R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
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+ ,0
+ ,1
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+ ,36
+ ,11
+ ,9
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+ ,0
+ ,0
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+ ,16
+ ,12
+ ,15
+ ,0
+ ,1
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+ ,35
+ ,15
+ ,10
+ ,14
+ ,0
+ ,0
+ ,27
+ ,29
+ ,12
+ ,9
+ ,13
+ ,0
+ ,1
+ ,16
+ ,22
+ ,6
+ ,6
+ ,12
+ ,0
+ ,0
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+ ,41
+ ,16
+ ,10
+ ,16
+ ,0
+ ,0
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+ ,36
+ ,10
+ ,9
+ ,16
+ ,0
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+ ,42
+ ,42
+ ,15
+ ,13
+ ,9
+ ,0
+ ,1
+ ,30
+ ,33
+ ,14
+ ,12
+ ,14)
+ ,dim=c(7
+ ,288)
+ ,dimnames=list(c('Pop'
+ ,'Gender'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness')
+ ,1:288))
> y <- array(NA,dim=c(7,288),dimnames=list(c('Pop','Gender','Connected','Separate','Learning','Software','Happiness'),1:288))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Gender Pop Connected Separate Learning Software Happiness
1 1 1 41 38 13 12 14
2 1 1 39 32 16 11 18
3 1 1 30 35 19 15 11
4 0 1 31 33 15 6 12
5 1 1 34 37 14 13 16
6 1 1 35 29 13 10 18
7 1 1 39 31 19 12 14
8 1 1 34 36 15 14 14
9 1 1 36 35 14 12 15
10 1 1 37 38 15 9 15
11 0 1 38 31 16 10 17
12 1 1 36 34 16 12 19
13 0 1 38 35 16 12 10
14 1 1 39 38 16 11 16
15 1 1 33 37 17 15 18
16 0 1 32 33 15 12 14
17 0 1 36 32 15 10 14
18 1 1 38 38 20 12 17
19 0 1 39 38 18 11 14
20 1 1 32 32 16 12 16
21 0 1 32 33 16 11 18
22 1 1 31 31 16 12 11
23 1 1 39 38 19 13 14
24 1 1 37 39 16 11 12
25 0 1 39 32 17 12 17
26 1 1 41 32 17 13 9
27 0 1 36 35 16 10 16
28 1 1 33 37 15 14 14
29 1 1 33 33 16 12 15
30 0 1 34 33 14 10 11
31 1 1 31 31 15 12 16
32 0 1 27 32 12 8 13
33 1 1 37 31 14 10 17
34 1 1 34 37 16 12 15
35 0 1 34 30 14 12 14
36 0 1 32 33 10 7 16
37 0 1 29 31 10 9 9
38 0 1 36 33 14 12 15
39 1 1 29 31 16 10 17
40 0 1 35 33 16 10 13
41 0 1 37 32 16 10 15
42 1 1 34 33 14 12 16
43 0 1 38 32 20 15 16
44 0 1 35 33 14 10 12
45 1 1 38 28 14 10 15
46 1 1 37 35 11 12 11
47 1 1 38 39 14 13 15
48 1 1 33 34 15 11 15
49 1 1 36 38 16 11 17
50 0 1 38 32 14 12 13
51 1 1 32 38 16 14 16
52 0 1 32 30 14 10 14
53 0 1 32 33 12 12 11
54 1 1 34 38 16 13 12
55 0 1 32 32 9 5 12
56 1 1 37 35 14 6 15
57 1 1 39 34 16 12 16
58 1 1 29 34 16 12 15
59 0 1 37 36 15 11 12
60 1 1 35 34 16 10 12
61 0 1 30 28 12 7 8
62 0 1 38 34 16 12 13
63 1 1 34 35 16 14 11
64 1 1 31 35 14 11 14
65 1 1 34 31 16 12 15
66 0 1 35 37 17 13 10
67 1 1 36 35 18 14 11
68 0 1 30 27 18 11 12
69 1 1 39 40 12 12 15
70 0 1 35 37 16 12 15
71 0 1 38 36 10 8 14
72 1 1 31 38 14 11 16
73 1 1 34 39 18 14 15
74 0 1 38 41 18 14 15
75 0 1 34 27 16 12 13
76 1 1 39 30 17 9 12
77 1 1 37 37 16 13 17
78 1 1 34 31 16 11 13
79 0 1 28 31 13 12 15
80 0 1 37 27 16 12 13
81 0 1 33 36 16 12 15
82 1 1 35 37 16 12 15
83 0 1 37 33 15 12 16
84 1 1 32 34 15 11 15
85 1 1 33 31 16 10 14
86 0 1 38 39 14 9 15
87 1 1 33 34 16 12 14
88 1 1 29 32 16 12 13
89 1 1 33 33 15 12 7
90 1 1 31 36 12 9 17
91 1 1 36 32 17 15 13
92 1 1 35 41 16 12 15
93 1 1 32 28 15 12 14
94 1 1 29 30 13 12 13
95 1 1 39 36 16 10 16
96 1 1 37 35 16 13 12
97 1 1 35 31 16 9 14
98 0 1 37 34 16 12 17
99 0 1 32 36 14 10 15
100 1 1 38 36 16 14 17
101 0 1 37 35 16 11 12
102 1 1 36 37 20 15 16
103 0 1 32 28 15 11 11
104 1 1 33 39 16 11 15
105 0 1 40 32 13 12 9
106 1 1 38 35 17 12 16
107 0 1 41 39 16 12 15
108 0 1 36 35 16 11 10
109 1 1 43 42 12 7 10
110 1 1 30 34 16 12 15
111 1 1 31 33 16 14 11
112 1 1 32 41 17 11 13
113 1 1 37 34 12 10 18
114 0 1 37 32 18 13 16
115 1 1 33 40 14 13 14
116 1 1 34 40 14 8 14
117 1 1 33 35 13 11 14
118 1 1 38 36 16 12 14
119 0 1 33 37 13 11 12
120 1 1 31 27 16 13 14
121 1 1 38 39 13 12 15
122 1 1 37 38 16 14 15
123 1 1 36 31 15 13 15
124 1 1 31 33 16 15 13
125 0 1 39 32 15 10 17
126 1 1 44 39 17 11 17
127 1 1 33 36 15 9 19
128 1 1 35 33 12 11 15
129 0 1 32 33 16 10 13
130 0 1 28 32 10 11 9
131 1 1 40 37 16 8 15
132 0 1 27 30 12 11 15
133 0 1 37 38 14 12 15
134 1 1 32 29 15 12 16
135 0 1 28 22 13 9 11
136 0 1 34 35 15 11 14
137 1 1 30 35 11 10 11
138 1 1 35 34 12 8 15
139 0 1 31 35 11 9 13
140 1 1 32 34 16 8 15
141 0 1 30 37 15 9 16
142 1 1 30 35 17 15 14
143 0 1 31 23 16 11 15
144 1 1 40 31 10 8 16
145 1 1 32 27 18 13 16
146 0 1 36 36 13 12 11
147 0 1 32 31 16 12 12
148 0 1 35 32 13 9 9
149 1 1 38 39 10 7 16
150 1 1 42 37 15 13 13
151 0 1 34 38 16 9 16
152 1 1 35 39 16 6 12
153 1 1 38 34 14 8 9
154 1 1 33 31 10 8 13
155 1 1 32 37 13 6 14
156 1 1 33 36 15 9 19
157 1 1 34 32 16 11 13
158 1 1 32 38 12 8 12
159 0 0 27 26 13 10 10
160 0 0 31 26 12 8 14
161 0 0 38 33 17 14 16
162 1 0 34 39 15 10 10
163 0 0 24 30 10 8 11
164 0 0 30 33 14 11 14
165 1 0 26 25 11 12 12
166 1 0 34 38 13 12 9
167 0 0 27 37 16 12 9
168 0 0 37 31 12 5 11
169 1 0 36 37 16 12 16
170 0 0 41 35 12 10 9
171 1 0 29 25 9 7 13
172 1 0 36 28 12 12 16
173 0 0 32 35 15 11 13
174 1 0 37 33 12 8 9
175 0 0 30 30 12 9 12
176 1 0 31 31 14 10 16
177 1 0 38 37 12 9 11
178 1 0 36 36 16 12 14
179 0 0 35 30 11 6 13
180 0 0 31 36 19 15 15
181 0 0 38 32 15 12 14
182 1 0 22 28 8 12 16
183 1 0 32 36 16 12 13
184 0 0 36 34 17 11 14
185 1 0 39 31 12 7 15
186 0 0 28 28 11 7 13
187 0 0 32 36 11 5 11
188 1 0 32 36 14 12 11
189 1 0 38 40 16 12 14
190 1 0 32 33 12 3 15
191 1 0 35 37 16 11 11
192 1 0 32 32 13 10 15
193 0 0 37 38 15 12 12
194 1 0 34 31 16 9 14
195 1 0 33 37 16 12 14
196 0 0 33 33 14 9 8
197 0 0 30 30 16 12 9
198 0 0 24 30 14 10 15
199 0 0 34 31 11 9 17
200 0 0 34 32 12 12 13
201 1 0 33 34 15 8 15
202 1 0 34 36 15 11 15
203 1 0 35 37 16 11 14
204 0 0 35 36 16 12 16
205 0 0 36 33 11 10 13
206 0 0 34 33 15 10 16
207 1 0 34 33 12 12 9
208 0 0 41 44 12 12 16
209 0 0 32 39 15 11 11
210 0 0 30 32 15 8 10
211 1 0 35 35 16 12 11
212 0 0 28 25 14 10 15
213 1 0 33 35 17 11 17
214 1 0 39 34 14 10 14
215 0 0 36 35 13 8 8
216 1 0 36 39 15 12 15
217 0 0 35 33 13 12 11
218 0 0 38 36 14 10 16
219 1 0 33 32 15 12 10
220 0 0 31 32 12 9 15
221 1 0 32 36 8 6 16
222 0 0 31 32 14 10 19
223 0 0 33 34 14 9 12
224 0 0 34 33 11 9 8
225 0 0 34 35 12 9 11
226 1 0 34 30 13 6 14
227 0 0 33 38 10 10 9
228 0 0 32 34 16 6 15
229 1 0 41 33 18 14 13
230 1 0 34 32 13 10 16
231 0 0 36 31 11 10 11
232 0 0 37 30 4 6 12
233 0 0 36 27 13 12 13
234 1 0 29 31 16 12 10
235 0 0 37 30 10 7 11
236 0 0 27 32 12 8 12
237 0 0 35 35 12 11 8
238 0 0 28 28 10 3 12
239 0 0 35 33 13 6 12
240 0 0 29 35 12 8 11
241 0 0 32 35 14 9 13
242 1 0 36 32 10 9 14
243 1 0 19 21 12 8 10
244 1 0 21 20 12 9 12
245 0 0 31 34 11 7 15
246 0 0 33 32 10 7 13
247 1 0 36 34 12 6 13
248 1 0 33 32 16 9 13
249 0 0 37 33 12 10 12
250 0 0 34 33 14 11 12
251 0 0 35 37 16 12 9
252 1 0 31 32 14 8 9
253 1 0 37 34 13 11 15
254 1 0 35 30 4 3 10
255 1 0 27 30 15 11 14
256 0 0 34 38 11 12 15
257 0 0 40 36 11 7 7
258 0 0 29 32 14 9 14
259 0 0 38 34 15 12 8
260 1 0 34 33 14 8 10
261 0 0 21 27 13 11 13
262 0 0 36 32 11 8 13
263 1 0 38 34 15 10 13
264 0 0 30 29 11 8 8
265 0 0 35 35 13 7 12
266 1 0 30 27 13 8 13
267 1 0 36 33 16 10 12
268 0 0 34 38 13 8 10
269 1 0 35 36 16 12 13
270 0 0 34 33 16 14 12
271 0 0 32 39 12 7 9
272 1 0 33 29 7 6 15
273 0 0 33 32 16 11 13
274 1 0 26 34 5 4 13
275 0 0 35 38 16 9 13
276 0 0 21 17 4 5 15
277 0 0 38 35 12 9 15
278 0 0 35 32 15 11 14
279 1 0 33 34 14 12 15
280 0 0 37 36 11 9 11
281 0 0 38 31 16 12 15
282 1 0 34 35 15 10 14
283 0 0 27 29 12 9 13
284 1 0 16 22 6 6 12
285 0 0 40 41 16 10 16
286 0 0 36 36 10 9 16
287 1 0 42 42 15 13 9
288 1 0 30 33 14 12 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop Connected Separate Learning Software
-0.430055 0.114625 -0.007721 0.015112 0.001974 0.017618
Happiness
0.032870
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.7860 -0.4793 0.2263 0.4228 0.8575
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.430055 0.317311 -1.355 0.17641
Pop 0.114625 0.063605 1.802 0.07260 .
Connected -0.007721 0.008417 -0.917 0.35976
Separate 0.015112 0.008715 1.734 0.08402 .
Learning 0.001974 0.015172 0.130 0.89657
Software 0.017618 0.016157 1.090 0.27645
Happiness 0.032870 0.012007 2.738 0.00658 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.483 on 281 degrees of freedom
Multiple R-squared: 0.08587, Adjusted R-squared: 0.06635
F-statistic: 4.399 on 6 and 281 DF, p-value: 0.0002869
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.08223569 0.1644714 0.9177643
[2,] 0.48757763 0.9751553 0.5124224
[3,] 0.34237231 0.6847446 0.6576277
[4,] 0.40282211 0.8056442 0.5971779
[5,] 0.29017000 0.5803400 0.7098300
[6,] 0.28038919 0.5607784 0.7196108
[7,] 0.36327146 0.7265429 0.6367285
[8,] 0.34399937 0.6879987 0.6560006
[9,] 0.27070042 0.5414008 0.7292996
[10,] 0.37768941 0.7553788 0.6223106
[11,] 0.32462998 0.6492600 0.6753700
[12,] 0.45094487 0.9018897 0.5490551
[13,] 0.49733358 0.9946672 0.5026664
[14,] 0.43367582 0.8673516 0.5663242
[15,] 0.40636394 0.8127279 0.5936361
[16,] 0.48813766 0.9762753 0.5118623
[17,] 0.46350787 0.9270157 0.5364921
[18,] 0.47685710 0.9537142 0.5231429
[19,] 0.41332131 0.8266426 0.5866787
[20,] 0.38210314 0.7642063 0.6178969
[21,] 0.37078459 0.7415692 0.6292154
[22,] 0.34188368 0.6837674 0.6581163
[23,] 0.29741854 0.5948371 0.7025815
[24,] 0.29498859 0.5899772 0.7050114
[25,] 0.25818718 0.5163744 0.7418128
[26,] 0.30347416 0.6069483 0.6965258
[27,] 0.26833645 0.5366729 0.7316635
[28,] 0.22795746 0.4559149 0.7720425
[29,] 0.28288771 0.5657754 0.7171123
[30,] 0.29561652 0.5912330 0.7043835
[31,] 0.28366091 0.5673218 0.7163391
[32,] 0.27629004 0.5525801 0.7237100
[33,] 0.24969815 0.4993963 0.7503019
[34,] 0.37349778 0.7469956 0.6265022
[35,] 0.35160997 0.7032199 0.6483900
[36,] 0.41207845 0.8241569 0.5879216
[37,] 0.38875291 0.7775058 0.6112471
[38,] 0.34472640 0.6894528 0.6552736
[39,] 0.33163982 0.6632796 0.6683602
[40,] 0.29735188 0.5947038 0.7026481
[41,] 0.31654172 0.6330834 0.6834583
[42,] 0.27782075 0.5556415 0.7221792
[43,] 0.26302586 0.5260517 0.7369741
[44,] 0.27222056 0.5444411 0.7277794
[45,] 0.24469930 0.4893986 0.7553007
[46,] 0.21743527 0.4348705 0.7825647
[47,] 0.26116670 0.5223334 0.7388333
[48,] 0.23784703 0.4756941 0.7621530
[49,] 0.22038038 0.4407608 0.7796196
[50,] 0.23404679 0.4680936 0.7659532
[51,] 0.25398678 0.5079736 0.7460132
[52,] 0.23050036 0.4610007 0.7694996
[53,] 0.24691444 0.4938289 0.7530856
[54,] 0.23145273 0.4629055 0.7685473
[55,] 0.21621488 0.4324298 0.7837851
[56,] 0.21079883 0.4215977 0.7892012
[57,] 0.23366106 0.4673221 0.7663389
[58,] 0.22199898 0.4439980 0.7780010
[59,] 0.20416582 0.4083316 0.7958342
[60,] 0.17873661 0.3574732 0.8212634
[61,] 0.23194802 0.4638960 0.7680520
[62,] 0.23240190 0.4648038 0.7675981
[63,] 0.20625238 0.4125048 0.7937476
[64,] 0.18008675 0.3601735 0.8199132
[65,] 0.27520423 0.5504085 0.7247958
[66,] 0.26671739 0.5334348 0.7332826
[67,] 0.32314236 0.6462847 0.6768576
[68,] 0.29159124 0.5831825 0.7084088
[69,] 0.29970310 0.5994062 0.7002969
[70,] 0.32185755 0.6437151 0.6781425
[71,] 0.31312463 0.6262493 0.6868754
[72,] 0.35818891 0.7163778 0.6418111
[73,] 0.33308164 0.6661633 0.6669184
[74,] 0.36582247 0.7316449 0.6341775
[75,] 0.35218639 0.7043728 0.6478136
[76,] 0.35924647 0.7184929 0.6407535
[77,] 0.38856390 0.7771278 0.6114361
[78,] 0.37250932 0.7450186 0.6274907
[79,] 0.36219865 0.7243973 0.6378014
[80,] 0.38500548 0.7700110 0.6149945
[81,] 0.36804801 0.7360960 0.6319520
[82,] 0.34864545 0.6972909 0.6513546
[83,] 0.31911938 0.6382388 0.6808806
[84,] 0.31812124 0.6362425 0.6818788
[85,] 0.31133060 0.6226612 0.6886694
[86,] 0.29959165 0.5991833 0.7004083
[87,] 0.28739154 0.5747831 0.7126085
[88,] 0.29627202 0.5925440 0.7037280
[89,] 0.33661054 0.6732211 0.6633895
[90,] 0.36422438 0.7284488 0.6357756
[91,] 0.33520732 0.6704146 0.6647927
[92,] 0.34465627 0.6893125 0.6553437
[93,] 0.31460826 0.6292165 0.6853917
[94,] 0.30734467 0.6146893 0.6926553
[95,] 0.28429867 0.5685973 0.7157013
[96,] 0.27194752 0.5438950 0.7280525
[97,] 0.25316549 0.5063310 0.7468345
[98,] 0.28827490 0.5765498 0.7117251
[99,] 0.28894483 0.5778897 0.7110552
[100,] 0.31487996 0.6297599 0.6851200
[101,] 0.29368878 0.5873776 0.7063112
[102,] 0.28058412 0.5611682 0.7194159
[103,] 0.25846822 0.5169364 0.7415318
[104,] 0.24631386 0.4926277 0.7536861
[105,] 0.27748235 0.5549647 0.7225176
[106,] 0.25435253 0.5087051 0.7456475
[107,] 0.24131782 0.4826356 0.7586822
[108,] 0.22990447 0.4598089 0.7700955
[109,] 0.21724246 0.4344849 0.7827575
[110,] 0.23410304 0.4682061 0.7658970
[111,] 0.22956866 0.4591373 0.7704313
[112,] 0.21246753 0.4249351 0.7875325
[113,] 0.19345571 0.3869114 0.8065443
[114,] 0.18459821 0.3691964 0.8154018
[115,] 0.17168801 0.3433760 0.8283120
[116,] 0.18304363 0.3660873 0.8169564
[117,] 0.16842353 0.3368471 0.8315765
[118,] 0.15292248 0.3058450 0.8470775
[119,] 0.14797907 0.2959581 0.8520209
[120,] 0.15360934 0.3072187 0.8463907
[121,] 0.14896944 0.2979389 0.8510306
[122,] 0.14574958 0.2914992 0.8542504
[123,] 0.15551323 0.3110265 0.8444868
[124,] 0.18364559 0.3672912 0.8163544
[125,] 0.17496021 0.3499204 0.8250398
[126,] 0.16579981 0.3315996 0.8342002
[127,] 0.18426690 0.3685338 0.8157331
[128,] 0.18470963 0.3694193 0.8152904
[129,] 0.18303754 0.3660751 0.8169625
[130,] 0.19121832 0.3824366 0.8087817
[131,] 0.18347717 0.3669543 0.8165228
[132,] 0.21598814 0.4319763 0.7840119
[133,] 0.19570487 0.3914097 0.8042951
[134,] 0.20624180 0.4124836 0.7937582
[135,] 0.20877874 0.4175575 0.7912213
[136,] 0.19522627 0.3904525 0.8047737
[137,] 0.20931240 0.4186248 0.7906876
[138,] 0.23154540 0.4630908 0.7684546
[139,] 0.24172727 0.4834545 0.7582727
[140,] 0.22657873 0.4531575 0.7734213
[141,] 0.20768862 0.4153772 0.7923114
[142,] 0.26603306 0.5320661 0.7339669
[143,] 0.25967303 0.5193461 0.7403270
[144,] 0.26552291 0.5310458 0.7344771
[145,] 0.26346092 0.5269218 0.7365391
[146,] 0.24969219 0.4993844 0.7503078
[147,] 0.22638122 0.4527624 0.7736188
[148,] 0.21353046 0.4270609 0.7864695
[149,] 0.19870581 0.3974116 0.8012942
[150,] 0.18443462 0.3688692 0.8155654
[151,] 0.17356975 0.3471395 0.8264302
[152,] 0.17466398 0.3493280 0.8253360
[153,] 0.19696758 0.3939352 0.8030324
[154,] 0.18179994 0.3635999 0.8182001
[155,] 0.17706744 0.3541349 0.8229326
[156,] 0.20409401 0.4081880 0.7959060
[157,] 0.22113534 0.4422707 0.7788647
[158,] 0.21492662 0.4298532 0.7850734
[159,] 0.19808734 0.3961747 0.8019127
[160,] 0.19250329 0.3850066 0.8074967
[161,] 0.17744222 0.3548844 0.8225578
[162,] 0.20590068 0.4118014 0.7940993
[163,] 0.20657949 0.4131590 0.7934205
[164,] 0.20756004 0.4151201 0.7924400
[165,] 0.23819392 0.4763878 0.7618061
[166,] 0.22853127 0.4570625 0.7714687
[167,] 0.22566784 0.4513357 0.7743322
[168,] 0.24176361 0.4835272 0.7582364
[169,] 0.23856951 0.4771390 0.7614305
[170,] 0.22705157 0.4541031 0.7729484
[171,] 0.25101092 0.5020218 0.7489891
[172,] 0.25183029 0.5036606 0.7481697
[173,] 0.25284756 0.5056951 0.7471524
[174,] 0.25309483 0.5061897 0.7469052
[175,] 0.25880030 0.5176006 0.7411997
[176,] 0.26768773 0.5353755 0.7323123
[177,] 0.25754969 0.5150994 0.7424503
[178,] 0.24311656 0.4862331 0.7568834
[179,] 0.25532200 0.5106440 0.7446780
[180,] 0.25685872 0.5137174 0.7431413
[181,] 0.26560570 0.5312114 0.7343943
[182,] 0.27635524 0.5527105 0.7236448
[183,] 0.27899632 0.5579926 0.7210037
[184,] 0.27836434 0.5567287 0.7216357
[185,] 0.28102961 0.5620592 0.7189704
[186,] 0.28346158 0.5669232 0.7165384
[187,] 0.26664829 0.5332966 0.7333517
[188,] 0.25826295 0.5165259 0.7417371
[189,] 0.26266446 0.5253289 0.7373355
[190,] 0.26440063 0.5288013 0.7355994
[191,] 0.25953134 0.5190627 0.7404687
[192,] 0.26405927 0.5281185 0.7359407
[193,] 0.26777732 0.5355546 0.7322227
[194,] 0.27589377 0.5517875 0.7241062
[195,] 0.28399181 0.5679836 0.7160082
[196,] 0.27479446 0.5495889 0.7252055
[197,] 0.27806629 0.5561326 0.7219337
[198,] 0.30528989 0.6105798 0.6947101
[199,] 0.31194198 0.6238840 0.6880580
[200,] 0.30092771 0.6018554 0.6990723
[201,] 0.28724988 0.5744998 0.7127501
[202,] 0.30177876 0.6035575 0.6982212
[203,] 0.31299628 0.6259926 0.6870037
[204,] 0.30484371 0.6096874 0.6951563
[205,] 0.31865013 0.6373003 0.6813499
[206,] 0.29432539 0.5886508 0.7056746
[207,] 0.31367893 0.6273579 0.6863211
[208,] 0.29884918 0.5976984 0.7011508
[209,] 0.29282341 0.5856468 0.7071766
[210,] 0.31274822 0.6254964 0.6872518
[211,] 0.30658788 0.6131758 0.6934121
[212,] 0.34074207 0.6814841 0.6592579
[213,] 0.35275585 0.7055117 0.6472442
[214,] 0.33863124 0.6772625 0.6613688
[215,] 0.31087967 0.6217593 0.6891203
[216,] 0.28980736 0.5796147 0.7101926
[217,] 0.30106752 0.6021350 0.6989325
[218,] 0.27480367 0.5496073 0.7251963
[219,] 0.27268355 0.5453671 0.7273165
[220,] 0.28026475 0.5605295 0.7197352
[221,] 0.28942011 0.5788402 0.7105799
[222,] 0.26785040 0.5357008 0.7321496
[223,] 0.24080241 0.4816048 0.7591976
[224,] 0.23737958 0.4747592 0.7626204
[225,] 0.24870916 0.4974183 0.7512908
[226,] 0.23651662 0.4730332 0.7634834
[227,] 0.22248800 0.4449760 0.7775120
[228,] 0.20267355 0.4053471 0.7973264
[229,] 0.20137157 0.4027431 0.7986284
[230,] 0.19805617 0.3961123 0.8019438
[231,] 0.18413318 0.3682664 0.8158668
[232,] 0.17685909 0.3537182 0.8231409
[233,] 0.19609750 0.3921950 0.8039025
[234,] 0.19216111 0.3843222 0.8078389
[235,] 0.18953543 0.3790709 0.8104646
[236,] 0.18962753 0.3792551 0.8103725
[237,] 0.18412077 0.3682415 0.8158792
[238,] 0.18134888 0.3626978 0.8186511
[239,] 0.18320032 0.3664006 0.8167997
[240,] 0.16638285 0.3327657 0.8336172
[241,] 0.15286965 0.3057393 0.8471303
[242,] 0.13735743 0.2747149 0.8626426
[243,] 0.14995687 0.2999137 0.8500431
[244,] 0.16045199 0.3209040 0.8395480
[245,] 0.19294265 0.3858853 0.8070574
[246,] 0.19303474 0.3860695 0.8069653
[247,] 0.18819134 0.3763827 0.8118087
[248,] 0.15877999 0.3175600 0.8412200
[249,] 0.14790400 0.2958080 0.8520960
[250,] 0.12911107 0.2582221 0.8708889
[251,] 0.15656832 0.3131366 0.8434317
[252,] 0.17912417 0.3582483 0.8208758
[253,] 0.15003742 0.3000748 0.8499626
[254,] 0.19840652 0.3968130 0.8015935
[255,] 0.17018580 0.3403716 0.8298142
[256,] 0.13679499 0.2735900 0.8632050
[257,] 0.19234900 0.3846980 0.8076510
[258,] 0.41164935 0.8232987 0.5883507
[259,] 0.34976154 0.6995231 0.6502385
[260,] 0.37929913 0.7585983 0.6207009
[261,] 0.42028397 0.8405679 0.5797160
[262,] 0.39005474 0.7801095 0.6099453
[263,] 0.75570618 0.4885876 0.2442938
[264,] 0.69386087 0.6122783 0.3061391
[265,] 0.70386077 0.5922785 0.2961392
[266,] 0.61059568 0.7788086 0.3894043
[267,] 0.53728974 0.9254205 0.4627103
[268,] 0.43864428 0.8772886 0.5613557
[269,] 0.31460629 0.6292126 0.6853937
> postscript(file="/var/wessaorg/rcomp/tmp/1lfrt1386667247.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2fpdq1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30owg1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/41ekb1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5aobn1386667247.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 = 288
Frequency = 1
1 2 3 4 5 6 7
0.3604840 0.3159296 0.3547983 -0.4736663 0.2362164 0.3539213 0.4389804
8 9 10 11 12 13 14
0.2974757 0.3323703 0.3456365 -0.6261915 0.2120546 -0.4917864 0.2909982
15 16 17 18 19 20 21
0.1215969 -0.6373944 -0.5561619 0.2248927 -0.6472102 0.3100036 -0.7532297
22 23 24 25 26 27 28
0.4817437 0.3155792 0.3919237 -0.6707929 0.5899899 -0.6692112 0.2746428
29 30 31 32 33 34 35
0.3354827 -0.4861322 0.3193685 -0.5516228 0.3700356 0.2827564 -0.5746427
36 37 38 39 40 41 42
-0.6051726 -0.4032595 -0.6374060 0.3043189 -0.5480990 -0.5832847 0.3142820
43 44 45 46 47 48 49
-0.7044210 -0.5112810 0.4888319 0.4774931 0.2697468 0.3399632 0.2349652
50 51 52 53 54 55 56
-0.5411123 0.1840960 -0.5548483 -0.5328626 0.3486358 -0.4213707 0.4458008
57 58 59 60 61 62 63
0.3338273 0.2894866 -0.5607667 0.4696590 -0.2860447 -0.5752841 0.4092230
64 65 66 67 68 69 70
0.3442531 0.3734274 -0.5647656 0.4207170 -0.4847297 0.2839224 -0.7095226
71 72 73 74 75 76 77
-0.5560603 0.2331779 0.2133480 -0.7859914 -0.5003854 0.5766348 0.2225616
78 79 80 81 82 83 84
0.4567854 -0.6669767 -0.4772223 -0.7098529 0.2904774 -0.6645288 0.3322421
85 86 87 88 89 90 91
0.4338127 -0.6597802 0.3532407 0.3854500 0.6004156 0.2697163 0.3846686
92 93 94 95 96 97 98
0.2300300 0.4381648 0.4215959 0.3388401 0.4171346 0.4668731 -0.7144846
99 100 101 102 103 104 105
-0.6783893 0.2277763 -0.5476290 0.2045776 -0.4456074 0.2624298 -0.3922166
106 107 108 109 110 111 112
0.3090204 -0.6934199 -0.4896103 0.5370235 0.2972077 0.4162835 0.2882507
113 114 115 116 117 118 119
0.2957783 -0.6729575 0.2488995 0.3447118 0.3616693 0.3616223 -0.6028147
120 121 122 123 124 125 126
0.4259633 0.2893391 0.2555712 0.3732254 0.3329256 -0.6316083 0.2796477
127 128 129 130 131 132 133
0.2134965 0.3764394 -0.5712622 -0.4613289 0.3995557 -0.6399936 -0.7052442
134 135 136 137 138 139 140
0.3573132 -0.3466359 -0.6345578 0.4586821 0.4141822 -0.5817183 0.3831227
141 142 143 144 145 146 147
-0.7261690 0.2601369 -0.5112227 0.4692014 0.3639965 -0.5492880 -0.5434051
148 149 150 151 152 153 154
-0.3779672 0.3504827 0.3946204 -0.7123706 0.4645727 0.6306164 0.5137635
155 156 157 158 159 160 161
0.4118158 0.2134965 0.4416736 0.4291812 -0.2849282 -0.3483128 -0.5813678
162 163 164 165 166 167 168
0.5687171 -0.3602499 -0.5186197 0.6254346 0.5854105 -0.4594473 -0.2260814
169 170 171 172 173 174 175
0.3799532 -0.2779960 0.7077673 0.5238562 -0.5025055 0.7565799 -0.3683598
176 177 178 179 180 181 182
0.4712036 0.6204956 0.4608048 -0.3077955 -0.6694474 -0.4613316 0.4236576
183 184 185 186 187 188 189
0.4627904 -0.4933274 0.6226448 -0.3492375 -0.3382718 0.5324783 0.4157995
190 191 192 193 194 195 196
0.6088467 0.5541997 0.4986568 -0.4939841 0.5737766 0.4225297 -0.2630008
197 198 199 200 201 202 203
-0.3305012 -0.5348621 -0.5149626 -0.4534238 0.5074425 0.4320851 0.4555901
204 205 206 207 208 209 210
-0.6126560 -0.4158830 -0.5378310 0.6629438 -0.6793281 -0.4972131 -0.3211477
211 212 213 214 215 216 217
0.5668051 -0.4284186 0.3697880 0.5533763 -0.2504690 0.3845734 -0.3970489
218 219 220 221 222 223 224
-0.5503082 0.6315425 -0.4894720 0.4856829 -0.6425178 -0.4095921 -0.2493575
225 226 227 228 229 230 231
-0.3801649 0.6476654 -0.3811519 -0.4670162 0.5384308 0.4812290 -0.3199195
232 233 234 235 236 237 238
-0.2456650 -0.3643965 0.6137960 -0.2422578 -0.4041284 -0.3090707 -0.2439206
239 240 241 242 243 244 245
-0.3242094 -0.4011520 -0.4652949 0.5859514 0.7660732 0.7132692 -0.4824851
246 247 248 249 250 251 252
-0.3691055 0.6375041 0.5838136 -0.3772661 -0.4219957 -0.3976789 0.7214173
253 254 255 256 257 258 259
0.4894201 0.8574873 0.5015786 -0.6078605 -0.1802604 -0.4759924 -0.2943362
260 261 262 263 264 265 266
0.6965987 -0.4625942 -0.3655346 0.5765510 -0.2021762 -0.3720513 0.6597501
267 268 269 270 271 272 273
0.6071165 -0.3769864 0.4859536 -0.4787986 -0.3550782 0.6340308 -0.4514229
274 275 276 277 278 279 280
0.6093485 -0.4914154 -0.2537393 -0.4807601 -0.4668766 0.4389436 -0.3701395
281 282 283 284 285 286 287
-0.4810637 0.4976850 -0.4092810 0.7091394 -0.6143735 -0.5402358 0.5651652
288
0.4637621
> postscript(file="/var/wessaorg/rcomp/tmp/69pl31386667247.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 = 288
Frequency = 1
lag(myerror, k = 1) myerror
0 0.3604840 NA
1 0.3159296 0.3604840
2 0.3547983 0.3159296
3 -0.4736663 0.3547983
4 0.2362164 -0.4736663
5 0.3539213 0.2362164
6 0.4389804 0.3539213
7 0.2974757 0.4389804
8 0.3323703 0.2974757
9 0.3456365 0.3323703
10 -0.6261915 0.3456365
11 0.2120546 -0.6261915
12 -0.4917864 0.2120546
13 0.2909982 -0.4917864
14 0.1215969 0.2909982
15 -0.6373944 0.1215969
16 -0.5561619 -0.6373944
17 0.2248927 -0.5561619
18 -0.6472102 0.2248927
19 0.3100036 -0.6472102
20 -0.7532297 0.3100036
21 0.4817437 -0.7532297
22 0.3155792 0.4817437
23 0.3919237 0.3155792
24 -0.6707929 0.3919237
25 0.5899899 -0.6707929
26 -0.6692112 0.5899899
27 0.2746428 -0.6692112
28 0.3354827 0.2746428
29 -0.4861322 0.3354827
30 0.3193685 -0.4861322
31 -0.5516228 0.3193685
32 0.3700356 -0.5516228
33 0.2827564 0.3700356
34 -0.5746427 0.2827564
35 -0.6051726 -0.5746427
36 -0.4032595 -0.6051726
37 -0.6374060 -0.4032595
38 0.3043189 -0.6374060
39 -0.5480990 0.3043189
40 -0.5832847 -0.5480990
41 0.3142820 -0.5832847
42 -0.7044210 0.3142820
43 -0.5112810 -0.7044210
44 0.4888319 -0.5112810
45 0.4774931 0.4888319
46 0.2697468 0.4774931
47 0.3399632 0.2697468
48 0.2349652 0.3399632
49 -0.5411123 0.2349652
50 0.1840960 -0.5411123
51 -0.5548483 0.1840960
52 -0.5328626 -0.5548483
53 0.3486358 -0.5328626
54 -0.4213707 0.3486358
55 0.4458008 -0.4213707
56 0.3338273 0.4458008
57 0.2894866 0.3338273
58 -0.5607667 0.2894866
59 0.4696590 -0.5607667
60 -0.2860447 0.4696590
61 -0.5752841 -0.2860447
62 0.4092230 -0.5752841
63 0.3442531 0.4092230
64 0.3734274 0.3442531
65 -0.5647656 0.3734274
66 0.4207170 -0.5647656
67 -0.4847297 0.4207170
68 0.2839224 -0.4847297
69 -0.7095226 0.2839224
70 -0.5560603 -0.7095226
71 0.2331779 -0.5560603
72 0.2133480 0.2331779
73 -0.7859914 0.2133480
74 -0.5003854 -0.7859914
75 0.5766348 -0.5003854
76 0.2225616 0.5766348
77 0.4567854 0.2225616
78 -0.6669767 0.4567854
79 -0.4772223 -0.6669767
80 -0.7098529 -0.4772223
81 0.2904774 -0.7098529
82 -0.6645288 0.2904774
83 0.3322421 -0.6645288
84 0.4338127 0.3322421
85 -0.6597802 0.4338127
86 0.3532407 -0.6597802
87 0.3854500 0.3532407
88 0.6004156 0.3854500
89 0.2697163 0.6004156
90 0.3846686 0.2697163
91 0.2300300 0.3846686
92 0.4381648 0.2300300
93 0.4215959 0.4381648
94 0.3388401 0.4215959
95 0.4171346 0.3388401
96 0.4668731 0.4171346
97 -0.7144846 0.4668731
98 -0.6783893 -0.7144846
99 0.2277763 -0.6783893
100 -0.5476290 0.2277763
101 0.2045776 -0.5476290
102 -0.4456074 0.2045776
103 0.2624298 -0.4456074
104 -0.3922166 0.2624298
105 0.3090204 -0.3922166
106 -0.6934199 0.3090204
107 -0.4896103 -0.6934199
108 0.5370235 -0.4896103
109 0.2972077 0.5370235
110 0.4162835 0.2972077
111 0.2882507 0.4162835
112 0.2957783 0.2882507
113 -0.6729575 0.2957783
114 0.2488995 -0.6729575
115 0.3447118 0.2488995
116 0.3616693 0.3447118
117 0.3616223 0.3616693
118 -0.6028147 0.3616223
119 0.4259633 -0.6028147
120 0.2893391 0.4259633
121 0.2555712 0.2893391
122 0.3732254 0.2555712
123 0.3329256 0.3732254
124 -0.6316083 0.3329256
125 0.2796477 -0.6316083
126 0.2134965 0.2796477
127 0.3764394 0.2134965
128 -0.5712622 0.3764394
129 -0.4613289 -0.5712622
130 0.3995557 -0.4613289
131 -0.6399936 0.3995557
132 -0.7052442 -0.6399936
133 0.3573132 -0.7052442
134 -0.3466359 0.3573132
135 -0.6345578 -0.3466359
136 0.4586821 -0.6345578
137 0.4141822 0.4586821
138 -0.5817183 0.4141822
139 0.3831227 -0.5817183
140 -0.7261690 0.3831227
141 0.2601369 -0.7261690
142 -0.5112227 0.2601369
143 0.4692014 -0.5112227
144 0.3639965 0.4692014
145 -0.5492880 0.3639965
146 -0.5434051 -0.5492880
147 -0.3779672 -0.5434051
148 0.3504827 -0.3779672
149 0.3946204 0.3504827
150 -0.7123706 0.3946204
151 0.4645727 -0.7123706
152 0.6306164 0.4645727
153 0.5137635 0.6306164
154 0.4118158 0.5137635
155 0.2134965 0.4118158
156 0.4416736 0.2134965
157 0.4291812 0.4416736
158 -0.2849282 0.4291812
159 -0.3483128 -0.2849282
160 -0.5813678 -0.3483128
161 0.5687171 -0.5813678
162 -0.3602499 0.5687171
163 -0.5186197 -0.3602499
164 0.6254346 -0.5186197
165 0.5854105 0.6254346
166 -0.4594473 0.5854105
167 -0.2260814 -0.4594473
168 0.3799532 -0.2260814
169 -0.2779960 0.3799532
170 0.7077673 -0.2779960
171 0.5238562 0.7077673
172 -0.5025055 0.5238562
173 0.7565799 -0.5025055
174 -0.3683598 0.7565799
175 0.4712036 -0.3683598
176 0.6204956 0.4712036
177 0.4608048 0.6204956
178 -0.3077955 0.4608048
179 -0.6694474 -0.3077955
180 -0.4613316 -0.6694474
181 0.4236576 -0.4613316
182 0.4627904 0.4236576
183 -0.4933274 0.4627904
184 0.6226448 -0.4933274
185 -0.3492375 0.6226448
186 -0.3382718 -0.3492375
187 0.5324783 -0.3382718
188 0.4157995 0.5324783
189 0.6088467 0.4157995
190 0.5541997 0.6088467
191 0.4986568 0.5541997
192 -0.4939841 0.4986568
193 0.5737766 -0.4939841
194 0.4225297 0.5737766
195 -0.2630008 0.4225297
196 -0.3305012 -0.2630008
197 -0.5348621 -0.3305012
198 -0.5149626 -0.5348621
199 -0.4534238 -0.5149626
200 0.5074425 -0.4534238
201 0.4320851 0.5074425
202 0.4555901 0.4320851
203 -0.6126560 0.4555901
204 -0.4158830 -0.6126560
205 -0.5378310 -0.4158830
206 0.6629438 -0.5378310
207 -0.6793281 0.6629438
208 -0.4972131 -0.6793281
209 -0.3211477 -0.4972131
210 0.5668051 -0.3211477
211 -0.4284186 0.5668051
212 0.3697880 -0.4284186
213 0.5533763 0.3697880
214 -0.2504690 0.5533763
215 0.3845734 -0.2504690
216 -0.3970489 0.3845734
217 -0.5503082 -0.3970489
218 0.6315425 -0.5503082
219 -0.4894720 0.6315425
220 0.4856829 -0.4894720
221 -0.6425178 0.4856829
222 -0.4095921 -0.6425178
223 -0.2493575 -0.4095921
224 -0.3801649 -0.2493575
225 0.6476654 -0.3801649
226 -0.3811519 0.6476654
227 -0.4670162 -0.3811519
228 0.5384308 -0.4670162
229 0.4812290 0.5384308
230 -0.3199195 0.4812290
231 -0.2456650 -0.3199195
232 -0.3643965 -0.2456650
233 0.6137960 -0.3643965
234 -0.2422578 0.6137960
235 -0.4041284 -0.2422578
236 -0.3090707 -0.4041284
237 -0.2439206 -0.3090707
238 -0.3242094 -0.2439206
239 -0.4011520 -0.3242094
240 -0.4652949 -0.4011520
241 0.5859514 -0.4652949
242 0.7660732 0.5859514
243 0.7132692 0.7660732
244 -0.4824851 0.7132692
245 -0.3691055 -0.4824851
246 0.6375041 -0.3691055
247 0.5838136 0.6375041
248 -0.3772661 0.5838136
249 -0.4219957 -0.3772661
250 -0.3976789 -0.4219957
251 0.7214173 -0.3976789
252 0.4894201 0.7214173
253 0.8574873 0.4894201
254 0.5015786 0.8574873
255 -0.6078605 0.5015786
256 -0.1802604 -0.6078605
257 -0.4759924 -0.1802604
258 -0.2943362 -0.4759924
259 0.6965987 -0.2943362
260 -0.4625942 0.6965987
261 -0.3655346 -0.4625942
262 0.5765510 -0.3655346
263 -0.2021762 0.5765510
264 -0.3720513 -0.2021762
265 0.6597501 -0.3720513
266 0.6071165 0.6597501
267 -0.3769864 0.6071165
268 0.4859536 -0.3769864
269 -0.4787986 0.4859536
270 -0.3550782 -0.4787986
271 0.6340308 -0.3550782
272 -0.4514229 0.6340308
273 0.6093485 -0.4514229
274 -0.4914154 0.6093485
275 -0.2537393 -0.4914154
276 -0.4807601 -0.2537393
277 -0.4668766 -0.4807601
278 0.4389436 -0.4668766
279 -0.3701395 0.4389436
280 -0.4810637 -0.3701395
281 0.4976850 -0.4810637
282 -0.4092810 0.4976850
283 0.7091394 -0.4092810
284 -0.6143735 0.7091394
285 -0.5402358 -0.6143735
286 0.5651652 -0.5402358
287 0.4637621 0.5651652
288 NA 0.4637621
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.3159296 0.3604840
[2,] 0.3547983 0.3159296
[3,] -0.4736663 0.3547983
[4,] 0.2362164 -0.4736663
[5,] 0.3539213 0.2362164
[6,] 0.4389804 0.3539213
[7,] 0.2974757 0.4389804
[8,] 0.3323703 0.2974757
[9,] 0.3456365 0.3323703
[10,] -0.6261915 0.3456365
[11,] 0.2120546 -0.6261915
[12,] -0.4917864 0.2120546
[13,] 0.2909982 -0.4917864
[14,] 0.1215969 0.2909982
[15,] -0.6373944 0.1215969
[16,] -0.5561619 -0.6373944
[17,] 0.2248927 -0.5561619
[18,] -0.6472102 0.2248927
[19,] 0.3100036 -0.6472102
[20,] -0.7532297 0.3100036
[21,] 0.4817437 -0.7532297
[22,] 0.3155792 0.4817437
[23,] 0.3919237 0.3155792
[24,] -0.6707929 0.3919237
[25,] 0.5899899 -0.6707929
[26,] -0.6692112 0.5899899
[27,] 0.2746428 -0.6692112
[28,] 0.3354827 0.2746428
[29,] -0.4861322 0.3354827
[30,] 0.3193685 -0.4861322
[31,] -0.5516228 0.3193685
[32,] 0.3700356 -0.5516228
[33,] 0.2827564 0.3700356
[34,] -0.5746427 0.2827564
[35,] -0.6051726 -0.5746427
[36,] -0.4032595 -0.6051726
[37,] -0.6374060 -0.4032595
[38,] 0.3043189 -0.6374060
[39,] -0.5480990 0.3043189
[40,] -0.5832847 -0.5480990
[41,] 0.3142820 -0.5832847
[42,] -0.7044210 0.3142820
[43,] -0.5112810 -0.7044210
[44,] 0.4888319 -0.5112810
[45,] 0.4774931 0.4888319
[46,] 0.2697468 0.4774931
[47,] 0.3399632 0.2697468
[48,] 0.2349652 0.3399632
[49,] -0.5411123 0.2349652
[50,] 0.1840960 -0.5411123
[51,] -0.5548483 0.1840960
[52,] -0.5328626 -0.5548483
[53,] 0.3486358 -0.5328626
[54,] -0.4213707 0.3486358
[55,] 0.4458008 -0.4213707
[56,] 0.3338273 0.4458008
[57,] 0.2894866 0.3338273
[58,] -0.5607667 0.2894866
[59,] 0.4696590 -0.5607667
[60,] -0.2860447 0.4696590
[61,] -0.5752841 -0.2860447
[62,] 0.4092230 -0.5752841
[63,] 0.3442531 0.4092230
[64,] 0.3734274 0.3442531
[65,] -0.5647656 0.3734274
[66,] 0.4207170 -0.5647656
[67,] -0.4847297 0.4207170
[68,] 0.2839224 -0.4847297
[69,] -0.7095226 0.2839224
[70,] -0.5560603 -0.7095226
[71,] 0.2331779 -0.5560603
[72,] 0.2133480 0.2331779
[73,] -0.7859914 0.2133480
[74,] -0.5003854 -0.7859914
[75,] 0.5766348 -0.5003854
[76,] 0.2225616 0.5766348
[77,] 0.4567854 0.2225616
[78,] -0.6669767 0.4567854
[79,] -0.4772223 -0.6669767
[80,] -0.7098529 -0.4772223
[81,] 0.2904774 -0.7098529
[82,] -0.6645288 0.2904774
[83,] 0.3322421 -0.6645288
[84,] 0.4338127 0.3322421
[85,] -0.6597802 0.4338127
[86,] 0.3532407 -0.6597802
[87,] 0.3854500 0.3532407
[88,] 0.6004156 0.3854500
[89,] 0.2697163 0.6004156
[90,] 0.3846686 0.2697163
[91,] 0.2300300 0.3846686
[92,] 0.4381648 0.2300300
[93,] 0.4215959 0.4381648
[94,] 0.3388401 0.4215959
[95,] 0.4171346 0.3388401
[96,] 0.4668731 0.4171346
[97,] -0.7144846 0.4668731
[98,] -0.6783893 -0.7144846
[99,] 0.2277763 -0.6783893
[100,] -0.5476290 0.2277763
[101,] 0.2045776 -0.5476290
[102,] -0.4456074 0.2045776
[103,] 0.2624298 -0.4456074
[104,] -0.3922166 0.2624298
[105,] 0.3090204 -0.3922166
[106,] -0.6934199 0.3090204
[107,] -0.4896103 -0.6934199
[108,] 0.5370235 -0.4896103
[109,] 0.2972077 0.5370235
[110,] 0.4162835 0.2972077
[111,] 0.2882507 0.4162835
[112,] 0.2957783 0.2882507
[113,] -0.6729575 0.2957783
[114,] 0.2488995 -0.6729575
[115,] 0.3447118 0.2488995
[116,] 0.3616693 0.3447118
[117,] 0.3616223 0.3616693
[118,] -0.6028147 0.3616223
[119,] 0.4259633 -0.6028147
[120,] 0.2893391 0.4259633
[121,] 0.2555712 0.2893391
[122,] 0.3732254 0.2555712
[123,] 0.3329256 0.3732254
[124,] -0.6316083 0.3329256
[125,] 0.2796477 -0.6316083
[126,] 0.2134965 0.2796477
[127,] 0.3764394 0.2134965
[128,] -0.5712622 0.3764394
[129,] -0.4613289 -0.5712622
[130,] 0.3995557 -0.4613289
[131,] -0.6399936 0.3995557
[132,] -0.7052442 -0.6399936
[133,] 0.3573132 -0.7052442
[134,] -0.3466359 0.3573132
[135,] -0.6345578 -0.3466359
[136,] 0.4586821 -0.6345578
[137,] 0.4141822 0.4586821
[138,] -0.5817183 0.4141822
[139,] 0.3831227 -0.5817183
[140,] -0.7261690 0.3831227
[141,] 0.2601369 -0.7261690
[142,] -0.5112227 0.2601369
[143,] 0.4692014 -0.5112227
[144,] 0.3639965 0.4692014
[145,] -0.5492880 0.3639965
[146,] -0.5434051 -0.5492880
[147,] -0.3779672 -0.5434051
[148,] 0.3504827 -0.3779672
[149,] 0.3946204 0.3504827
[150,] -0.7123706 0.3946204
[151,] 0.4645727 -0.7123706
[152,] 0.6306164 0.4645727
[153,] 0.5137635 0.6306164
[154,] 0.4118158 0.5137635
[155,] 0.2134965 0.4118158
[156,] 0.4416736 0.2134965
[157,] 0.4291812 0.4416736
[158,] -0.2849282 0.4291812
[159,] -0.3483128 -0.2849282
[160,] -0.5813678 -0.3483128
[161,] 0.5687171 -0.5813678
[162,] -0.3602499 0.5687171
[163,] -0.5186197 -0.3602499
[164,] 0.6254346 -0.5186197
[165,] 0.5854105 0.6254346
[166,] -0.4594473 0.5854105
[167,] -0.2260814 -0.4594473
[168,] 0.3799532 -0.2260814
[169,] -0.2779960 0.3799532
[170,] 0.7077673 -0.2779960
[171,] 0.5238562 0.7077673
[172,] -0.5025055 0.5238562
[173,] 0.7565799 -0.5025055
[174,] -0.3683598 0.7565799
[175,] 0.4712036 -0.3683598
[176,] 0.6204956 0.4712036
[177,] 0.4608048 0.6204956
[178,] -0.3077955 0.4608048
[179,] -0.6694474 -0.3077955
[180,] -0.4613316 -0.6694474
[181,] 0.4236576 -0.4613316
[182,] 0.4627904 0.4236576
[183,] -0.4933274 0.4627904
[184,] 0.6226448 -0.4933274
[185,] -0.3492375 0.6226448
[186,] -0.3382718 -0.3492375
[187,] 0.5324783 -0.3382718
[188,] 0.4157995 0.5324783
[189,] 0.6088467 0.4157995
[190,] 0.5541997 0.6088467
[191,] 0.4986568 0.5541997
[192,] -0.4939841 0.4986568
[193,] 0.5737766 -0.4939841
[194,] 0.4225297 0.5737766
[195,] -0.2630008 0.4225297
[196,] -0.3305012 -0.2630008
[197,] -0.5348621 -0.3305012
[198,] -0.5149626 -0.5348621
[199,] -0.4534238 -0.5149626
[200,] 0.5074425 -0.4534238
[201,] 0.4320851 0.5074425
[202,] 0.4555901 0.4320851
[203,] -0.6126560 0.4555901
[204,] -0.4158830 -0.6126560
[205,] -0.5378310 -0.4158830
[206,] 0.6629438 -0.5378310
[207,] -0.6793281 0.6629438
[208,] -0.4972131 -0.6793281
[209,] -0.3211477 -0.4972131
[210,] 0.5668051 -0.3211477
[211,] -0.4284186 0.5668051
[212,] 0.3697880 -0.4284186
[213,] 0.5533763 0.3697880
[214,] -0.2504690 0.5533763
[215,] 0.3845734 -0.2504690
[216,] -0.3970489 0.3845734
[217,] -0.5503082 -0.3970489
[218,] 0.6315425 -0.5503082
[219,] -0.4894720 0.6315425
[220,] 0.4856829 -0.4894720
[221,] -0.6425178 0.4856829
[222,] -0.4095921 -0.6425178
[223,] -0.2493575 -0.4095921
[224,] -0.3801649 -0.2493575
[225,] 0.6476654 -0.3801649
[226,] -0.3811519 0.6476654
[227,] -0.4670162 -0.3811519
[228,] 0.5384308 -0.4670162
[229,] 0.4812290 0.5384308
[230,] -0.3199195 0.4812290
[231,] -0.2456650 -0.3199195
[232,] -0.3643965 -0.2456650
[233,] 0.6137960 -0.3643965
[234,] -0.2422578 0.6137960
[235,] -0.4041284 -0.2422578
[236,] -0.3090707 -0.4041284
[237,] -0.2439206 -0.3090707
[238,] -0.3242094 -0.2439206
[239,] -0.4011520 -0.3242094
[240,] -0.4652949 -0.4011520
[241,] 0.5859514 -0.4652949
[242,] 0.7660732 0.5859514
[243,] 0.7132692 0.7660732
[244,] -0.4824851 0.7132692
[245,] -0.3691055 -0.4824851
[246,] 0.6375041 -0.3691055
[247,] 0.5838136 0.6375041
[248,] -0.3772661 0.5838136
[249,] -0.4219957 -0.3772661
[250,] -0.3976789 -0.4219957
[251,] 0.7214173 -0.3976789
[252,] 0.4894201 0.7214173
[253,] 0.8574873 0.4894201
[254,] 0.5015786 0.8574873
[255,] -0.6078605 0.5015786
[256,] -0.1802604 -0.6078605
[257,] -0.4759924 -0.1802604
[258,] -0.2943362 -0.4759924
[259,] 0.6965987 -0.2943362
[260,] -0.4625942 0.6965987
[261,] -0.3655346 -0.4625942
[262,] 0.5765510 -0.3655346
[263,] -0.2021762 0.5765510
[264,] -0.3720513 -0.2021762
[265,] 0.6597501 -0.3720513
[266,] 0.6071165 0.6597501
[267,] -0.3769864 0.6071165
[268,] 0.4859536 -0.3769864
[269,] -0.4787986 0.4859536
[270,] -0.3550782 -0.4787986
[271,] 0.6340308 -0.3550782
[272,] -0.4514229 0.6340308
[273,] 0.6093485 -0.4514229
[274,] -0.4914154 0.6093485
[275,] -0.2537393 -0.4914154
[276,] -0.4807601 -0.2537393
[277,] -0.4668766 -0.4807601
[278,] 0.4389436 -0.4668766
[279,] -0.3701395 0.4389436
[280,] -0.4810637 -0.3701395
[281,] 0.4976850 -0.4810637
[282,] -0.4092810 0.4976850
[283,] 0.7091394 -0.4092810
[284,] -0.6143735 0.7091394
[285,] -0.5402358 -0.6143735
[286,] 0.5651652 -0.5402358
[287,] 0.4637621 0.5651652
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.3159296 0.3604840
2 0.3547983 0.3159296
3 -0.4736663 0.3547983
4 0.2362164 -0.4736663
5 0.3539213 0.2362164
6 0.4389804 0.3539213
7 0.2974757 0.4389804
8 0.3323703 0.2974757
9 0.3456365 0.3323703
10 -0.6261915 0.3456365
11 0.2120546 -0.6261915
12 -0.4917864 0.2120546
13 0.2909982 -0.4917864
14 0.1215969 0.2909982
15 -0.6373944 0.1215969
16 -0.5561619 -0.6373944
17 0.2248927 -0.5561619
18 -0.6472102 0.2248927
19 0.3100036 -0.6472102
20 -0.7532297 0.3100036
21 0.4817437 -0.7532297
22 0.3155792 0.4817437
23 0.3919237 0.3155792
24 -0.6707929 0.3919237
25 0.5899899 -0.6707929
26 -0.6692112 0.5899899
27 0.2746428 -0.6692112
28 0.3354827 0.2746428
29 -0.4861322 0.3354827
30 0.3193685 -0.4861322
31 -0.5516228 0.3193685
32 0.3700356 -0.5516228
33 0.2827564 0.3700356
34 -0.5746427 0.2827564
35 -0.6051726 -0.5746427
36 -0.4032595 -0.6051726
37 -0.6374060 -0.4032595
38 0.3043189 -0.6374060
39 -0.5480990 0.3043189
40 -0.5832847 -0.5480990
41 0.3142820 -0.5832847
42 -0.7044210 0.3142820
43 -0.5112810 -0.7044210
44 0.4888319 -0.5112810
45 0.4774931 0.4888319
46 0.2697468 0.4774931
47 0.3399632 0.2697468
48 0.2349652 0.3399632
49 -0.5411123 0.2349652
50 0.1840960 -0.5411123
51 -0.5548483 0.1840960
52 -0.5328626 -0.5548483
53 0.3486358 -0.5328626
54 -0.4213707 0.3486358
55 0.4458008 -0.4213707
56 0.3338273 0.4458008
57 0.2894866 0.3338273
58 -0.5607667 0.2894866
59 0.4696590 -0.5607667
60 -0.2860447 0.4696590
61 -0.5752841 -0.2860447
62 0.4092230 -0.5752841
63 0.3442531 0.4092230
64 0.3734274 0.3442531
65 -0.5647656 0.3734274
66 0.4207170 -0.5647656
67 -0.4847297 0.4207170
68 0.2839224 -0.4847297
69 -0.7095226 0.2839224
70 -0.5560603 -0.7095226
71 0.2331779 -0.5560603
72 0.2133480 0.2331779
73 -0.7859914 0.2133480
74 -0.5003854 -0.7859914
75 0.5766348 -0.5003854
76 0.2225616 0.5766348
77 0.4567854 0.2225616
78 -0.6669767 0.4567854
79 -0.4772223 -0.6669767
80 -0.7098529 -0.4772223
81 0.2904774 -0.7098529
82 -0.6645288 0.2904774
83 0.3322421 -0.6645288
84 0.4338127 0.3322421
85 -0.6597802 0.4338127
86 0.3532407 -0.6597802
87 0.3854500 0.3532407
88 0.6004156 0.3854500
89 0.2697163 0.6004156
90 0.3846686 0.2697163
91 0.2300300 0.3846686
92 0.4381648 0.2300300
93 0.4215959 0.4381648
94 0.3388401 0.4215959
95 0.4171346 0.3388401
96 0.4668731 0.4171346
97 -0.7144846 0.4668731
98 -0.6783893 -0.7144846
99 0.2277763 -0.6783893
100 -0.5476290 0.2277763
101 0.2045776 -0.5476290
102 -0.4456074 0.2045776
103 0.2624298 -0.4456074
104 -0.3922166 0.2624298
105 0.3090204 -0.3922166
106 -0.6934199 0.3090204
107 -0.4896103 -0.6934199
108 0.5370235 -0.4896103
109 0.2972077 0.5370235
110 0.4162835 0.2972077
111 0.2882507 0.4162835
112 0.2957783 0.2882507
113 -0.6729575 0.2957783
114 0.2488995 -0.6729575
115 0.3447118 0.2488995
116 0.3616693 0.3447118
117 0.3616223 0.3616693
118 -0.6028147 0.3616223
119 0.4259633 -0.6028147
120 0.2893391 0.4259633
121 0.2555712 0.2893391
122 0.3732254 0.2555712
123 0.3329256 0.3732254
124 -0.6316083 0.3329256
125 0.2796477 -0.6316083
126 0.2134965 0.2796477
127 0.3764394 0.2134965
128 -0.5712622 0.3764394
129 -0.4613289 -0.5712622
130 0.3995557 -0.4613289
131 -0.6399936 0.3995557
132 -0.7052442 -0.6399936
133 0.3573132 -0.7052442
134 -0.3466359 0.3573132
135 -0.6345578 -0.3466359
136 0.4586821 -0.6345578
137 0.4141822 0.4586821
138 -0.5817183 0.4141822
139 0.3831227 -0.5817183
140 -0.7261690 0.3831227
141 0.2601369 -0.7261690
142 -0.5112227 0.2601369
143 0.4692014 -0.5112227
144 0.3639965 0.4692014
145 -0.5492880 0.3639965
146 -0.5434051 -0.5492880
147 -0.3779672 -0.5434051
148 0.3504827 -0.3779672
149 0.3946204 0.3504827
150 -0.7123706 0.3946204
151 0.4645727 -0.7123706
152 0.6306164 0.4645727
153 0.5137635 0.6306164
154 0.4118158 0.5137635
155 0.2134965 0.4118158
156 0.4416736 0.2134965
157 0.4291812 0.4416736
158 -0.2849282 0.4291812
159 -0.3483128 -0.2849282
160 -0.5813678 -0.3483128
161 0.5687171 -0.5813678
162 -0.3602499 0.5687171
163 -0.5186197 -0.3602499
164 0.6254346 -0.5186197
165 0.5854105 0.6254346
166 -0.4594473 0.5854105
167 -0.2260814 -0.4594473
168 0.3799532 -0.2260814
169 -0.2779960 0.3799532
170 0.7077673 -0.2779960
171 0.5238562 0.7077673
172 -0.5025055 0.5238562
173 0.7565799 -0.5025055
174 -0.3683598 0.7565799
175 0.4712036 -0.3683598
176 0.6204956 0.4712036
177 0.4608048 0.6204956
178 -0.3077955 0.4608048
179 -0.6694474 -0.3077955
180 -0.4613316 -0.6694474
181 0.4236576 -0.4613316
182 0.4627904 0.4236576
183 -0.4933274 0.4627904
184 0.6226448 -0.4933274
185 -0.3492375 0.6226448
186 -0.3382718 -0.3492375
187 0.5324783 -0.3382718
188 0.4157995 0.5324783
189 0.6088467 0.4157995
190 0.5541997 0.6088467
191 0.4986568 0.5541997
192 -0.4939841 0.4986568
193 0.5737766 -0.4939841
194 0.4225297 0.5737766
195 -0.2630008 0.4225297
196 -0.3305012 -0.2630008
197 -0.5348621 -0.3305012
198 -0.5149626 -0.5348621
199 -0.4534238 -0.5149626
200 0.5074425 -0.4534238
201 0.4320851 0.5074425
202 0.4555901 0.4320851
203 -0.6126560 0.4555901
204 -0.4158830 -0.6126560
205 -0.5378310 -0.4158830
206 0.6629438 -0.5378310
207 -0.6793281 0.6629438
208 -0.4972131 -0.6793281
209 -0.3211477 -0.4972131
210 0.5668051 -0.3211477
211 -0.4284186 0.5668051
212 0.3697880 -0.4284186
213 0.5533763 0.3697880
214 -0.2504690 0.5533763
215 0.3845734 -0.2504690
216 -0.3970489 0.3845734
217 -0.5503082 -0.3970489
218 0.6315425 -0.5503082
219 -0.4894720 0.6315425
220 0.4856829 -0.4894720
221 -0.6425178 0.4856829
222 -0.4095921 -0.6425178
223 -0.2493575 -0.4095921
224 -0.3801649 -0.2493575
225 0.6476654 -0.3801649
226 -0.3811519 0.6476654
227 -0.4670162 -0.3811519
228 0.5384308 -0.4670162
229 0.4812290 0.5384308
230 -0.3199195 0.4812290
231 -0.2456650 -0.3199195
232 -0.3643965 -0.2456650
233 0.6137960 -0.3643965
234 -0.2422578 0.6137960
235 -0.4041284 -0.2422578
236 -0.3090707 -0.4041284
237 -0.2439206 -0.3090707
238 -0.3242094 -0.2439206
239 -0.4011520 -0.3242094
240 -0.4652949 -0.4011520
241 0.5859514 -0.4652949
242 0.7660732 0.5859514
243 0.7132692 0.7660732
244 -0.4824851 0.7132692
245 -0.3691055 -0.4824851
246 0.6375041 -0.3691055
247 0.5838136 0.6375041
248 -0.3772661 0.5838136
249 -0.4219957 -0.3772661
250 -0.3976789 -0.4219957
251 0.7214173 -0.3976789
252 0.4894201 0.7214173
253 0.8574873 0.4894201
254 0.5015786 0.8574873
255 -0.6078605 0.5015786
256 -0.1802604 -0.6078605
257 -0.4759924 -0.1802604
258 -0.2943362 -0.4759924
259 0.6965987 -0.2943362
260 -0.4625942 0.6965987
261 -0.3655346 -0.4625942
262 0.5765510 -0.3655346
263 -0.2021762 0.5765510
264 -0.3720513 -0.2021762
265 0.6597501 -0.3720513
266 0.6071165 0.6597501
267 -0.3769864 0.6071165
268 0.4859536 -0.3769864
269 -0.4787986 0.4859536
270 -0.3550782 -0.4787986
271 0.6340308 -0.3550782
272 -0.4514229 0.6340308
273 0.6093485 -0.4514229
274 -0.4914154 0.6093485
275 -0.2537393 -0.4914154
276 -0.4807601 -0.2537393
277 -0.4668766 -0.4807601
278 0.4389436 -0.4668766
279 -0.3701395 0.4389436
280 -0.4810637 -0.3701395
281 0.4976850 -0.4810637
282 -0.4092810 0.4976850
283 0.7091394 -0.4092810
284 -0.6143735 0.7091394
285 -0.5402358 -0.6143735
286 0.5651652 -0.5402358
287 0.4637621 0.5651652
> 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/78cva1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8cy8s1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9hglk1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10gc751386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11xizc1386667247.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/124pc31386667247.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13ga8p1386667248.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14kcja1386667248.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15sh891386667248.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16kfqv1386667248.tab")
+ }
>
> try(system("convert tmp/1lfrt1386667247.ps tmp/1lfrt1386667247.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fpdq1386667247.ps tmp/2fpdq1386667247.png",intern=TRUE))
character(0)
> try(system("convert tmp/30owg1386667247.ps tmp/30owg1386667247.png",intern=TRUE))
character(0)
> try(system("convert tmp/41ekb1386667247.ps tmp/41ekb1386667247.png",intern=TRUE))
character(0)
> try(system("convert tmp/5aobn1386667247.ps tmp/5aobn1386667247.png",intern=TRUE))
character(0)
> try(system("convert tmp/69pl31386667247.ps tmp/69pl31386667247.png",intern=TRUE))
character(0)
> try(system("convert tmp/78cva1386667247.ps tmp/78cva1386667247.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cy8s1386667247.ps tmp/8cy8s1386667247.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hglk1386667247.ps tmp/9hglk1386667247.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gc751386667247.ps tmp/10gc751386667247.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
22.058 3.840 25.931