000000
[198,] 4.573950e-73 9.147900e-73 1.0000000
[199,] 1.660628e-73 3.321255e-73 1.0000000
[200,] 7.447087e-74 1.489417e-73 1.0000000
[201,] 1.807670e-74 3.615341e-74 1.0000000
[202,] 4.307794e-75 8.615587e-75 1.0000000
[203,] 1.071552e-75 2.143103e-75 1.0000000
[204,] 2.597159e-76 5.194318e-76 1.0000000
[205,] 7.447962e-77 1.489592e-76 1.0000000
[206,] 2.236658e-77 4.473316e-77 1.0000000
[207,] 6.734265e-78 1.346853e-77 1.0000000
[208,] 3.437249e-78 6.874499e-78 1.0000000
[209,] 8.675510e-79 1.735102e-78 1.0000000
[210,] 2.024947e-79 4.049894e-79 1.0000000
[211,] 4.662987e-80 9.325973e-80 1.0000000
[212,] 1.096337e-80 2.192673e-80 1.0000000
[213,] 2.701426e-81 5.402853e-81 1.0000000
[214,] 6.749188e-82 1.349838e-81 1.0000000
[215,] 1.829216e-82 3.658432e-82 1.0000000
[216,] 2.101187e-82 4.202374e-82 1.0000000
[217,] 5.480261e-83 1.096052e-82 1.0000000
[218,] 2.029320e-83 4.058640e-83 1.0000000
[219,] 4.574010e-84 9.148019e-84 1.0000000
[220,] 1.109928e-84 2.219855e-84 1.0000000
[221,] 2.713710e-85 5.427420e-85 1.0000000
[222,] 6.641656e-86 1.328331e-85 1.0000000
[223,] 1.531520e-86 3.063041e-86 1.0000000
[224,] 3.619825e-87 7.239650e-87 1.0000000
[225,] 1.017246e-87 2.034492e-87 1.0000000
[226,] 3.211026e-88 6.422052e-88 1.0000000
[227,] 7.942531e-89 1.588506e-88 1.0000000
[228,] 1.834371e-89 3.668742e-89 1.0000000
[229,] 4.160124e-90 8.320248e-90 1.0000000
[230,] 9.089187e-91 1.817837e-90 1.0000000
[231,] 4.297772e-91 8.595545e-91 1.0000000
[232,] 1.003140e-91 2.006281e-91 1.0000000
[233,] 2.153507e-92 4.307014e-92 1.0000000
[234,] 4.646054e-93 9.292108e-93 1.0000000
[235,] 1.772792e-93 3.545583e-93 1.0000000
[236,] 5.034200e-94 1.006840e-93 1.0000000
[237,] 1.157363e-94 2.314725e-94 1.0000000
[238,] 3.193113e-95 6.386226e-95 1.0000000
[239,] 7.323406e-96 1.464681e-95 1.0000000
[240,] 1.548331e-96 3.096663e-96 1.0000000
[241,] 3.349255e-97 6.698509e-97 1.0000000
[242,] 6.975875e-98 1.395175e-97 1.0000000
[243,] 1.467433e-98 2.934867e-98 1.0000000
[244,] 5.417983e-99 1.083597e-98 1.0000000
[245,] 4.125309e-99 8.250618e-99 1.0000000
[246,] 8.919352e-100 1.783870e-99 1.0000000
[247,] 1.985420e-100 3.970840e-100 1.0000000
[248,] 4.345328e-101 8.690656e-101 1.0000000
[249,] 1.029698e-101 2.059397e-101 1.0000000
[250,] 2.777643e-102 5.555286e-102 1.0000000
[251,] 6.386357e-103 1.277271e-102 1.0000000
[252,] 1.444751e-103 2.889502e-103 1.0000000
[253,] 3.850697e-104 7.701394e-104 1.0000000
[254,] 7.760198e-105 1.552040e-104 1.0000000
[255,] 1.571147e-105 3.142295e-105 1.0000000
[256,] 3.929678e-106 7.859356e-106 1.0000000
[257,] 1.021130e-106 2.042261e-106 1.0000000
[258,] 2.642215e-107 5.284430e-107 1.0000000
[259,] 5.309947e-108 1.061989e-107 1.0000000
[260,] 1.069301e-108 2.138603e-108 1.0000000
[261,] 2.673656e-109 5.347312e-109 1.0000000
[262,] 6.196130e-110 1.239226e-109 1.0000000
[263,] 1.266234e-110 2.532468e-110 1.0000000
[264,] 2.844284e-111 5.688569e-111 1.0000000
[265,] 5.931554e-112 1.186311e-111 1.0000000
[266,] 1.171996e-112 2.343991e-112 1.0000000
[267,] 5.957105e-113 1.191421e-112 1.0000000
[268,] 1.190147e-113 2.380294e-113 1.0000000
[269,] 2.613444e-114 5.226888e-114 1.0000000
[270,] 4.908414e-115 9.816827e-115 1.0000000
[271,] 9.785217e-116 1.957043e-115 1.0000000
[272,] 1.914072e-116 3.828143e-116 1.0000000
[273,] 3.552027e-117 7.104053e-117 1.0000000
[274,] 6.544956e-118 1.308991e-117 1.0000000
[275,] 1.219235e-118 2.438471e-118 1.0000000
[276,] 6.102997e-119 1.220599e-118 1.0000000
[277,] 1.113357e-119 2.226714e-119 1.0000000
[278,] 6.119840e-120 1.223968e-119 1.0000000
[279,] 4.446837e-120 8.893674e-120 1.0000000
[280,] 3.667816e-120 7.335631e-120 1.0000000
[281,] 9.887707e-99 1.977541e-98 1.0000000
[282,] 3.863035e-99 7.726071e-99 1.0000000
[283,] 1.401099e-99 2.802198e-99 1.0000000
[284,] 4.256939e-100 8.513877e-100 1.0000000
[285,] 2.638869e-94 5.277738e-94 1.0000000
[286,] 8.502789e-95 1.700558e-94 1.0000000
[287,] 2.569735e-95 5.139470e-95 1.0000000
[288,] 6.600744e-96 1.320149e-95 1.0000000
[289,] 2.423929e-96 4.847858e-96 1.0000000
[290,] 1.764360e-96 3.528719e-96 1.0000000
[291,] 5.408461e-97 1.081692e-96 1.0000000
[292,] 1.572991e-97 3.145982e-97 1.0000000
[293,] 3.709529e-98 7.419058e-98 1.0000000
[294,] 9.256898e-99 1.851380e-98 1.0000000
[295,] 2.265428e-99 4.530856e-99 1.0000000
[296,] 6.353719e-100 1.270744e-99 1.0000000
[297,] 1.619591e-100 3.239181e-100 1.0000000
[298,] 3.824339e-101 7.648677e-101 1.0000000
[299,] 1.223681e-101 2.447363e-101 1.0000000
[300,] 3.301172e-102 6.602343e-102 1.0000000
[301,] 7.744246e-103 1.548849e-102 1.0000000
[302,] 1.813658e-103 3.627316e-103 1.0000000
[303,] 5.465281e-104 1.093056e-103 1.0000000
[304,] 1.326870e-104 2.653739e-104 1.0000000
[305,] 3.168018e-105 6.336036e-105 1.0000000
[306,] 6.864391e-106 1.372878e-105 1.0000000
[307,] 1.530165e-106 3.060329e-106 1.0000000
[308,] 5.105759e-107 1.021152e-106 1.0000000
[309,] 1.110077e-107 2.220154e-107 1.0000000
[310,] 2.585225e-108 5.170450e-108 1.0000000
[311,] 5.712446e-109 1.142489e-108 1.0000000
[312,] 1.305796e-109 2.611592e-109 1.0000000
[313,] 3.404426e-110 6.808853e-110 1.0000000
[314,] 7.619328e-111 1.523866e-110 1.0000000
[315,] 5.510041e-111 1.102008e-110 1.0000000
[316,] 2.035732e-111 4.071464e-111 1.0000000
[317,] 6.753344e-112 1.350669e-111 1.0000000
[318,] 1.943839e-112 3.887677e-112 1.0000000
[319,] 5.267180e-113 1.053436e-112 1.0000000
[320,] 1.485896e-113 2.971791e-113 1.0000000
[321,] 3.956625e-114 7.913249e-114 1.0000000
[322,] 9.733118e-115 1.946624e-114 1.0000000
[323,] 2.661099e-115 5.322198e-115 1.0000000
[324,] 1.021211e-115 2.042421e-115 1.0000000
[325,] 5.090742e-116 1.018148e-115 1.0000000
[326,] 3.014531e-105 6.029063e-105 1.0000000
[327,] 1.155867e-105 2.311734e-105 1.0000000
[328,] 3.098352e-106 6.196705e-106 1.0000000
[329,] 9.005273e-107 1.801055e-106 1.0000000
[330,] 1.244807e-101 2.489615e-101 1.0000000
[331,] 5.029316e-102 1.005863e-101 1.0000000
[332,] 1.781562e-102 3.563124e-102 1.0000000
[333,] 4.885771e-103 9.771542e-103 1.0000000
[334,] 1.676836e-103 3.353671e-103 1.0000000
[335,] 1.979255e-103 3.958509e-103 1.0000000
[336,] 5.201290e-104 1.040258e-103 1.0000000
[337,] 1.376615e-104 2.753231e-104 1.0000000
[338,] 3.533606e-105 7.067212e-105 1.0000000
[339,] 8.784442e-106 1.756888e-105 1.0000000
[340,] 2.107611e-106 4.215223e-106 1.0000000
[341,] 4.853327e-107 9.706654e-107 1.0000000
[342,] 1.048848e-107 2.097696e-107 1.0000000
[343,] 2.461267e-108 4.922534e-108 1.0000000
[344,] 5.471135e-109 1.094227e-108 1.0000000
[345,] 1.187433e-109 2.374867e-109 1.0000000
[346,] 2.647184e-110 5.294369e-110 1.0000000
[347,] 5.744695e-111 1.148939e-110 1.0000000
[348,] 1.430369e-111 2.860738e-111 1.0000000
[349,] 2.944446e-112 5.888891e-112 1.0000000
[350,] 7.082715e-113 1.416543e-112 1.0000000
[351,] 1.906257e-113 3.812513e-113 1.0000000
[352,] 5.196616e-114 1.039323e-113 1.0000000
[353,] 1.312205e-114 2.624410e-114 1.0000000
[354,] 4.503589e-115 9.007179e-115 1.0000000
[355,] 9.226647e-116 1.845329e-115 1.0000000
[356,] 1.197490e-115 2.394979e-115 1.0000000
[357,] 3.102670e-116 6.205341e-116 1.0000000
[358,] 7.052096e-117 1.410419e-116 1.0000000
[359,] 4.198098e-117 8.396196e-117 1.0000000
[360,] 9.636804e-118 1.927361e-117 1.0000000
[361,] 2.259706e-118 4.519412e-118 1.0000000
[362,] 6.009879e-119 1.201976e-118 1.0000000
[363,] 1.233548e-119 2.467096e-119 1.0000000
[364,] 2.426331e-120 4.852662e-120 1.0000000
[365,] 4.946111e-121 9.892222e-121 1.0000000
[366,] 1.466512e-121 2.933024e-121 1.0000000
[367,] 2.952689e-122 5.905378e-122 1.0000000
[368,] 5.814476e-123 1.162895e-122 1.0000000
[369,] 1.176756e-123 2.353512e-123 1.0000000
[370,] 2.697694e-124 5.395388e-124 1.0000000
[371,] 6.891738e-125 1.378348e-124 1.0000000
[372,] 1.573665e-125 3.147330e-125 1.0000000
[373,] 3.131195e-126 6.262391e-126 1.0000000
[374,] 6.141065e-127 1.228213e-126 1.0000000
[375,] 1.620770e-127 3.241540e-127 1.0000000
[376,] 3.276100e-128 6.552199e-128 1.0000000
[377,] 6.364333e-129 1.272867e-128 1.0000000
[378,] 1.301959e-129 2.603918e-129 1.0000000
[379,] 2.708485e-130 5.416971e-130 1.0000000
[380,] 5.699128e-131 1.139826e-130 1.0000000
[381,] 3.511269e-131 7.022539e-131 1.0000000
[382,] 3.531369e-131 7.062737e-131 1.0000000
[383,] 6.651369e-131 1.330274e-130 1.0000000
[384,] 1.726232e-130 3.452464e-130 1.0000000
[385,] 1.448708e-129 2.897416e-129 1.0000000
[386,] 3.602970e-128 7.205940e-128 1.0000000
[387,] 6.304723e-129 1.260945e-128 1.0000000
[388,] 1.484490e-129 2.968980e-129 1.0000000
[389,] 7.418507e-130 1.483701e-129 1.0000000
[390,] 1.493836e-130 2.987671e-130 1.0000000
[391,] 2.908137e-131 5.816275e-131 1.0000000
[392,] 5.379360e-132 1.075872e-131 1.0000000
[393,] 1.031742e-132 2.063485e-132 1.0000000
[394,] 1.944928e-133 3.889856e-133 1.0000000
[395,] 4.019090e-134 8.038180e-134 1.0000000
[396,] 6.615841e-135 1.323168e-134 1.0000000
[397,] 1.092723e-135 2.185447e-135 1.0000000
[398,] 2.088858e-136 4.177717e-136 1.0000000
[399,] 7.568946e-137 1.513789e-136 1.0000000
[400,] 1.191610e-137 2.383221e-137 1.0000000
[401,] 1.970007e-138 3.940014e-138 1.0000000
[402,] 4.146715e-139 8.293429e-139 1.0000000
[403,] 6.545276e-140 1.309055e-139 1.0000000
[404,] 1.022206e-140 2.044412e-140 1.0000000
[405,] 2.904400e-136 5.808800e-136 1.0000000
[406,] 2.620008e-134 5.240016e-134 1.0000000
[407,] 2.169570e-134 4.339141e-134 1.0000000
[408,] 2.169071e-133 4.338142e-133 1.0000000
[409,] 7.508239e-134 1.501648e-133 1.0000000
[410,] 3.247512e-134 6.495023e-134 1.0000000
[411,] 3.589991e-132 7.179981e-132 1.0000000
[412,] 1.222125e-132 2.444251e-132 1.0000000
[413,] 4.620918e-133 9.241837e-133 1.0000000
[414,] 7.126758e-134 1.425352e-133 1.0000000
[415,] 1.542844e-134 3.085688e-134 1.0000000
[416,] 2.304200e-135 4.608401e-135 1.0000000
[417,] 3.435922e-136 6.871845e-136 1.0000000
[418,] 5.094945e-137 1.018989e-136 1.0000000
[419,] 7.205805e-138 1.441161e-137 1.0000000
[420,] 1.682382e-138 3.364765e-138 1.0000000
[421,] 2.168200e-139 4.336399e-139 1.0000000
[422,] 6.613927e-140 1.322785e-139 1.0000000
[423,] 1.137715e-140 2.275429e-140 1.0000000
[424,] 1.638187e-141 3.276373e-141 1.0000000
[425,] 2.243482e-142 4.486964e-142 1.0000000
[426,] 3.010008e-143 6.020016e-143 1.0000000
[427,] 4.835170e-144 9.670341e-144 1.0000000
[428,] 5.599584e-145 1.119917e-144 1.0000000
[429,] 1.253184e-145 2.506367e-145 1.0000000
[430,] 1.379605e-146 2.759210e-146 1.0000000
[431,] 1.496795e-147 2.993590e-147 1.0000000
[432,] 2.313359e-148 4.626718e-148 1.0000000
[433,] 3.700213e-149 7.400426e-149 1.0000000
[434,] 4.850795e-150 9.701589e-150 1.0000000
[435,] 5.018896e-151 1.003779e-150 1.0000000
[436,] 5.552753e-152 1.110551e-151 1.0000000
[437,] 7.718970e-153 1.543794e-152 1.0000000
[438,] 1.194456e-153 2.388912e-153 1.0000000
[439,] 8.916069e-154 1.783214e-153 1.0000000
[440,] 5.896534e-87 1.179307e-86 1.0000000
[441,] 5.867151e-71 1.173430e-70 1.0000000
[442,] 8.311891e-66 1.662378e-65 1.0000000
[443,] 2.281689e-51 4.563378e-51 1.0000000
[444,] 9.220078e-47 1.844016e-46 1.0000000
[445,] 3.328407e-30 6.656813e-30 1.0000000
[446,] 7.430269e-31 1.486054e-30 1.0000000
[447,] 3.310870e-30 6.621741e-30 1.0000000
[448,] 1.615410e-25 3.230820e-25 1.0000000
[449,] 1.931648e-15 3.863296e-15 1.0000000
[450,] 6.503656e-13 1.300731e-12 1.0000000
[451,] 1.678937e-09 3.357873e-09 1.0000000
> postscript(file="/var/wessaorg/rcomp/tmp/1odwu1462484259.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/28dyn1462484259.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3gua71462484259.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4yu8n1462484259.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/545l11462484259.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 = 462
Frequency = 1
1 2 3 4 5
0.865198442 0.575484035 1.706577971 0.663903979 1.175599512
6 7 8 9 10
2.348732313 0.861634159 0.788501358 1.651749637 -0.367271866
11 12 13 14 15
-0.472042462 -0.904600976 -0.686928535 -0.501410436 -0.843853472
16 17 18 19 20
-0.818107519 -0.855263412 -0.968739545 -1.242303828 -0.547499720
21 22 23 24 25
-0.846351146 -0.433908110 -0.645776859 -0.575922766 -0.274801521
26 27 28 29 30
0.169969075 -0.146554792 0.852466711 0.709595295 0.447152258
31 32 33 34 35
0.535398986 0.779337030 0.765945964 2.074709241 0.115399005
36 37 38 39 40
0.234997935 -0.339487068 -0.152589458 0.473703472 1.440400556
41 42 43 44 45
-0.209714922 -0.228107538 1.191491391 -0.534801521 -0.203163726
46 47 48 49 50
0.501120712 0.479798922 2.030430948 1.703047201 0.488507561
51 52 53 54 55
1.466295479 1.490689231 0.060084533 0.197298165 1.399890210
56 57 58 59 60
-0.848046698 -0.081437764 -0.767995143 -0.988888535 -1.222282702
61 62 63 64 65
-0.957396629 -1.650274248 -1.730803182 -0.782422425 -0.840158808
66 67 68 69 70
5.045769646 0.647121848 0.674250431 1.216292416 1.673217354
71 72 73 74 75
-0.654339610 0.374824718 0.030169601 0.260859365 0.478589545
76 77 78 79 80
0.785341248 1.126031012 0.693703453 0.831375895 0.764450957
81 82 83 84 85
0.071436754 -0.330948544 -0.405087113 0.822126518 0.069224672
86 87 88 89 90
0.226322827 -0.029137533 0.275459845 0.641953283 -0.386895023
91 92 93 94 95
-0.803877699 1.277817834 0.410145392 -0.195889255 0.029455628
96 97 98 99 100
0.227128069 0.063363242 0.061667709 0.262241996 -0.532644077
101 102 103 104 105
0.617355923 0.606322827 -0.074251461 0.110862467 -0.011580570
106 107 108 109 110
0.559452508 0.818820482 -0.609225683 -0.237329624 -0.077071341
111 112 113 114 115
-0.461209910 -0.638824612 -0.436211461 0.900312406 0.628501377
116 117 118 119 120
0.772755424 -0.393880763 -4.109082782 -2.315147840 -2.062008874
121 122 123 124 125
-0.773619416 0.288759642 0.507465179 0.927410542 -3.982504278
126 127 128 129 130
-0.492154838 -0.757615198 -0.826007815 -0.541179480 -0.498678705
131 132 133 134 135
-0.763449300 -0.705892337 -0.985433528 -0.150605193 1.028413246
136 137 138 139 140
-1.146588304 -0.218113722 -0.609320035 -0.627648729 -0.820982056
141 142 143 144 145
-0.586992476 -0.736305813 -1.504005563 -0.647788415 -0.691583671
146 147 148 149 150
1.017149195 0.172931761 -0.198590556 -0.400401566 -0.190401566
151 152 153 154 155
0.523387450 0.360889776 2.108042549 0.674134935 1.270859346
156 157 158 159 160
0.849163813 0.831059872 0.650427865 0.718100306 0.729795839
161 162 163 164 165
0.639106075 -0.057448222 -0.103169948 4.737030578 1.089042133
166 167 168 169 170
0.358668391 0.342712608 1.331549111 0.727006351 0.221114510
171 172 173 174 175
0.850291281 -0.890887704 0.578538009 -0.123273002 0.224168484
176 177 178 179 180
-0.041060920 0.044283962 0.113709675 -0.430544372 -7.911881256
181 182 183 184 185
-4.194727393 -3.044851648 -0.597296093 -5.976890982 0.046034132
186 187 188 189 190
-0.220173731 -0.177156408 0.561205798 0.533648835 0.516091871
191 192 193 194 195
0.153132287 -0.042528599 -0.370201041 1.086781617 0.032041451
196 197 198 199 200
0.402041451 1.189944848 -0.082355402 1.168218885 1.289136504
201 202 203 204 205
1.370750071 0.524457159 -0.697876564 -1.150662932 -2.918977229
206 207 208 209 210
-0.316527464 -0.175861926 -0.604254543 -0.608566329 0.005860935
211 212 213 214 215
0.271524922 -0.894026689 -1.060751101 -0.158396232 0.332898230
216 217 218 219 220
0.094277759 0.187237344 -0.262306948 -0.743343127 -0.610526348
221 222 223 224 225
1.259510265 -0.310489735 -0.674974737 0.123992166 -0.193163726
226 227 228 229 230
-0.371841936 0.346951751 -0.370924317 -0.329286523 0.202038369
231 232 233 234 235
-0.758535918 -0.654886569 -0.230088664 -0.222416223 0.340285078
236 237 238 239 240
0.946377464 -0.235375808 0.094566454 -0.204339610 -0.987846153
241 242 243 244 245
-1.005779961 -0.644427759 0.080516052 -0.676697579 -0.439140616
246 247 248 249 250
-0.509772642 -0.250404668 0.567468281 -0.025549043 1.627526019
251 252 253 254 255
0.814450957 0.511433633 0.476836255 1.202439402 0.261433633
256 257 258 259 260
0.545572203 -0.148800404 -0.068481299 0.102296634 -0.108362702
261 262 263 264 265
0.124770100 -0.785172162 -1.119459721 -0.487992042 -0.324023588
266 267 268 269 270
0.402734298 0.693682328 0.730060306 1.093019891 0.525979476
271 272 273 274 275
0.746611502 1.335833588 0.633420962 0.282296615 -0.069453537
276 277 278 279 280
-0.465804188 -0.458131746 -0.201033592 -0.129283422 0.260862467
281 282 283 284 285
0.976322827 -0.121781096 3.639336954 2.730026757 1.110285022
286 287 288 289 290
8.898701865 2.696350079 2.519020970 1.968392064 5.838793135
291 292 293 294 295
1.471467126 1.599598377 0.659880906 -0.508973621 1.700828936
296 297 298 299 300
-0.043883921 -0.150462425 0.403013708 0.850081432 0.554736550
301 302 303 304 305
0.408759642 0.781290846 0.628331262 0.161549111 1.076893993
306 307 308 309 310
0.567583758 0.665198461 1.343244644 0.865687681 0.824049886
311 312 313 314 315
-0.245002046 -0.572674488 -1.285779961 -0.363510141 0.069194262
316 317 318 319 320
-0.461322286 0.143390570 0.236751225 -0.302589439 0.027650782
321 322 323 324 325
0.688656550 1.870467561 1.626098036 1.138656550 1.407277021
326 327 328 329 330
1.201327441 0.854138036 1.004229286 0.181871317 0.158449822
331 332 333 334 335
6.465742318 0.224766960 0.565572203 0.708735415 5.305660352
336 337 338 339 340
0.557355885 1.494766942 0.500692332 -0.127700284 1.860892877
341 342 343 344 345
0.471837805 0.777410542 0.292928641 0.480169601 0.323761211
346 347 348 349 350
0.165405208 -0.344257644 -0.750061336 -0.453938539 -0.097186818
351 352 353 354 355
0.048158064 -0.431267649 0.112323962 -0.432215678 -1.207019785
356 357 358 359 360
0.281351705 -0.926262997 0.072843611 0.424770100 -0.391492402
361 362 363 364 365
1.003648854 -0.107417755 -1.273829245 -3.739044253 0.274800510
366 367 368 369 370
-0.525600560 0.420090755 -0.011951211 -0.304336509 0.063998369
371 372 373 374 375
0.785119615 -0.073674072 -0.142984307 -0.260431996 -0.271927003
376 377 378 379 380
0.556492942 0.312585328 -0.218104437 0.021895563 0.527182707
381 382 383 384 385
0.164165383 -0.008162176 -0.756089780 -0.815804188 -1.762544068
386 387 388 389 390
-4.429099436 -4.913940156 -5.396902842 -5.620950725 -6.386280665
391 392 393 394 395
-6.022559218 -0.035776878 -0.491753786 1.136894012 0.673217335
396 397 398 399 400
-0.082674506 -0.004604096 0.350543343 0.221175369 0.541375895
401 402 403 404 405
-0.327183717 -0.002413122 -0.729338059 0.773047238 -0.174567464
406 407 408 409 410
-0.382586338 0.402816283 -0.387125978 -0.160316518 -10.049718136
411 412 413 414 415
-7.013190096 -1.991177470 -5.492387410 0.189531410 -2.757390972
416 417 418 419 420
-6.398519491 -2.173103413 -1.902771922 -0.411322286 -0.915403117
421 422 423 424 425
-0.124828830 -0.440517044 -0.433303412 -0.235229900 0.745833607
426 427 428 429 430
0.176894012 -1.661578014 0.320570652 0.656979042 0.746805825
431 432 433 434 435
1.356979042 0.379996365 0.921318156 1.589738101 0.715082983
436 437 438 439 440
0.341117630 -0.533221465 -0.570435097 0.528215803 0.023560685
441 442 443 444 445
-0.263364252 -0.475634072 -0.122732226 -0.485807289 -0.708134848
446 447 448 449 450
9.680386807 5.239851784 1.873762631 -9.108920983 -3.586893019
451 452 453 454 455
-14.494274829 -12.782180687 -10.891846677 -1.722907048 0.787026584
456 457 458 459 460
8.646975723 6.299573498 1.029200357 16.526065883 0.902398981
461 462
-2.859102266 10.958188371
> postscript(file="/var/wessaorg/rcomp/tmp/6yert1462484259.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 = 462
Frequency = 1
lag(myerror, k = 1) myerror
0 0.865198442 NA
1 0.575484035 0.865198442
2 1.706577971 0.575484035
3 0.663903979 1.706577971
4 1.175599512 0.663903979
5 2.348732313 1.175599512
6 0.861634159 2.348732313
7 0.788501358 0.861634159
8 1.651749637 0.788501358
9 -0.367271866 1.651749637
10 -0.472042462 -0.367271866
11 -0.904600976 -0.472042462
12 -0.686928535 -0.904600976
13 -0.501410436 -0.686928535
14 -0.843853472 -0.501410436
15 -0.818107519 -0.843853472
16 -0.855263412 -0.818107519
17 -0.968739545 -0.855263412
18 -1.242303828 -0.968739545
19 -0.547499720 -1.242303828
20 -0.846351146 -0.547499720
21 -0.433908110 -0.846351146
22 -0.645776859 -0.433908110
23 -0.575922766 -0.645776859
24 -0.274801521 -0.575922766
25 0.169969075 -0.274801521
26 -0.146554792 0.169969075
27 0.852466711 -0.146554792
28 0.709595295 0.852466711
29 0.447152258 0.709595295
30 0.535398986 0.447152258
31 0.779337030 0.535398986
32 0.765945964 0.779337030
33 2.074709241 0.765945964
34 0.115399005 2.074709241
35 0.234997935 0.115399005
36 -0.339487068 0.234997935
37 -0.152589458 -0.339487068
38 0.473703472 -0.152589458
39 1.440400556 0.473703472
40 -0.209714922 1.440400556
41 -0.228107538 -0.209714922
42 1.191491391 -0.228107538
43 -0.534801521 1.191491391
44 -0.203163726 -0.534801521
45 0.501120712 -0.203163726
46 0.479798922 0.501120712
47 2.030430948 0.479798922
48 1.703047201 2.030430948
49 0.488507561 1.703047201
50 1.466295479 0.488507561
51 1.490689231 1.466295479
52 0.060084533 1.490689231
53 0.197298165 0.060084533
54 1.399890210 0.197298165
55 -0.848046698 1.399890210
56 -0.081437764 -0.848046698
57 -0.767995143 -0.081437764
58 -0.988888535 -0.767995143
59 -1.222282702 -0.988888535
60 -0.957396629 -1.222282702
61 -1.650274248 -0.957396629
62 -1.730803182 -1.650274248
63 -0.782422425 -1.730803182
64 -0.840158808 -0.782422425
65 5.045769646 -0.840158808
66 0.647121848 5.045769646
67 0.674250431 0.647121848
68 1.216292416 0.674250431
69 1.673217354 1.216292416
70 -0.654339610 1.673217354
71 0.374824718 -0.654339610
72 0.030169601 0.374824718
73 0.260859365 0.030169601
74 0.478589545 0.260859365
75 0.785341248 0.478589545
76 1.126031012 0.785341248
77 0.693703453 1.126031012
78 0.831375895 0.693703453
79 0.764450957 0.831375895
80 0.071436754 0.764450957
81 -0.330948544 0.071436754
82 -0.405087113 -0.330948544
83 0.822126518 -0.405087113
84 0.069224672 0.822126518
85 0.226322827 0.069224672
86 -0.029137533 0.226322827
87 0.275459845 -0.029137533
88 0.641953283 0.275459845
89 -0.386895023 0.641953283
90 -0.803877699 -0.386895023
91 1.277817834 -0.803877699
92 0.410145392 1.277817834
93 -0.195889255 0.410145392
94 0.029455628 -0.195889255
95 0.227128069 0.029455628
96 0.063363242 0.227128069
97 0.061667709 0.063363242
98 0.262241996 0.061667709
99 -0.532644077 0.262241996
100 0.617355923 -0.532644077
101 0.606322827 0.617355923
102 -0.074251461 0.606322827
103 0.110862467 -0.074251461
104 -0.011580570 0.110862467
105 0.559452508 -0.011580570
106 0.818820482 0.559452508
107 -0.609225683 0.818820482
108 -0.237329624 -0.609225683
109 -0.077071341 -0.237329624
110 -0.461209910 -0.077071341
111 -0.638824612 -0.461209910
112 -0.436211461 -0.638824612
113 0.900312406 -0.436211461
114 0.628501377 0.900312406
115 0.772755424 0.628501377
116 -0.393880763 0.772755424
117 -4.109082782 -0.393880763
118 -2.315147840 -4.109082782
119 -2.062008874 -2.315147840
120 -0.773619416 -2.062008874
121 0.288759642 -0.773619416
122 0.507465179 0.288759642
123 0.927410542 0.507465179
124 -3.982504278 0.927410542
125 -0.492154838 -3.982504278
126 -0.757615198 -0.492154838
127 -0.826007815 -0.757615198
128 -0.541179480 -0.826007815
129 -0.498678705 -0.541179480
130 -0.763449300 -0.498678705
131 -0.705892337 -0.763449300
132 -0.985433528 -0.705892337
133 -0.150605193 -0.985433528
134 1.028413246 -0.150605193
135 -1.146588304 1.028413246
136 -0.218113722 -1.146588304
137 -0.609320035 -0.218113722
138 -0.627648729 -0.609320035
139 -0.820982056 -0.627648729
140 -0.586992476 -0.820982056
141 -0.736305813 -0.586992476
142 -1.504005563 -0.736305813
143 -0.647788415 -1.504005563
144 -0.691583671 -0.647788415
145 1.017149195 -0.691583671
146 0.172931761 1.017149195
147 -0.198590556 0.172931761
148 -0.400401566 -0.198590556
149 -0.190401566 -0.400401566
150 0.523387450 -0.190401566
151 0.360889776 0.523387450
152 2.108042549 0.360889776
153 0.674134935 2.108042549
154 1.270859346 0.674134935
155 0.849163813 1.270859346
156 0.831059872 0.849163813
157 0.650427865 0.831059872
158 0.718100306 0.650427865
159 0.729795839 0.718100306
160 0.639106075 0.729795839
161 -0.057448222 0.639106075
162 -0.103169948 -0.057448222
163 4.737030578 -0.103169948
164 1.089042133 4.737030578
165 0.358668391 1.089042133
166 0.342712608 0.358668391
167 1.331549111 0.342712608
168 0.727006351 1.331549111
169 0.221114510 0.727006351
170 0.850291281 0.221114510
171 -0.890887704 0.850291281
172 0.578538009 -0.890887704
173 -0.123273002 0.578538009
174 0.224168484 -0.123273002
175 -0.041060920 0.224168484
176 0.044283962 -0.041060920
177 0.113709675 0.044283962
178 -0.430544372 0.113709675
179 -7.911881256 -0.430544372
180 -4.194727393 -7.911881256
181 -3.044851648 -4.194727393
182 -0.597296093 -3.044851648
183 -5.976890982 -0.597296093
184 0.046034132 -5.976890982
185 -0.220173731 0.046034132
186 -0.177156408 -0.220173731
187 0.561205798 -0.177156408
188 0.533648835 0.561205798
189 0.516091871 0.533648835
190 0.153132287 0.516091871
191 -0.042528599 0.153132287
192 -0.370201041 -0.042528599
193 1.086781617 -0.370201041
194 0.032041451 1.086781617
195 0.402041451 0.032041451
196 1.189944848 0.402041451
197 -0.082355402 1.189944848
198 1.168218885 -0.082355402
199 1.289136504 1.168218885
200 1.370750071 1.289136504
201 0.524457159 1.370750071
202 -0.697876564 0.524457159
203 -1.150662932 -0.697876564
204 -2.918977229 -1.150662932
205 -0.316527464 -2.918977229
206 -0.175861926 -0.316527464
207 -0.604254543 -0.175861926
208 -0.608566329 -0.604254543
209 0.005860935 -0.608566329
210 0.271524922 0.005860935
211 -0.894026689 0.271524922
212 -1.060751101 -0.894026689
213 -0.158396232 -1.060751101
214 0.332898230 -0.158396232
215 0.094277759 0.332898230
216 0.187237344 0.094277759
217 -0.262306948 0.187237344
218 -0.743343127 -0.262306948
219 -0.610526348 -0.743343127
220 1.259510265 -0.610526348
221 -0.310489735 1.259510265
222 -0.674974737 -0.310489735
223 0.123992166 -0.674974737
224 -0.193163726 0.123992166
225 -0.371841936 -0.193163726
226 0.346951751 -0.371841936
227 -0.370924317 0.346951751
228 -0.329286523 -0.370924317
229 0.202038369 -0.329286523
230 -0.758535918 0.202038369
231 -0.654886569 -0.758535918
232 -0.230088664 -0.654886569
233 -0.222416223 -0.230088664
234 0.340285078 -0.222416223
235 0.946377464 0.340285078
236 -0.235375808 0.946377464
237 0.094566454 -0.235375808
238 -0.204339610 0.094566454
239 -0.987846153 -0.204339610
240 -1.005779961 -0.987846153
241 -0.644427759 -1.005779961
242 0.080516052 -0.644427759
243 -0.676697579 0.080516052
244 -0.439140616 -0.676697579
245 -0.509772642 -0.439140616
246 -0.250404668 -0.509772642
247 0.567468281 -0.250404668
248 -0.025549043 0.567468281
249 1.627526019 -0.025549043
250 0.814450957 1.627526019
251 0.511433633 0.814450957
252 0.476836255 0.511433633
253 1.202439402 0.476836255
254 0.261433633 1.202439402
255 0.545572203 0.261433633
256 -0.148800404 0.545572203
257 -0.068481299 -0.148800404
258 0.102296634 -0.068481299
259 -0.108362702 0.102296634
260 0.124770100 -0.108362702
261 -0.785172162 0.124770100
262 -1.119459721 -0.785172162
263 -0.487992042 -1.119459721
264 -0.324023588 -0.487992042
265 0.402734298 -0.324023588
266 0.693682328 0.402734298
267 0.730060306 0.693682328
268 1.093019891 0.730060306
269 0.525979476 1.093019891
270 0.746611502 0.525979476
271 1.335833588 0.746611502
272 0.633420962 1.335833588
273 0.282296615 0.633420962
274 -0.069453537 0.282296615
275 -0.465804188 -0.069453537
276 -0.458131746 -0.465804188
277 -0.201033592 -0.458131746
278 -0.129283422 -0.201033592
279 0.260862467 -0.129283422
280 0.976322827 0.260862467
281 -0.121781096 0.976322827
282 3.639336954 -0.121781096
283 2.730026757 3.639336954
284 1.110285022 2.730026757
285 8.898701865 1.110285022
286 2.696350079 8.898701865
287 2.519020970 2.696350079
288 1.968392064 2.519020970
289 5.838793135 1.968392064
290 1.471467126 5.838793135
291 1.599598377 1.471467126
292 0.659880906 1.599598377
293 -0.508973621 0.659880906
294 1.700828936 -0.508973621
295 -0.043883921 1.700828936
296 -0.150462425 -0.043883921
297 0.403013708 -0.150462425
298 0.850081432 0.403013708
299 0.554736550 0.850081432
300 0.408759642 0.554736550
301 0.781290846 0.408759642
302 0.628331262 0.781290846
303 0.161549111 0.628331262
304 1.076893993 0.161549111
305 0.567583758 1.076893993
306 0.665198461 0.567583758
307 1.343244644 0.665198461
308 0.865687681 1.343244644
309 0.824049886 0.865687681
310 -0.245002046 0.824049886
311 -0.572674488 -0.245002046
312 -1.285779961 -0.572674488
313 -0.363510141 -1.285779961
314 0.069194262 -0.363510141
315 -0.461322286 0.069194262
316 0.143390570 -0.461322286
317 0.236751225 0.143390570
318 -0.302589439 0.236751225
319 0.027650782 -0.302589439
320 0.688656550 0.027650782
321 1.870467561 0.688656550
322 1.626098036 1.870467561
323 1.138656550 1.626098036
324 1.407277021 1.138656550
325 1.201327441 1.407277021
326 0.854138036 1.201327441
327 1.004229286 0.854138036
328 0.181871317 1.004229286
329 0.158449822 0.181871317
330 6.465742318 0.158449822
331 0.224766960 6.465742318
332 0.565572203 0.224766960
333 0.708735415 0.565572203
334 5.305660352 0.708735415
335 0.557355885 5.305660352
336 1.494766942 0.557355885
337 0.500692332 1.494766942
338 -0.127700284 0.500692332
339 1.860892877 -0.127700284
340 0.471837805 1.860892877
341 0.777410542 0.471837805
342 0.292928641 0.777410542
343 0.480169601 0.292928641
344 0.323761211 0.480169601
345 0.165405208 0.323761211
346 -0.344257644 0.165405208
347 -0.750061336 -0.344257644
348 -0.453938539 -0.750061336
349 -0.097186818 -0.453938539
350 0.048158064 -0.097186818
351 -0.431267649 0.048158064
352 0.112323962 -0.431267649
353 -0.432215678 0.112323962
354 -1.207019785 -0.432215678
355 0.281351705 -1.207019785
356 -0.926262997 0.281351705
357 0.072843611 -0.926262997
358 0.424770100 0.072843611
359 -0.391492402 0.424770100
360 1.003648854 -0.391492402
361 -0.107417755 1.003648854
362 -1.273829245 -0.107417755
363 -3.739044253 -1.273829245
364 0.274800510 -3.739044253
365 -0.525600560 0.274800510
366 0.420090755 -0.525600560
367 -0.011951211 0.420090755
368 -0.304336509 -0.011951211
369 0.063998369 -0.304336509
370 0.785119615 0.063998369
371 -0.073674072 0.785119615
372 -0.142984307 -0.073674072
373 -0.260431996 -0.142984307
374 -0.271927003 -0.260431996
375 0.556492942 -0.271927003
376 0.312585328 0.556492942
377 -0.218104437 0.312585328
378 0.021895563 -0.218104437
379 0.527182707 0.021895563
380 0.164165383 0.527182707
381 -0.008162176 0.164165383
382 -0.756089780 -0.008162176
383 -0.815804188 -0.756089780
384 -1.762544068 -0.815804188
385 -4.429099436 -1.762544068
386 -4.913940156 -4.429099436
387 -5.396902842 -4.913940156
388 -5.620950725 -5.396902842
389 -6.386280665 -5.620950725
390 -6.022559218 -6.386280665
391 -0.035776878 -6.022559218
392 -0.491753786 -0.035776878
393 1.136894012 -0.491753786
394 0.673217335 1.136894012
395 -0.082674506 0.673217335
396 -0.004604096 -0.082674506
397 0.350543343 -0.004604096
398 0.221175369 0.350543343
399 0.541375895 0.221175369
400 -0.327183717 0.541375895
401 -0.002413122 -0.327183717
402 -0.729338059 -0.002413122
403 0.773047238 -0.729338059
404 -0.174567464 0.773047238
405 -0.382586338 -0.174567464
406 0.402816283 -0.382586338
407 -0.387125978 0.402816283
408 -0.160316518 -0.387125978
409 -10.049718136 -0.160316518
410 -7.013190096 -10.049718136
411 -1.991177470 -7.013190096
412 -5.492387410 -1.991177470
413 0.189531410 -5.492387410
414 -2.757390972 0.189531410
415 -6.398519491 -2.757390972
416 -2.173103413 -6.398519491
417 -1.902771922 -2.173103413
418 -0.411322286 -1.902771922
419 -0.915403117 -0.411322286
420 -0.124828830 -0.915403117
421 -0.440517044 -0.124828830
422 -0.433303412 -0.440517044
423 -0.235229900 -0.433303412
424 0.745833607 -0.235229900
425 0.176894012 0.745833607
426 -1.661578014 0.176894012
427 0.320570652 -1.661578014
428 0.656979042 0.320570652
429 0.746805825 0.656979042
430 1.356979042 0.746805825
431 0.379996365 1.356979042
432 0.921318156 0.379996365
433 1.589738101 0.921318156
434 0.715082983 1.589738101
435 0.341117630 0.715082983
436 -0.533221465 0.341117630
437 -0.570435097 -0.533221465
438 0.528215803 -0.570435097
439 0.023560685 0.528215803
440 -0.263364252 0.023560685
441 -0.475634072 -0.263364252
442 -0.122732226 -0.475634072
443 -0.485807289 -0.122732226
444 -0.708134848 -0.485807289
445 9.680386807 -0.708134848
446 5.239851784 9.680386807
447 1.873762631 5.239851784
448 -9.108920983 1.873762631
449 -3.586893019 -9.108920983
450 -14.494274829 -3.586893019
451 -12.782180687 -14.494274829
452 -10.891846677 -12.782180687
453 -1.722907048 -10.891846677
454 0.787026584 -1.722907048
455 8.646975723 0.787026584
456 6.299573498 8.646975723
457 1.029200357 6.299573498
458 16.526065883 1.029200357
459 0.902398981 16.526065883
460 -2.859102266 0.902398981
461 10.958188371 -2.859102266
462 NA 10.958188371
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.575484035 0.865198442
[2,] 1.706577971 0.575484035
[3,] 0.663903979 1.706577971
[4,] 1.175599512 0.663903979
[5,] 2.348732313 1.175599512
[6,] 0.861634159 2.348732313
[7,] 0.788501358 0.861634159
[8,] 1.651749637 0.788501358
[9,] -0.367271866 1.651749637
[10,] -0.472042462 -0.367271866
[11,] -0.904600976 -0.472042462
[12,] -0.686928535 -0.904600976
[13,] -0.501410436 -0.686928535
[14,] -0.843853472 -0.501410436
[15,] -0.818107519 -0.843853472
[16,] -0.855263412 -0.818107519
[17,] -0.968739545 -0.855263412
[18,] -1.242303828 -0.968739545
[19,] -0.547499720 -1.242303828
[20,] -0.846351146 -0.547499720
[21,] -0.433908110 -0.846351146
[22,] -0.645776859 -0.433908110
[23,] -0.575922766 -0.645776859
[24,] -0.274801521 -0.575922766
[25,] 0.169969075 -0.274801521
[26,] -0.146554792 0.169969075
[27,] 0.852466711 -0.146554792
[28,] 0.709595295 0.852466711
[29,] 0.447152258 0.709595295
[30,] 0.535398986 0.447152258
[31,] 0.779337030 0.535398986
[32,] 0.765945964 0.779337030
[33,] 2.074709241 0.765945964
[34,] 0.115399005 2.074709241
[35,] 0.234997935 0.115399005
[36,] -0.339487068 0.234997935
[37,] -0.152589458 -0.339487068
[38,] 0.473703472 -0.152589458
[39,] 1.440400556 0.473703472
[40,] -0.209714922 1.440400556
[41,] -0.228107538 -0.209714922
[42,] 1.191491391 -0.228107538
[43,] -0.534801521 1.191491391
[44,] -0.203163726 -0.534801521
[45,] 0.501120712 -0.203163726
[46,] 0.479798922 0.501120712
[47,] 2.030430948 0.479798922
[48,] 1.703047201 2.030430948
[49,] 0.488507561 1.703047201
[50,] 1.466295479 0.488507561
[51,] 1.490689231 1.466295479
[52,] 0.060084533 1.490689231
[53,] 0.197298165 0.060084533
[54,] 1.399890210 0.197298165
[55,] -0.848046698 1.399890210
[56,] -0.081437764 -0.848046698
[57,] -0.767995143 -0.081437764
[58,] -0.988888535 -0.767995143
[59,] -1.222282702 -0.988888535
[60,] -0.957396629 -1.222282702
[61,] -1.650274248 -0.957396629
[62,] -1.730803182 -1.650274248
[63,] -0.782422425 -1.730803182
[64,] -0.840158808 -0.782422425
[65,] 5.045769646 -0.840158808
[66,] 0.647121848 5.045769646
[67,] 0.674250431 0.647121848
[68,] 1.216292416 0.674250431
[69,] 1.673217354 1.216292416
[70,] -0.654339610 1.673217354
[71,] 0.374824718 -0.654339610
[72,] 0.030169601 0.374824718
[73,] 0.260859365 0.030169601
[74,] 0.478589545 0.260859365
[75,] 0.785341248 0.478589545
[76,] 1.126031012 0.785341248
[77,] 0.693703453 1.126031012
[78,] 0.831375895 0.693703453
[79,] 0.764450957 0.831375895
[80,] 0.071436754 0.764450957
[81,] -0.330948544 0.071436754
[82,] -0.405087113 -0.330948544
[83,] 0.822126518 -0.405087113
[84,] 0.069224672 0.822126518
[85,] 0.226322827 0.069224672
[86,] -0.029137533 0.226322827
[87,] 0.275459845 -0.029137533
[88,] 0.641953283 0.275459845
[89,] -0.386895023 0.641953283
[90,] -0.803877699 -0.386895023
[91,] 1.277817834 -0.803877699
[92,] 0.410145392 1.277817834
[93,] -0.195889255 0.410145392
[94,] 0.029455628 -0.195889255
[95,] 0.227128069 0.029455628
[96,] 0.063363242 0.227128069
[97,] 0.061667709 0.063363242
[98,] 0.262241996 0.061667709
[99,] -0.532644077 0.262241996
[100,] 0.617355923 -0.532644077
[101,] 0.606322827 0.617355923
[102,] -0.074251461 0.606322827
[103,] 0.110862467 -0.074251461
[104,] -0.011580570 0.110862467
[105,] 0.559452508 -0.011580570
[106,] 0.818820482 0.559452508
[107,] -0.609225683 0.818820482
[108,] -0.237329624 -0.609225683
[109,] -0.077071341 -0.237329624
[110,] -0.461209910 -0.077071341
[111,] -0.638824612 -0.461209910
[112,] -0.436211461 -0.638824612
[113,] 0.900312406 -0.436211461
[114,] 0.628501377 0.900312406
[115,] 0.772755424 0.628501377
[116,] -0.393880763 0.772755424
[117,] -4.109082782 -0.393880763
[118,] -2.315147840 -4.109082782
[119,] -2.062008874 -2.315147840
[120,] -0.773619416 -2.062008874
[121,] 0.288759642 -0.773619416
[122,] 0.507465179 0.288759642
[123,] 0.927410542 0.507465179
[124,] -3.982504278 0.927410542
[125,] -0.492154838 -3.982504278
[126,] -0.757615198 -0.492154838
[127,] -0.826007815 -0.757615198
[128,] -0.541179480 -0.826007815
[129,] -0.498678705 -0.541179480
[130,] -0.763449300 -0.498678705
[131,] -0.705892337 -0.763449300
[132,] -0.985433528 -0.705892337
[133,] -0.150605193 -0.985433528
[134,] 1.028413246 -0.150605193
[135,] -1.146588304 1.028413246
[136,] -0.218113722 -1.146588304
[137,] -0.609320035 -0.218113722
[138,] -0.627648729 -0.609320035
[139,] -0.820982056 -0.627648729
[140,] -0.586992476 -0.820982056
[141,] -0.736305813 -0.586992476
[142,] -1.504005563 -0.736305813
[143,] -0.647788415 -1.504005563
[144,] -0.691583671 -0.647788415
[145,] 1.017149195 -0.691583671
[146,] 0.172931761 1.017149195
[147,] -0.198590556 0.172931761
[148,] -0.400401566 -0.198590556
[149,] -0.190401566 -0.400401566
[150,] 0.523387450 -0.190401566
[151,] 0.360889776 0.523387450
[152,] 2.108042549 0.360889776
[153,] 0.674134935 2.108042549
[154,] 1.270859346 0.674134935
[155,] 0.849163813 1.270859346
[156,] 0.831059872 0.849163813
[157,] 0.650427865 0.831059872
[158,] 0.718100306 0.650427865
[159,] 0.729795839 0.718100306
[160,] 0.639106075 0.729795839
[161,] -0.057448222 0.639106075
[162,] -0.103169948 -0.057448222
[163,] 4.737030578 -0.103169948
[164,] 1.089042133 4.737030578
[165,] 0.358668391 1.089042133
[166,] 0.342712608 0.358668391
[167,] 1.331549111 0.342712608
[168,] 0.727006351 1.331549111
[169,] 0.221114510 0.727006351
[170,] 0.850291281 0.221114510
[171,] -0.890887704 0.850291281
[172,] 0.578538009 -0.890887704
[173,] -0.123273002 0.578538009
[174,] 0.224168484 -0.123273002
[175,] -0.041060920 0.224168484
[176,] 0.044283962 -0.041060920
[177,] 0.113709675 0.044283962
[178,] -0.430544372 0.113709675
[179,] -7.911881256 -0.430544372
[180,] -4.194727393 -7.911881256
[181,] -3.044851648 -4.194727393
[182,] -0.597296093 -3.044851648
[183,] -5.976890982 -0.597296093
[184,] 0.046034132 -5.976890982
[185,] -0.220173731 0.046034132
[186,] -0.177156408 -0.220173731
[187,] 0.561205798 -0.177156408
[188,] 0.533648835 0.561205798
[189,] 0.516091871 0.533648835
[190,] 0.153132287 0.516091871
[191,] -0.042528599 0.153132287
[192,] -0.370201041 -0.042528599
[193,] 1.086781617 -0.370201041
[194,] 0.032041451 1.086781617
[195,] 0.402041451 0.032041451
[196,] 1.189944848 0.402041451
[197,] -0.082355402 1.189944848
[198,] 1.168218885 -0.082355402
[199,] 1.289136504 1.168218885
[200,] 1.370750071 1.289136504
[201,] 0.524457159 1.370750071
[202,] -0.697876564 0.524457159
[203,] -1.150662932 -0.697876564
[204,] -2.918977229 -1.150662932
[205,] -0.316527464 -2.918977229
[206,] -0.175861926 -0.316527464
[207,] -0.604254543 -0.175861926
[208,] -0.608566329 -0.604254543
[209,] 0.005860935 -0.608566329
[210,] 0.271524922 0.005860935
[211,] -0.894026689 0.271524922
[212,] -1.060751101 -0.894026689
[213,] -0.158396232 -1.060751101
[214,] 0.332898230 -0.158396232
[215,] 0.094277759 0.332898230
[216,] 0.187237344 0.094277759
[217,] -0.262306948 0.187237344
[218,] -0.743343127 -0.262306948
[219,] -0.610526348 -0.743343127
[220,] 1.259510265 -0.610526348
[221,] -0.310489735 1.259510265
[222,] -0.674974737 -0.310489735
[223,] 0.123992166 -0.674974737
[224,] -0.193163726 0.123992166
[225,] -0.371841936 -0.193163726
[226,] 0.346951751 -0.371841936
[227,] -0.370924317 0.346951751
[228,] -0.329286523 -0.370924317
[229,] 0.202038369 -0.329286523
[230,] -0.758535918 0.202038369
[231,] -0.654886569 -0.758535918
[232,] -0.230088664 -0.654886569
[233,] -0.222416223 -0.230088664
[234,] 0.340285078 -0.222416223
[235,] 0.946377464 0.340285078
[236,] -0.235375808 0.946377464
[237,] 0.094566454 -0.235375808
[238,] -0.204339610 0.094566454
[239,] -0.987846153 -0.204339610
[240,] -1.005779961 -0.987846153
[241,] -0.644427759 -1.005779961
[242,] 0.080516052 -0.644427759
[243,] -0.676697579 0.080516052
[244,] -0.439140616 -0.676697579
[245,] -0.509772642 -0.439140616
[246,] -0.250404668 -0.509772642
[247,] 0.567468281 -0.250404668
[248,] -0.025549043 0.567468281
[249,] 1.627526019 -0.025549043
[250,] 0.814450957 1.627526019
[251,] 0.511433633 0.814450957
[252,] 0.476836255 0.511433633
[253,] 1.202439402 0.476836255
[254,] 0.261433633 1.202439402
[255,] 0.545572203 0.261433633
[256,] -0.148800404 0.545572203
[257,] -0.068481299 -0.148800404
[258,] 0.102296634 -0.068481299
[259,] -0.108362702 0.102296634
[260,] 0.124770100 -0.108362702
[261,] -0.785172162 0.124770100
[262,] -1.119459721 -0.785172162
[263,] -0.487992042 -1.119459721
[264,] -0.324023588 -0.487992042
[265,] 0.402734298 -0.324023588
[266,] 0.693682328 0.402734298
[267,] 0.730060306 0.693682328
[268,] 1.093019891 0.730060306
[269,] 0.525979476 1.093019891
[270,] 0.746611502 0.525979476
[271,] 1.335833588 0.746611502
[272,] 0.633420962 1.335833588
[273,] 0.282296615 0.633420962
[274,] -0.069453537 0.282296615
[275,] -0.465804188 -0.069453537
[276,] -0.458131746 -0.465804188
[277,] -0.201033592 -0.458131746
[278,] -0.129283422 -0.201033592
[279,] 0.260862467 -0.129283422
[280,] 0.976322827 0.260862467
[281,] -0.121781096 0.976322827
[282,] 3.639336954 -0.121781096
[283,] 2.730026757 3.639336954
[284,] 1.110285022 2.730026757
[285,] 8.898701865 1.110285022
[286,] 2.696350079 8.898701865
[287,] 2.519020970 2.696350079
[288,] 1.968392064 2.519020970
[289,] 5.838793135 1.968392064
[290,] 1.471467126 5.838793135
[291,] 1.599598377 1.471467126
[292,] 0.659880906 1.599598377
[293,] -0.508973621 0.659880906
[294,] 1.700828936 -0.508973621
[295,] -0.043883921 1.700828936
[296,] -0.150462425 -0.043883921
[297,] 0.403013708 -0.150462425
[298,] 0.850081432 0.403013708
[299,] 0.554736550 0.850081432
[300,] 0.408759642 0.554736550
[301,] 0.781290846 0.408759642
[302,] 0.628331262 0.781290846
[303,] 0.161549111 0.628331262
[304,] 1.076893993 0.161549111
[305,] 0.567583758 1.076893993
[306,] 0.665198461 0.567583758
[307,] 1.343244644 0.665198461
[308,] 0.865687681 1.343244644
[309,] 0.824049886 0.865687681
[310,] -0.245002046 0.824049886
[311,] -0.572674488 -0.245002046
[312,] -1.285779961 -0.572674488
[313,] -0.363510141 -1.285779961
[314,] 0.069194262 -0.363510141
[315,] -0.461322286 0.069194262
[316,] 0.143390570 -0.461322286
[317,] 0.236751225 0.143390570
[318,] -0.302589439 0.236751225
[319,] 0.027650782 -0.302589439
[320,] 0.688656550 0.027650782
[321,] 1.870467561 0.688656550
[322,] 1.626098036 1.870467561
[323,] 1.138656550 1.626098036
[324,] 1.407277021 1.138656550
[325,] 1.201327441 1.407277021
[326,] 0.854138036 1.201327441
[327,] 1.004229286 0.854138036
[328,] 0.181871317 1.004229286
[329,] 0.158449822 0.181871317
[330,] 6.465742318 0.158449822
[331,] 0.224766960 6.465742318
[332,] 0.565572203 0.224766960
[333,] 0.708735415 0.565572203
[334,] 5.305660352 0.708735415
[335,] 0.557355885 5.305660352
[336,] 1.494766942 0.557355885
[337,] 0.500692332 1.494766942
[338,] -0.127700284 0.500692332
[339,] 1.860892877 -0.127700284
[340,] 0.471837805 1.860892877
[341,] 0.777410542 0.471837805
[342,] 0.292928641 0.777410542
[343,] 0.480169601 0.292928641
[344,] 0.323761211 0.480169601
[345,] 0.165405208 0.323761211
[346,] -0.344257644 0.165405208
[347,] -0.750061336 -0.344257644
[348,] -0.453938539 -0.750061336
[349,] -0.097186818 -0.453938539
[350,] 0.048158064 -0.097186818
[351,] -0.431267649 0.048158064
[352,] 0.112323962 -0.431267649
[353,] -0.432215678 0.112323962
[354,] -1.207019785 -0.432215678
[355,] 0.281351705 -1.207019785
[356,] -0.926262997 0.281351705
[357,] 0.072843611 -0.926262997
[358,] 0.424770100 0.072843611
[359,] -0.391492402 0.424770100
[360,] 1.003648854 -0.391492402
[361,] -0.107417755 1.003648854
[362,] -1.273829245 -0.107417755
[363,] -3.739044253 -1.273829245
[364,] 0.274800510 -3.739044253
[365,] -0.525600560 0.274800510
[366,] 0.420090755 -0.525600560
[367,] -0.011951211 0.420090755
[368,] -0.304336509 -0.011951211
[369,] 0.063998369 -0.304336509
[370,] 0.785119615 0.063998369
[371,] -0.073674072 0.785119615
[372,] -0.142984307 -0.073674072
[373,] -0.260431996 -0.142984307
[374,] -0.271927003 -0.260431996
[375,] 0.556492942 -0.271927003
[376,] 0.312585328 0.556492942
[377,] -0.218104437 0.312585328
[378,] 0.021895563 -0.218104437
[379,] 0.527182707 0.021895563
[380,] 0.164165383 0.527182707
[381,] -0.008162176 0.164165383
[382,] -0.756089780 -0.008162176
[383,] -0.815804188 -0.756089780
[384,] -1.762544068 -0.815804188
[385,] -4.429099436 -1.762544068
[386,] -4.913940156 -4.429099436
[387,] -5.396902842 -4.913940156
[388,] -5.620950725 -5.396902842
[389,] -6.386280665 -5.620950725
[390,] -6.022559218 -6.386280665
[391,] -0.035776878 -6.022559218
[392,] -0.491753786 -0.035776878
[393,] 1.136894012 -0.491753786
[394,] 0.673217335 1.136894012
[395,] -0.082674506 0.673217335
[396,] -0.004604096 -0.082674506
[397,] 0.350543343 -0.004604096
[398,] 0.221175369 0.350543343
[399,] 0.541375895 0.221175369
[400,] -0.327183717 0.541375895
[401,] -0.002413122 -0.327183717
[402,] -0.729338059 -0.002413122
[403,] 0.773047238 -0.729338059
[404,] -0.174567464 0.773047238
[405,] -0.382586338 -0.174567464
[406,] 0.402816283 -0.382586338
[407,] -0.387125978 0.402816283
[408,] -0.160316518 -0.387125978
[409,] -10.049718136 -0.160316518
[410,] -7.013190096 -10.049718136
[411,] -1.991177470 -7.013190096
[412,] -5.492387410 -1.991177470
[413,] 0.189531410 -5.492387410
[414,] -2.757390972 0.189531410
[415,] -6.398519491 -2.757390972
[416,] -2.173103413 -6.398519491
[417,] -1.902771922 -2.173103413
[418,] -0.411322286 -1.902771922
[419,] -0.915403117 -0.411322286
[420,] -0.124828830 -0.915403117
[421,] -0.440517044 -0.124828830
[422,] -0.433303412 -0.440517044
[423,] -0.235229900 -0.433303412
[424,] 0.745833607 -0.235229900
[425,] 0.176894012 0.745833607
[426,] -1.661578014 0.176894012
[427,] 0.320570652 -1.661578014
[428,] 0.656979042 0.320570652
[429,] 0.746805825 0.656979042
[430,] 1.356979042 0.746805825
[431,] 0.379996365 1.356979042
[432,] 0.921318156 0.379996365
[433,] 1.589738101 0.921318156
[434,] 0.715082983 1.589738101
[435,] 0.341117630 0.715082983
[436,] -0.533221465 0.341117630
[437,] -0.570435097 -0.533221465
[438,] 0.528215803 -0.570435097
[439,] 0.023560685 0.528215803
[440,] -0.263364252 0.023560685
[441,] -0.475634072 -0.263364252
[442,] -0.122732226 -0.475634072
[443,] -0.485807289 -0.122732226
[444,] -0.708134848 -0.485807289
[445,] 9.680386807 -0.708134848
[446,] 5.239851784 9.680386807
[447,] 1.873762631 5.239851784
[448,] -9.108920983 1.873762631
[449,] -3.586893019 -9.108920983
[450,] -14.494274829 -3.586893019
[451,] -12.782180687 -14.494274829
[452,] -10.891846677 -12.782180687
[453,] -1.722907048 -10.891846677
[454,] 0.787026584 -1.722907048
[455,] 8.646975723 0.787026584
[456,] 6.299573498 8.646975723
[457,] 1.029200357 6.299573498
[458,] 16.526065883 1.029200357
[459,] 0.902398981 16.526065883
[460,] -2.859102266 0.902398981
[461,] 10.958188371 -2.859102266
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.575484035 0.865198442
2 1.706577971 0.575484035
3 0.663903979 1.706577971
4 1.175599512 0.663903979
5 2.348732313 1.175599512
6 0.861634159 2.348732313
7 0.788501358 0.861634159
8 1.651749637 0.788501358
9 -0.367271866 1.651749637
10 -0.472042462 -0.367271866
11 -0.904600976 -0.472042462
12 -0.686928535 -0.904600976
13 -0.501410436 -0.686928535
14 -0.843853472 -0.501410436
15 -0.818107519 -0.843853472
16 -0.855263412 -0.818107519
17 -0.968739545 -0.855263412
18 -1.242303828 -0.968739545
19 -0.547499720 -1.242303828
20 -0.846351146 -0.547499720
21 -0.433908110 -0.846351146
22 -0.645776859 -0.433908110
23 -0.575922766 -0.645776859
24 -0.274801521 -0.575922766
25 0.169969075 -0.274801521
26 -0.146554792 0.169969075
27 0.852466711 -0.146554792
28 0.709595295 0.852466711
29 0.447152258 0.709595295
30 0.535398986 0.447152258
31 0.779337030 0.535398986
32 0.765945964 0.779337030
33 2.074709241 0.765945964
34 0.115399005 2.074709241
35 0.234997935 0.115399005
36 -0.339487068 0.234997935
37 -0.152589458 -0.339487068
38 0.473703472 -0.152589458
39 1.440400556 0.473703472
40 -0.209714922 1.440400556
41 -0.228107538 -0.209714922
42 1.191491391 -0.228107538
43 -0.534801521 1.191491391
44 -0.203163726 -0.534801521
45 0.501120712 -0.203163726
46 0.479798922 0.501120712
47 2.030430948 0.479798922
48 1.703047201 2.030430948
49 0.488507561 1.703047201
50 1.466295479 0.488507561
51 1.490689231 1.466295479
52 0.060084533 1.490689231
53 0.197298165 0.060084533
54 1.399890210 0.197298165
55 -0.848046698 1.399890210
56 -0.081437764 -0.848046698
57 -0.767995143 -0.081437764
58 -0.988888535 -0.767995143
59 -1.222282702 -0.988888535
60 -0.957396629 -1.222282702
61 -1.650274248 -0.957396629
62 -1.730803182 -1.650274248
63 -0.782422425 -1.730803182
64 -0.840158808 -0.782422425
65 5.045769646 -0.840158808
66 0.647121848 5.045769646
67 0.674250431 0.647121848
68 1.216292416 0.674250431
69 1.673217354 1.216292416
70 -0.654339610 1.673217354
71 0.374824718 -0.654339610
72 0.030169601 0.374824718
73 0.260859365 0.030169601
74 0.478589545 0.260859365
75 0.785341248 0.478589545
76 1.126031012 0.785341248
77 0.693703453 1.126031012
78 0.831375895 0.693703453
79 0.764450957 0.831375895
80 0.071436754 0.764450957
81 -0.330948544 0.071436754
82 -0.405087113 -0.330948544
83 0.822126518 -0.405087113
84 0.069224672 0.822126518
85 0.226322827 0.069224672
86 -0.029137533 0.226322827
87 0.275459845 -0.029137533
88 0.641953283 0.275459845
89 -0.386895023 0.641953283
90 -0.803877699 -0.386895023
91 1.277817834 -0.803877699
92 0.410145392 1.277817834
93 -0.195889255 0.410145392
94 0.029455628 -0.195889255
95 0.227128069 0.029455628
96 0.063363242 0.227128069
97 0.061667709 0.063363242
98 0.262241996 0.061667709
99 -0.532644077 0.262241996
100 0.617355923 -0.532644077
101 0.606322827 0.617355923
102 -0.074251461 0.606322827
103 0.110862467 -0.074251461
104 -0.011580570 0.110862467
105 0.559452508 -0.011580570
106 0.818820482 0.559452508
107 -0.609225683 0.818820482
108 -0.237329624 -0.609225683
109 -0.077071341 -0.237329624
110 -0.461209910 -0.077071341
111 -0.638824612 -0.461209910
112 -0.436211461 -0.638824612
113 0.900312406 -0.436211461
114 0.628501377 0.900312406
115 0.772755424 0.628501377
116 -0.393880763 0.772755424
117 -4.109082782 -0.393880763
118 -2.315147840 -4.109082782
119 -2.062008874 -2.315147840
120 -0.773619416 -2.062008874
121 0.288759642 -0.773619416
122 0.507465179 0.288759642
123 0.927410542 0.507465179
124 -3.982504278 0.927410542
125 -0.492154838 -3.982504278
126 -0.757615198 -0.492154838
127 -0.826007815 -0.757615198
128 -0.541179480 -0.826007815
129 -0.498678705 -0.541179480
130 -0.763449300 -0.498678705
131 -0.705892337 -0.763449300
132 -0.985433528 -0.705892337
133 -0.150605193 -0.985433528
134 1.028413246 -0.150605193
135 -1.146588304 1.028413246
136 -0.218113722 -1.146588304
137 -0.609320035 -0.218113722
138 -0.627648729 -0.609320035
139 -0.820982056 -0.627648729
140 -0.586992476 -0.820982056
141 -0.736305813 -0.586992476
142 -1.504005563 -0.736305813
143 -0.647788415 -1.504005563
144 -0.691583671 -0.647788415
145 1.017149195 -0.691583671
146 0.172931761 1.017149195
147 -0.198590556 0.172931761
148 -0.400401566 -0.198590556
149 -0.190401566 -0.400401566
150 0.523387450 -0.190401566
151 0.360889776 0.523387450
152 2.108042549 0.360889776
153 0.674134935 2.108042549
154 1.270859346 0.674134935
155 0.849163813 1.270859346
156 0.831059872 0.849163813
157 0.650427865 0.831059872
158 0.718100306 0.650427865
159 0.729795839 0.718100306
160 0.639106075 0.729795839
161 -0.057448222 0.639106075
162 -0.103169948 -0.057448222
163 4.737030578 -0.103169948
164 1.089042133 4.737030578
165 0.358668391 1.089042133
166 0.342712608 0.358668391
167 1.331549111 0.342712608
168 0.727006351 1.331549111
169 0.221114510 0.727006351
170 0.850291281 0.221114510
171 -0.890887704 0.850291281
172 0.578538009 -0.890887704
173 -0.123273002 0.578538009
174 0.224168484 -0.123273002
175 -0.041060920 0.224168484
176 0.044283962 -0.041060920
177 0.113709675 0.044283962
178 -0.430544372 0.113709675
179 -7.911881256 -0.430544372
180 -4.194727393 -7.911881256
181 -3.044851648 -4.194727393
182 -0.597296093 -3.044851648
183 -5.976890982 -0.597296093
184 0.046034132 -5.976890982
185 -0.220173731 0.046034132
186 -0.177156408 -0.220173731
187 0.561205798 -0.177156408
188 0.533648835 0.561205798
189 0.516091871 0.533648835
190 0.153132287 0.516091871
191 -0.042528599 0.153132287
192 -0.370201041 -0.042528599
193 1.086781617 -0.370201041
194 0.032041451 1.086781617
195 0.402041451 0.032041451
196 1.189944848 0.402041451
197 -0.082355402 1.189944848
198 1.168218885 -0.082355402
199 1.289136504 1.168218885
200 1.370750071 1.289136504
201 0.524457159 1.370750071
202 -0.697876564 0.524457159
203 -1.150662932 -0.697876564
204 -2.918977229 -1.150662932
205 -0.316527464 -2.918977229
206 -0.175861926 -0.316527464
207 -0.604254543 -0.175861926
208 -0.608566329 -0.604254543
209 0.005860935 -0.608566329
210 0.271524922 0.005860935
211 -0.894026689 0.271524922
212 -1.060751101 -0.894026689
213 -0.158396232 -1.060751101
214 0.332898230 -0.158396232
215 0.094277759 0.332898230
216 0.187237344 0.094277759
217 -0.262306948 0.187237344
218 -0.743343127 -0.262306948
219 -0.610526348 -0.743343127
220 1.259510265 -0.610526348
221 -0.310489735 1.259510265
222 -0.674974737 -0.310489735
223 0.123992166 -0.674974737
224 -0.193163726 0.123992166
225 -0.371841936 -0.193163726
226 0.346951751 -0.371841936
227 -0.370924317 0.346951751
228 -0.329286523 -0.370924317
229 0.202038369 -0.329286523
230 -0.758535918 0.202038369
231 -0.654886569 -0.758535918
232 -0.230088664 -0.654886569
233 -0.222416223 -0.230088664
234 0.340285078 -0.222416223
235 0.946377464 0.340285078
236 -0.235375808 0.946377464
237 0.094566454 -0.235375808
238 -0.204339610 0.094566454
239 -0.987846153 -0.204339610
240 -1.005779961 -0.987846153
241 -0.644427759 -1.005779961
242 0.080516052 -0.644427759
243 -0.676697579 0.080516052
244 -0.439140616 -0.676697579
245 -0.509772642 -0.439140616
246 -0.250404668 -0.509772642
247 0.567468281 -0.250404668
248 -0.025549043 0.567468281
249 1.627526019 -0.025549043
250 0.814450957 1.627526019
251 0.511433633 0.814450957
252 0.476836255 0.511433633
253 1.202439402 0.476836255
254 0.261433633 1.202439402
255 0.545572203 0.261433633
256 -0.148800404 0.545572203
257 -0.068481299 -0.148800404
258 0.102296634 -0.068481299
259 -0.108362702 0.102296634
260 0.124770100 -0.108362702
261 -0.785172162 0.124770100
262 -1.119459721 -0.785172162
263 -0.487992042 -1.119459721
264 -0.324023588 -0.487992042
265 0.402734298 -0.324023588
266 0.693682328 0.402734298
267 0.730060306 0.693682328
268 1.093019891 0.730060306
269 0.525979476 1.093019891
270 0.746611502 0.525979476
271 1.335833588 0.746611502
272 0.633420962 1.335833588
273 0.282296615 0.633420962
274 -0.069453537 0.282296615
275 -0.465804188 -0.069453537
276 -0.458131746 -0.465804188
277 -0.201033592 -0.458131746
278 -0.129283422 -0.201033592
279 0.260862467 -0.129283422
280 0.976322827 0.260862467
281 -0.121781096 0.976322827
282 3.639336954 -0.121781096
283 2.730026757 3.639336954
284 1.110285022 2.730026757
285 8.898701865 1.110285022
286 2.696350079 8.898701865
287 2.519020970 2.696350079
288 1.968392064 2.519020970
289 5.838793135 1.968392064
290 1.471467126 5.838793135
291 1.599598377 1.471467126
292 0.659880906 1.599598377
293 -0.508973621 0.659880906
294 1.700828936 -0.508973621
295 -0.043883921 1.700828936
296 -0.150462425 -0.043883921
297 0.403013708 -0.150462425
298 0.850081432 0.403013708
299 0.554736550 0.850081432
300 0.408759642 0.554736550
301 0.781290846 0.408759642
302 0.628331262 0.781290846
303 0.161549111 0.628331262
304 1.076893993 0.161549111
305 0.567583758 1.076893993
306 0.665198461 0.567583758
307 1.343244644 0.665198461
308 0.865687681 1.343244644
309 0.824049886 0.865687681
310 -0.245002046 0.824049886
311 -0.572674488 -0.245002046
312 -1.285779961 -0.572674488
313 -0.363510141 -1.285779961
314 0.069194262 -0.363510141
315 -0.461322286 0.069194262
316 0.143390570 -0.461322286
317 0.236751225 0.143390570
318 -0.302589439 0.236751225
319 0.027650782 -0.302589439
320 0.688656550 0.027650782
321 1.870467561 0.688656550
322 1.626098036 1.870467561
323 1.138656550 1.626098036
324 1.407277021 1.138656550
325 1.201327441 1.407277021
326 0.854138036 1.201327441
327 1.004229286 0.854138036
328 0.181871317 1.004229286
329 0.158449822 0.181871317
330 6.465742318 0.158449822
331 0.224766960 6.465742318
332 0.565572203 0.224766960
333 0.708735415 0.565572203
334 5.305660352 0.708735415
335 0.557355885 5.305660352
336 1.494766942 0.557355885
337 0.500692332 1.494766942
338 -0.127700284 0.500692332
339 1.860892877 -0.127700284
340 0.471837805 1.860892877
341 0.777410542 0.471837805
342 0.292928641 0.777410542
343 0.480169601 0.292928641
344 0.323761211 0.480169601
345 0.165405208 0.323761211
346 -0.344257644 0.165405208
347 -0.750061336 -0.344257644
348 -0.453938539 -0.750061336
349 -0.097186818 -0.453938539
350 0.048158064 -0.097186818
351 -0.431267649 0.048158064
352 0.112323962 -0.431267649
353 -0.432215678 0.112323962
354 -1.207019785 -0.432215678
355 0.281351705 -1.207019785
356 -0.926262997 0.281351705
357 0.072843611 -0.926262997
358 0.424770100 0.072843611
359 -0.391492402 0.424770100
360 1.003648854 -0.391492402
361 -0.107417755 1.003648854
362 -1.273829245 -0.107417755
363 -3.739044253 -1.273829245
364 0.274800510 -3.739044253
365 -0.525600560 0.274800510
366 0.420090755 -0.525600560
367 -0.011951211 0.420090755
368 -0.304336509 -0.011951211
369 0.063998369 -0.304336509
370 0.785119615 0.063998369
371 -0.073674072 0.785119615
372 -0.142984307 -0.073674072
373 -0.260431996 -0.142984307
374 -0.271927003 -0.260431996
375 0.556492942 -0.271927003
376 0.312585328 0.556492942
377 -0.218104437 0.312585328
378 0.021895563 -0.218104437
379 0.527182707 0.021895563
380 0.164165383 0.527182707
381 -0.008162176 0.164165383
382 -0.756089780 -0.008162176
383 -0.815804188 -0.756089780
384 -1.762544068 -0.815804188
385 -4.429099436 -1.762544068
386 -4.913940156 -4.429099436
387 -5.396902842 -4.913940156
388 -5.620950725 -5.396902842
389 -6.386280665 -5.620950725
390 -6.022559218 -6.386280665
391 -0.035776878 -6.022559218
392 -0.491753786 -0.035776878
393 1.136894012 -0.491753786
394 0.673217335 1.136894012
395 -0.082674506 0.673217335
396 -0.004604096 -0.082674506
397 0.350543343 -0.004604096
398 0.221175369 0.350543343
399 0.541375895 0.221175369
400 -0.327183717 0.541375895
401 -0.002413122 -0.327183717
402 -0.729338059 -0.002413122
403 0.773047238 -0.729338059
404 -0.174567464 0.773047238
405 -0.382586338 -0.174567464
406 0.402816283 -0.382586338
407 -0.387125978 0.402816283
408 -0.160316518 -0.387125978
409 -10.049718136 -0.160316518
410 -7.013190096 -10.049718136
411 -1.991177470 -7.013190096
412 -5.492387410 -1.991177470
413 0.189531410 -5.492387410
414 -2.757390972 0.189531410
415 -6.398519491 -2.757390972
416 -2.173103413 -6.398519491
417 -1.902771922 -2.173103413
418 -0.411322286 -1.902771922
419 -0.915403117 -0.411322286
420 -0.124828830 -0.915403117
421 -0.440517044 -0.124828830
422 -0.433303412 -0.440517044
423 -0.235229900 -0.433303412
424 0.745833607 -0.235229900
425 0.176894012 0.745833607
426 -1.661578014 0.176894012
427 0.320570652 -1.661578014
428 0.656979042 0.320570652
429 0.746805825 0.656979042
430 1.356979042 0.746805825
431 0.379996365 1.356979042
432 0.921318156 0.379996365
433 1.589738101 0.921318156
434 0.715082983 1.589738101
435 0.341117630 0.715082983
436 -0.533221465 0.341117630
437 -0.570435097 -0.533221465
438 0.528215803 -0.570435097
439 0.023560685 0.528215803
440 -0.263364252 0.023560685
441 -0.475634072 -0.263364252
442 -0.122732226 -0.475634072
443 -0.485807289 -0.122732226
444 -0.708134848 -0.485807289
445 9.680386807 -0.708134848
446 5.239851784 9.680386807
447 1.873762631 5.239851784
448 -9.108920983 1.873762631
449 -3.586893019 -9.108920983
450 -14.494274829 -3.586893019
451 -12.782180687 -14.494274829
452 -10.891846677 -12.782180687
453 -1.722907048 -10.891846677
454 0.787026584 -1.722907048
455 8.646975723 0.787026584
456 6.299573498 8.646975723
457 1.029200357 6.299573498
458 16.526065883 1.029200357
459 0.902398981 16.526065883
460 -2.859102266 0.902398981
461 10.958188371 -2.859102266
> 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/wessaorg/rcomp/tmp/7061z1462484259.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8alzo1462484259.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9sxwq1462484259.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10uhle1462484259.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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.row.start(a)
> a<-table.element(a, mywarning)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11l51q1462484259.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/125c9h1462484259.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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
> 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,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13k0v01462484259.tab")
> if(n < 200) {
+ 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,formatC(signif(x[i],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/147agr1462484259.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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15hpfj1462484259.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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
+ 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/wessaorg/rcomp/tmp/16pgj21462484259.tab")
+ }
+ }
>
> try(system("convert tmp/1odwu1462484259.ps tmp/1odwu1462484259.png",intern=TRUE))
character(0)
> try(system("convert tmp/28dyn1462484259.ps tmp/28dyn1462484259.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gua71462484259.ps tmp/3gua71462484259.png",intern=TRUE))
character(0)
> try(system("convert tmp/4yu8n1462484259.ps tmp/4yu8n1462484259.png",intern=TRUE))
character(0)
> try(system("convert tmp/545l11462484259.ps tmp/545l11462484259.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yert1462484259.ps tmp/6yert1462484259.png",intern=TRUE))
character(0)
> try(system("convert tmp/7061z1462484259.ps tmp/7061z1462484259.png",intern=TRUE))
character(0)
> try(system("convert tmp/8alzo1462484259.ps tmp/8alzo1462484259.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sxwq1462484259.ps tmp/9sxwq1462484259.png",intern=TRUE))
character(0)
> try(system("convert tmp/10uhle1462484259.ps tmp/10uhle1462484259.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.212 0.755 6.007