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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+ ,36
+ ,11
+ ,9
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+ ,0
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+ ,0
+ ,1
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+ ,35
+ ,15
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+ ,14
+ ,0
+ ,0
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+ ,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
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+ ,0
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+ ,42
+ ,15
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+ ,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 = '6'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '6'
> #'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
Software Pop Gender Connected Separate Learning Happiness
1 12 1 1 41 38 13 14
2 11 1 1 39 32 16 18
3 15 1 1 30 35 19 11
4 6 1 0 31 33 15 12
5 13 1 1 34 37 14 16
6 10 1 1 35 29 13 18
7 12 1 1 39 31 19 14
8 14 1 1 34 36 15 14
9 12 1 1 36 35 14 15
10 9 1 1 37 38 15 15
11 10 1 0 38 31 16 17
12 12 1 1 36 34 16 19
13 12 1 0 38 35 16 10
14 11 1 1 39 38 16 16
15 15 1 1 33 37 17 18
16 12 1 0 32 33 15 14
17 10 1 0 36 32 15 14
18 12 1 1 38 38 20 17
19 11 1 0 39 38 18 14
20 12 1 1 32 32 16 16
21 11 1 0 32 33 16 18
22 12 1 1 31 31 16 11
23 13 1 1 39 38 19 14
24 11 1 1 37 39 16 12
25 12 1 0 39 32 17 17
26 13 1 1 41 32 17 9
27 10 1 0 36 35 16 16
28 14 1 1 33 37 15 14
29 12 1 1 33 33 16 15
30 10 1 0 34 33 14 11
31 12 1 1 31 31 15 16
32 8 1 0 27 32 12 13
33 10 1 1 37 31 14 17
34 12 1 1 34 37 16 15
35 12 1 0 34 30 14 14
36 7 1 0 32 33 10 16
37 9 1 0 29 31 10 9
38 12 1 0 36 33 14 15
39 10 1 1 29 31 16 17
40 10 1 0 35 33 16 13
41 10 1 0 37 32 16 15
42 12 1 1 34 33 14 16
43 15 1 0 38 32 20 16
44 10 1 0 35 33 14 12
45 10 1 1 38 28 14 15
46 12 1 1 37 35 11 11
47 13 1 1 38 39 14 15
48 11 1 1 33 34 15 15
49 11 1 1 36 38 16 17
50 12 1 0 38 32 14 13
51 14 1 1 32 38 16 16
52 10 1 0 32 30 14 14
53 12 1 0 32 33 12 11
54 13 1 1 34 38 16 12
55 5 1 0 32 32 9 12
56 6 1 1 37 35 14 15
57 12 1 1 39 34 16 16
58 12 1 1 29 34 16 15
59 11 1 0 37 36 15 12
60 10 1 1 35 34 16 12
61 7 1 0 30 28 12 8
62 12 1 0 38 34 16 13
63 14 1 1 34 35 16 11
64 11 1 1 31 35 14 14
65 12 1 1 34 31 16 15
66 13 1 0 35 37 17 10
67 14 1 1 36 35 18 11
68 11 1 0 30 27 18 12
69 12 1 1 39 40 12 15
70 12 1 0 35 37 16 15
71 8 1 0 38 36 10 14
72 11 1 1 31 38 14 16
73 14 1 1 34 39 18 15
74 14 1 0 38 41 18 15
75 12 1 0 34 27 16 13
76 9 1 1 39 30 17 12
77 13 1 1 37 37 16 17
78 11 1 1 34 31 16 13
79 12 1 0 28 31 13 15
80 12 1 0 37 27 16 13
81 12 1 0 33 36 16 15
82 12 1 1 35 37 16 15
83 12 1 0 37 33 15 16
84 11 1 1 32 34 15 15
85 10 1 1 33 31 16 14
86 9 1 0 38 39 14 15
87 12 1 1 33 34 16 14
88 12 1 1 29 32 16 13
89 12 1 1 33 33 15 7
90 9 1 1 31 36 12 17
91 15 1 1 36 32 17 13
92 12 1 1 35 41 16 15
93 12 1 1 32 28 15 14
94 12 1 1 29 30 13 13
95 10 1 1 39 36 16 16
96 13 1 1 37 35 16 12
97 9 1 1 35 31 16 14
98 12 1 0 37 34 16 17
99 10 1 0 32 36 14 15
100 14 1 1 38 36 16 17
101 11 1 0 37 35 16 12
102 15 1 1 36 37 20 16
103 11 1 0 32 28 15 11
104 11 1 1 33 39 16 15
105 12 1 0 40 32 13 9
106 12 1 1 38 35 17 16
107 12 1 0 41 39 16 15
108 11 1 0 36 35 16 10
109 7 1 1 43 42 12 10
110 12 1 1 30 34 16 15
111 14 1 1 31 33 16 11
112 11 1 1 32 41 17 13
113 10 1 1 37 34 12 18
114 13 1 0 37 32 18 16
115 13 1 1 33 40 14 14
116 8 1 1 34 40 14 14
117 11 1 1 33 35 13 14
118 12 1 1 38 36 16 14
119 11 1 0 33 37 13 12
120 13 1 1 31 27 16 14
121 12 1 1 38 39 13 15
122 14 1 1 37 38 16 15
123 13 1 1 36 31 15 15
124 15 1 1 31 33 16 13
125 10 1 0 39 32 15 17
126 11 1 1 44 39 17 17
127 9 1 1 33 36 15 19
128 11 1 1 35 33 12 15
129 10 1 0 32 33 16 13
130 11 1 0 28 32 10 9
131 8 1 1 40 37 16 15
132 11 1 0 27 30 12 15
133 12 1 0 37 38 14 15
134 12 1 1 32 29 15 16
135 9 1 0 28 22 13 11
136 11 1 0 34 35 15 14
137 10 1 1 30 35 11 11
138 8 1 1 35 34 12 15
139 9 1 0 31 35 11 13
140 8 1 1 32 34 16 15
141 9 1 0 30 37 15 16
142 15 1 1 30 35 17 14
143 11 1 0 31 23 16 15
144 8 1 1 40 31 10 16
145 13 1 1 32 27 18 16
146 12 1 0 36 36 13 11
147 12 1 0 32 31 16 12
148 9 1 0 35 32 13 9
149 7 1 1 38 39 10 16
150 13 1 1 42 37 15 13
151 9 1 0 34 38 16 16
152 6 1 1 35 39 16 12
153 8 1 1 38 34 14 9
154 8 1 1 33 31 10 13
155 6 1 1 32 37 13 14
156 9 1 1 33 36 15 19
157 11 1 1 34 32 16 13
158 8 1 1 32 38 12 12
159 10 0 0 27 26 13 10
160 8 0 0 31 26 12 14
161 14 0 0 38 33 17 16
162 10 0 1 34 39 15 10
163 8 0 0 24 30 10 11
164 11 0 0 30 33 14 14
165 12 0 1 26 25 11 12
166 12 0 1 34 38 13 9
167 12 0 0 27 37 16 9
168 5 0 0 37 31 12 11
169 12 0 1 36 37 16 16
170 10 0 0 41 35 12 9
171 7 0 1 29 25 9 13
172 12 0 1 36 28 12 16
173 11 0 0 32 35 15 13
174 8 0 1 37 33 12 9
175 9 0 0 30 30 12 12
176 10 0 1 31 31 14 16
177 9 0 1 38 37 12 11
178 12 0 1 36 36 16 14
179 6 0 0 35 30 11 13
180 15 0 0 31 36 19 15
181 12 0 0 38 32 15 14
182 12 0 1 22 28 8 16
183 12 0 1 32 36 16 13
184 11 0 0 36 34 17 14
185 7 0 1 39 31 12 15
186 7 0 0 28 28 11 13
187 5 0 0 32 36 11 11
188 12 0 1 32 36 14 11
189 12 0 1 38 40 16 14
190 3 0 1 32 33 12 15
191 11 0 1 35 37 16 11
192 10 0 1 32 32 13 15
193 12 0 0 37 38 15 12
194 9 0 1 34 31 16 14
195 12 0 1 33 37 16 14
196 9 0 0 33 33 14 8
197 12 0 0 30 30 16 9
198 10 0 0 24 30 14 15
199 9 0 0 34 31 11 17
200 12 0 0 34 32 12 13
201 8 0 1 33 34 15 15
202 11 0 1 34 36 15 15
203 11 0 1 35 37 16 14
204 12 0 0 35 36 16 16
205 10 0 0 36 33 11 13
206 10 0 0 34 33 15 16
207 12 0 1 34 33 12 9
208 12 0 0 41 44 12 16
209 11 0 0 32 39 15 11
210 8 0 0 30 32 15 10
211 12 0 1 35 35 16 11
212 10 0 0 28 25 14 15
213 11 0 1 33 35 17 17
214 10 0 1 39 34 14 14
215 8 0 0 36 35 13 8
216 12 0 1 36 39 15 15
217 12 0 0 35 33 13 11
218 10 0 0 38 36 14 16
219 12 0 1 33 32 15 10
220 9 0 0 31 32 12 15
221 6 0 1 32 36 8 16
222 10 0 0 31 32 14 19
223 9 0 0 33 34 14 12
224 9 0 0 34 33 11 8
225 9 0 0 34 35 12 11
226 6 0 1 34 30 13 14
227 10 0 0 33 38 10 9
228 6 0 0 32 34 16 15
229 14 0 1 41 33 18 13
230 10 0 1 34 32 13 16
231 10 0 0 36 31 11 11
232 6 0 0 37 30 4 12
233 12 0 0 36 27 13 13
234 12 0 1 29 31 16 10
235 7 0 0 37 30 10 11
236 8 0 0 27 32 12 12
237 11 0 0 35 35 12 8
238 3 0 0 28 28 10 12
239 6 0 0 35 33 13 12
240 8 0 0 29 35 12 11
241 9 0 0 32 35 14 13
242 9 0 1 36 32 10 14
243 8 0 1 19 21 12 10
244 9 0 1 21 20 12 12
245 7 0 0 31 34 11 15
246 7 0 0 33 32 10 13
247 6 0 1 36 34 12 13
248 9 0 1 33 32 16 13
249 10 0 0 37 33 12 12
250 11 0 0 34 33 14 12
251 12 0 0 35 37 16 9
252 8 0 1 31 32 14 9
253 11 0 1 37 34 13 15
254 3 0 1 35 30 4 10
255 11 0 1 27 30 15 14
256 12 0 0 34 38 11 15
257 7 0 0 40 36 11 7
258 9 0 0 29 32 14 14
259 12 0 0 38 34 15 8
260 8 0 1 34 33 14 10
261 11 0 0 21 27 13 13
262 8 0 0 36 32 11 13
263 10 0 1 38 34 15 13
264 8 0 0 30 29 11 8
265 7 0 0 35 35 13 12
266 8 0 1 30 27 13 13
267 10 0 1 36 33 16 12
268 8 0 0 34 38 13 10
269 12 0 1 35 36 16 13
270 14 0 0 34 33 16 12
271 7 0 0 32 39 12 9
272 6 0 1 33 29 7 15
273 11 0 0 33 32 16 13
274 4 0 1 26 34 5 13
275 9 0 0 35 38 16 13
276 5 0 0 21 17 4 15
277 9 0 0 38 35 12 15
278 11 0 0 35 32 15 14
279 12 0 1 33 34 14 15
280 9 0 0 37 36 11 11
281 12 0 0 38 31 16 15
282 10 0 1 34 35 15 14
283 9 0 0 27 29 12 13
284 6 0 1 16 22 6 12
285 10 0 0 40 41 16 16
286 9 0 0 36 36 10 16
287 13 0 1 42 42 15 9
288 12 0 1 30 33 14 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop Gender Connected Separate Learning
2.062851 0.452576 0.239170 -0.013256 0.024356 0.547343
Happiness
-0.009087
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1134 -1.0103 0.1771 1.1442 5.0743
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.062851 1.166455 1.768 0.0781 .
Pop 0.452576 0.234148 1.933 0.0543 .
Gender 0.239170 0.219332 1.090 0.2765
Connected -0.013256 0.031048 -0.427 0.6698
Separate 0.024356 0.032249 0.755 0.4507
Learning 0.547343 0.045375 12.063 <2e-16 ***
Happiness -0.009087 0.044820 -0.203 0.8395
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.78 on 281 degrees of freedom
Multiple R-squared: 0.4415, Adjusted R-squared: 0.4296
F-statistic: 37.02 on 6 and 281 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,] 0.728426691 0.54314662 0.27157331
[2,] 0.932111292 0.13577742 0.06788871
[3,] 0.883927529 0.23214494 0.11607247
[4,] 0.907996117 0.18400777 0.09200388
[5,] 0.882918643 0.23416271 0.11708136
[6,] 0.893372417 0.21325517 0.10662758
[7,] 0.901444286 0.19711143 0.09855571
[8,] 0.860478231 0.27904354 0.13952177
[9,] 0.850882362 0.29823528 0.14911764
[10,] 0.800875674 0.39824865 0.19912433
[11,] 0.741526956 0.51694609 0.25847304
[12,] 0.680045375 0.63990925 0.31995463
[13,] 0.611980282 0.77603944 0.38801972
[14,] 0.538947838 0.92210432 0.46105216
[15,] 0.514659703 0.97068059 0.48534030
[16,] 0.513177509 0.97364498 0.48682249
[17,] 0.479991011 0.95998202 0.52000899
[18,] 0.421395820 0.84279164 0.57860418
[19,] 0.425690363 0.85138073 0.57430964
[20,] 0.365084396 0.73016879 0.63491560
[21,] 0.306802041 0.61360408 0.69319796
[22,] 0.254354710 0.50870942 0.74564529
[23,] 0.248959157 0.49791831 0.75104084
[24,] 0.217421179 0.43484236 0.78257882
[25,] 0.178369235 0.35673847 0.82163077
[26,] 0.217867818 0.43573564 0.78213218
[27,] 0.201215206 0.40243041 0.79878479
[28,] 0.165356409 0.33071282 0.83464359
[29,] 0.193459405 0.38691881 0.80654060
[30,] 0.206894026 0.41378805 0.79310597
[31,] 0.177929303 0.35585861 0.82207070
[32,] 0.149026372 0.29805274 0.85097363
[33,] 0.126655282 0.25331056 0.87334472
[34,] 0.183568355 0.36713671 0.81643165
[35,] 0.151176229 0.30235246 0.84882377
[36,] 0.128675511 0.25735102 0.87132449
[37,] 0.137559111 0.27511822 0.86244089
[38,] 0.130673344 0.26134669 0.86932666
[39,] 0.109771478 0.21954296 0.89022852
[40,] 0.098803901 0.19760780 0.90119610
[41,] 0.103123587 0.20624717 0.89687641
[42,] 0.106559722 0.21311944 0.89344028
[43,] 0.086225542 0.17245108 0.91377446
[44,] 0.104472982 0.20894596 0.89552702
[45,] 0.086456044 0.17291209 0.91354396
[46,] 0.147758140 0.29551628 0.85224186
[47,] 0.431409104 0.86281821 0.56859090
[48,] 0.388468979 0.77693796 0.61153102
[49,] 0.346869908 0.69373982 0.65313009
[50,] 0.306952257 0.61390451 0.69304774
[51,] 0.324717126 0.64943425 0.67528287
[52,] 0.340600684 0.68120137 0.65939932
[53,] 0.311522200 0.62304440 0.68847780
[54,] 0.315766119 0.63153224 0.68423388
[55,] 0.279619399 0.55923880 0.72038060
[56,] 0.246153018 0.49230604 0.75384698
[57,] 0.221143290 0.44228658 0.77885671
[58,] 0.197420643 0.39484129 0.80257936
[59,] 0.176192401 0.35238480 0.82380760
[60,] 0.171067476 0.34213495 0.82893252
[61,] 0.148045216 0.29609043 0.85195478
[62,] 0.127234060 0.25446812 0.87276594
[63,] 0.108818488 0.21763698 0.89118151
[64,] 0.093295548 0.18659110 0.90670445
[65,] 0.083399275 0.16679855 0.91660073
[66,] 0.080250178 0.16050036 0.91974982
[67,] 0.117197684 0.23439537 0.88280232
[68,] 0.103749000 0.20749800 0.89625100
[69,] 0.088686409 0.17737282 0.91131359
[70,] 0.102924772 0.20584954 0.89707523
[71,] 0.100437323 0.20087465 0.89956268
[72,] 0.084718555 0.16943711 0.91528145
[73,] 0.071107711 0.14221542 0.92889229
[74,] 0.065514231 0.13102846 0.93448577
[75,] 0.054810475 0.10962095 0.94518952
[76,] 0.053225704 0.10645141 0.94677430
[77,] 0.055466180 0.11093236 0.94453382
[78,] 0.045528133 0.09105627 0.95447187
[79,] 0.037109584 0.07421917 0.96289042
[80,] 0.030630681 0.06126136 0.96936932
[81,] 0.027078672 0.05415734 0.97292133
[82,] 0.038591033 0.07718207 0.96140897
[83,] 0.032498776 0.06499755 0.96750122
[84,] 0.028515413 0.05703083 0.97148459
[85,] 0.029159403 0.05831881 0.97084060
[86,] 0.030352778 0.06070556 0.96964722
[87,] 0.026536311 0.05307262 0.97346369
[88,] 0.035460158 0.07092032 0.96453984
[89,] 0.030176926 0.06035385 0.96982307
[90,] 0.024980877 0.04996175 0.97501912
[91,] 0.028417284 0.05683457 0.97158272
[92,] 0.023308761 0.04661752 0.97669124
[93,] 0.019998776 0.03999755 0.98000122
[94,] 0.016269827 0.03253965 0.98373017
[95,] 0.014915210 0.02983042 0.98508479
[96,] 0.017143104 0.03428621 0.98285690
[97,] 0.013758715 0.02751743 0.98624128
[98,] 0.011007060 0.02201412 0.98899294
[99,] 0.008928292 0.01785658 0.99107171
[100,] 0.016329717 0.03265943 0.98367028
[101,] 0.013087159 0.02617432 0.98691284
[102,] 0.014475063 0.02895013 0.98552494
[103,] 0.015059464 0.03011893 0.98494054
[104,] 0.012193924 0.02438785 0.98780608
[105,] 0.010047724 0.02009545 0.98995228
[106,] 0.010978684 0.02195737 0.98902132
[107,] 0.017447367 0.03489473 0.98255263
[108,] 0.014696337 0.02939267 0.98530366
[109,] 0.011809809 0.02361962 0.98819019
[110,] 0.010184320 0.02036864 0.98981568
[111,] 0.009407333 0.01881467 0.99059267
[112,] 0.009639000 0.01927800 0.99036100
[113,] 0.011297296 0.02259459 0.98870270
[114,] 0.012158688 0.02431738 0.98784131
[115,] 0.021655322 0.04331064 0.97834468
[116,] 0.017859678 0.03571936 0.98214032
[117,] 0.015770055 0.03154011 0.98422994
[118,] 0.017894760 0.03578952 0.98210524
[119,] 0.017349566 0.03469913 0.98265043
[120,] 0.015882242 0.03176448 0.98411776
[121,] 0.021531604 0.04306321 0.97846840
[122,] 0.040160940 0.08032188 0.95983906
[123,] 0.040631181 0.08126236 0.95936882
[124,] 0.041567335 0.08313467 0.95843266
[125,] 0.037682891 0.07536578 0.96231711
[126,] 0.032238578 0.06447716 0.96776142
[127,] 0.026982968 0.05396594 0.97301703
[128,] 0.025336796 0.05067359 0.97466320
[129,] 0.024257359 0.04851472 0.97574264
[130,] 0.020812578 0.04162516 0.97918742
[131,] 0.038789484 0.07757897 0.96121052
[132,] 0.039104915 0.07820983 0.96089509
[133,] 0.056593467 0.11318693 0.94340653
[134,] 0.047649781 0.09529956 0.95235022
[135,] 0.040295736 0.08059147 0.95970426
[136,] 0.035495727 0.07099145 0.96450427
[137,] 0.046036377 0.09207275 0.95396362
[138,] 0.044050552 0.08810110 0.95594945
[139,] 0.039529122 0.07905824 0.96047088
[140,] 0.036791511 0.07358302 0.96320849
[141,] 0.051325762 0.10265152 0.94867424
[142,] 0.052222827 0.10444565 0.94777717
[143,] 0.166016846 0.33203369 0.83398315
[144,] 0.176969114 0.35393823 0.82303089
[145,] 0.166829392 0.33365878 0.83317061
[146,] 0.237590108 0.47518022 0.76240989
[147,] 0.235014057 0.47002811 0.76498594
[148,] 0.213986860 0.42797372 0.78601314
[149,] 0.203038268 0.40607654 0.79696173
[150,] 0.182208974 0.36441795 0.81779103
[151,] 0.164331255 0.32866251 0.83566875
[152,] 0.178340170 0.35668034 0.82165983
[153,] 0.166535898 0.33307180 0.83346410
[154,] 0.146495944 0.29299189 0.85350406
[155,] 0.131699757 0.26339951 0.86830024
[156,] 0.180864718 0.36172944 0.81913528
[157,] 0.184754625 0.36950925 0.81524538
[158,] 0.166763965 0.33352793 0.83323603
[159,] 0.272531411 0.54506282 0.72746859
[160,] 0.246579674 0.49315935 0.75342033
[161,] 0.227264993 0.45452999 0.77273501
[162,] 0.206148438 0.41229688 0.79385156
[163,] 0.244480732 0.48896146 0.75551927
[164,] 0.218676287 0.43735257 0.78132371
[165,] 0.206846189 0.41369238 0.79315381
[166,] 0.183531097 0.36706219 0.81646890
[167,] 0.163016291 0.32603258 0.83698371
[168,] 0.143149062 0.28629812 0.85685094
[169,] 0.126345431 0.25269086 0.87365457
[170,] 0.140624580 0.28124916 0.85937542
[171,] 0.148518312 0.29703662 0.85148169
[172,] 0.140445517 0.28089103 0.85955448
[173,] 0.330391013 0.66078203 0.66960899
[174,] 0.306685119 0.61337024 0.69331488
[175,] 0.282606853 0.56521371 0.71739315
[176,] 0.299879637 0.59975927 0.70012036
[177,] 0.288743014 0.57748603 0.71125699
[178,] 0.378482369 0.75696474 0.62151763
[179,] 0.383746818 0.76749364 0.61625318
[180,] 0.355025911 0.71005182 0.64497409
[181,] 0.719695050 0.56060990 0.28030495
[182,] 0.689307330 0.62138534 0.31069267
[183,] 0.659233787 0.68153243 0.34076621
[184,] 0.644470940 0.71105812 0.35552906
[185,] 0.663296545 0.67340691 0.33670346
[186,] 0.637665068 0.72466986 0.36233493
[187,] 0.613621057 0.77275789 0.38637894
[188,] 0.590738125 0.81852375 0.40926188
[189,] 0.558668743 0.88266251 0.44133126
[190,] 0.525758067 0.94848387 0.47424193
[191,] 0.597638961 0.80472208 0.40236104
[192,] 0.642066927 0.71586615 0.35793307
[193,] 0.607307988 0.78538402 0.39269201
[194,] 0.571295748 0.85740850 0.42870425
[195,] 0.543423273 0.91315345 0.45657673
[196,] 0.534025902 0.93194820 0.46597410
[197,] 0.498789093 0.99757819 0.50121091
[198,] 0.567149306 0.86570139 0.43285069
[199,] 0.626169596 0.74766081 0.37383040
[200,] 0.595775077 0.80844985 0.40422492
[201,] 0.626797661 0.74640468 0.37320234
[202,] 0.597860726 0.80427855 0.40213927
[203,] 0.559449706 0.88110059 0.44055029
[204,] 0.525777027 0.94844595 0.47422297
[205,] 0.486822653 0.97364531 0.51317735
[206,] 0.475073244 0.95014649 0.52492676
[207,] 0.463329927 0.92665985 0.53667007
[208,] 0.501002315 0.99799537 0.49899768
[209,] 0.460515567 0.92103113 0.53948443
[210,] 0.448083061 0.89616612 0.55191694
[211,] 0.408872582 0.81774516 0.59112742
[212,] 0.375727179 0.75145436 0.62427282
[213,] 0.337751750 0.67550350 0.66224825
[214,] 0.309783300 0.61956660 0.69021670
[215,] 0.278692799 0.55738560 0.72130720
[216,] 0.245135587 0.49027117 0.75486441
[217,] 0.348811289 0.69762258 0.65118871
[218,] 0.394249563 0.78849913 0.60575044
[219,] 0.698373734 0.60325253 0.30162627
[220,] 0.680000357 0.63999929 0.31999964
[221,] 0.640145694 0.71970861 0.35985431
[222,] 0.634317602 0.73136480 0.36568240
[223,] 0.631447483 0.73710503 0.36855252
[224,] 0.672882503 0.65423499 0.32711750
[225,] 0.648688012 0.70262398 0.35131199
[226,] 0.608816825 0.78236635 0.39118317
[227,] 0.570738534 0.85852293 0.42926147
[228,] 0.637305774 0.72538845 0.36269423
[229,] 0.851833287 0.29633343 0.14816671
[230,] 0.922131016 0.15573797 0.07786898
[231,] 0.905562826 0.18887435 0.09443717
[232,] 0.893475460 0.21304908 0.10652454
[233,] 0.884291041 0.23141792 0.11570896
[234,] 0.860671442 0.27865712 0.13932856
[235,] 0.830883729 0.33823254 0.16911627
[236,] 0.832544721 0.33491056 0.16745528
[237,] 0.808498231 0.38300354 0.19150177
[238,] 0.862589238 0.27482152 0.13741076
[239,] 0.883531141 0.23293772 0.11646886
[240,] 0.866265834 0.26746833 0.13373417
[241,] 0.843303151 0.31339370 0.15669685
[242,] 0.827808695 0.34438261 0.17219131
[243,] 0.821910790 0.35617842 0.17808921
[244,] 0.801542567 0.39691487 0.19845743
[245,] 0.770086330 0.45982734 0.22991367
[246,] 0.722155187 0.55568963 0.27784481
[247,] 0.875956133 0.24808773 0.12404387
[248,] 0.849459041 0.30108192 0.15054096
[249,] 0.826345905 0.34730819 0.17365409
[250,] 0.831923224 0.33615355 0.16807678
[251,] 0.848636175 0.30272765 0.15136382
[252,] 0.845162515 0.30967497 0.15483749
[253,] 0.801396043 0.39720791 0.19860396
[254,] 0.778310490 0.44337902 0.22168951
[255,] 0.719715416 0.56056917 0.28028458
[256,] 0.772381155 0.45523769 0.22761885
[257,] 0.839922674 0.32015465 0.16007733
[258,] 0.926917810 0.14616438 0.07308219
[259,] 0.906671518 0.18665696 0.09332848
[260,] 0.865007780 0.26998444 0.13499222
[261,] 0.926659219 0.14668156 0.07334078
[262,] 0.904538175 0.19092365 0.09546182
[263,] 0.943421373 0.11315725 0.05657863
[264,] 0.902042734 0.19591453 0.09795727
[265,] 0.903071709 0.19385658 0.09692829
[266,] 0.889933918 0.22013216 0.11006608
[267,] 0.811762000 0.37647600 0.18823800
[268,] 0.719977651 0.56004470 0.28002235
[269,] 0.555516236 0.88896753 0.44448376
> postscript(file="/var/fisher/rcomp/tmp/11ysk1386451469.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/27t1f1386451469.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3fn3i1386451469.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4iptx1386451469.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5m1uz1386451469.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
1.8750970296 -0.6109594616 1.4910331280 -5.0093684057 2.2774947977
6 7 8 9 10
0.0511172658 -1.2649813138 2.7363336933 1.3436313701 -2.2635251254
11 12 13 14 15
-1.3697759301 0.3096484152 0.4691907408 -0.7752705341 2.6403827848
16 17 18 19 20
1.0220610089 -0.9005605867 -1.9688129209 -1.6489614692 0.2780778194
21 22 23 24 25
-0.4889347055 0.2437438105 -0.4354747239 -0.8624855646 0.0717799772
26 27 28 29 30
0.7864258107 -1.5027987692 2.6987219411 0.2578902368 -0.4313452434
31 32 33 34 35
0.8365219121 -1.3869171493 -0.5275144095 0.1737209818 1.6689841567
36 37 38 39 40
-1.2230479173 0.7222893100 1.6315135858 -1.7282456995 -1.4946027128
41 42 43 44 45
-1.4255615461 1.3749196035 1.4074071685 -0.4090027607 -0.4593641183
46 47 48 49 50
2.9625695193 2.2727176659 -0.2191225226 -0.8059502544 1.6642070249
51 52 53 54 55
2.1319406108 -0.3575269448 2.6368305392 1.1221039846 -2.6876960015
56 57 58 59 60
-4.6431130791 0.3221542717 0.1805118324 -0.0029037056 -1.7672156589
61 62 63 64 65
-2.2951603509 0.5208077380 2.1860856570 0.2682666845 0.3198581904
66 67 68 69 70
0.8333682437 1.1179098744 -1.5185170738 2.3563038992 0.4261463452
71 72 73 74 75
-0.2347570810 0.2133719440 1.0303216949 1.2738013077 0.6382789451
76 77 78 79 80
-3.1641120922 1.2316614978 -0.6983156734 2.1215250247 0.6780455973
81 82 83 84 85
0.4239914452 0.1869765325 1.1065126264 -0.2323780734 -1.7024842923
86 87 88 89 90
-1.4881125214 0.2244471034 0.2110503714 0.7325382235 -0.6341418372
91 92 93 94 95
2.7564957846 0.0895517267 0.9046722033 1.9017931003 -1.7265581312
96 97 98 99 100
1.2349392411 -2.6759731908 0.5438999149 -0.4945772215 2.2692732500
101 102 103 104 105
-0.5258909461 1.0199452471 0.1165812203 -0.8882469719 2.2017138407
106 107 108 109 110
-0.2628009225 0.4569672468 -0.5573203607 -2.6848209603 0.1937673831
111 112 113 114 115
2.1950314076 -1.5157322313 0.5031908021 0.4888385018 2.1729967788
116 117 118 119 120
-2.8137476704 0.8421212280 0.2420124543 1.0144047740 1.3684294120
121 122 123 124 125
1.8200611079 2.1891314325 1.8937127339 3.2132052715 -0.8335331388
126 127 128 129 130
-1.2716054919 -2.2314871979 1.4737751063 -1.5343693651 2.6846775578
131 132 133 134 135
-3.7467457138 1.6799691174 1.5229881293 0.8984898657 -0.6956168900
136 137 138 139 140
-0.0001402925 0.8697806641 -1.5505810951 0.1403798914 -3.7797215154
141 142 143 144 145
-2.0837010345 2.6129808078 -0.2858890376 -0.3074609212 0.3051719426
146 147 148 149 150
2.0694406958 0.5052561059 -0.8645639130 -1.5288216342 1.8089349659
151 152 153 154 155
-2.6023784750 -5.8889966661 -2.6600229184 -0.4275105721 -4.2198467256
156 157 158 159 160
-2.2314871979 -0.7226718749 -1.7150333489 0.6371918584 -0.7260947690
161 162 163 164 165
2.4776573299 -0.9205066019 0.1511176584 0.9954693862 3.5219834442
166 167 168 169 170
2.1894495517 0.7181563846 -3.7956032675 0.6618950520 1.1418202659
171 172 173 174 175
-0.3344760876 3.0704746329 0.4168377109 -1.1016593469 0.1450510107
176 177 178 179 180
-0.1635586091 -0.1676547381 0.6680773896 -2.2322408617 2.2080260545
181 182 183 184 185
1.5785265515 5.0742706906 0.6059682547 -0.5913838368 -1.9719142511
186 187 188 189 190
-1.2763173140 -3.4363185864 1.6824812749 0.5971636853 -6.1134155091
191 192 193 194 195
-0.3967951584 0.3635972503 1.4009599283 -2.2366527047 0.6039545359
196 197 198 199 200
-1.0192855531 0.9284164469 -0.0019083820 0.7664951138 3.1584477427
201 202 203 204 205
-2.7665464859 0.1979966620 -0.3695343626 0.9121655154 1.7079460847
206 207 208 209 210
-0.4806779890 2.8585740009 2.9862229764 0.3012390413 -2.5638655820
211 212 213 214 215
0.6519172445 0.1728948281 -0.8674157075 -0.1487566713 -1.4808878617
216 217 218 219 220
1.1514391592 2.5818297862 0.0466190516 1.2367312575 0.1368549543
221 222 223 224 225
-0.9880234135 0.0785157979 -1.0072940269 0.6360003237 0.0672052745
226 227 228 229 230
-3.5702661773 2.0573941397 -5.0879756659 1.7036499317 0.3991952837
231 232 233 234 235
1.7384846238 1.6165874020 2.7593964094 0.6607218139 -0.6765601820
236 237 238 239 240
-0.9434280444 2.0532000295 -4.7380608039 -3.4090832819 -0.9990724792
241 242 243 244 245
-1.0358188471 1.0495628474 -1.0388979111 0.0301432557 -1.3645140066
246 247 248 249 250
-0.7601209240 -3.1029233714 -2.2833513888 1.1647712616 1.0303177253
251 252 253 254 255
0.8242007905 -2.2515233339 1.3811626011 -1.6672673761 0.2422580835
256 257 258 259 260
3.5778278399 -1.3666219081 -0.9934299631 1.4752925572 -2.2270259512
261 262 263 264 265
1.5605631482 -0.2676977138 -0.7184425960 -0.3195970735 -2.4577956848
266 267 268 269 270
-1.5593067078 -1.2770278699 -1.5622937037 0.6457349069 2.9356308413
271 272 273 274 275
-2.0749044965 -0.2660179426 -0.0441815761 -1.4040747848 -2.1638076832
276 277 278 279 280
0.7483900046 0.1565752052 0.5387598993 1.7807969562 0.6299591673
281 282 283 284 285
1.0646262429 -0.7867340685 0.1387274918 0.1992137512 -1.1433377381
286 287 288
1.2094817182 2.1033822678 1.7562995735
> postscript(file="/var/fisher/rcomp/tmp/6pvrt1386451469.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 1.8750970296 NA
1 -0.6109594616 1.8750970296
2 1.4910331280 -0.6109594616
3 -5.0093684057 1.4910331280
4 2.2774947977 -5.0093684057
5 0.0511172658 2.2774947977
6 -1.2649813138 0.0511172658
7 2.7363336933 -1.2649813138
8 1.3436313701 2.7363336933
9 -2.2635251254 1.3436313701
10 -1.3697759301 -2.2635251254
11 0.3096484152 -1.3697759301
12 0.4691907408 0.3096484152
13 -0.7752705341 0.4691907408
14 2.6403827848 -0.7752705341
15 1.0220610089 2.6403827848
16 -0.9005605867 1.0220610089
17 -1.9688129209 -0.9005605867
18 -1.6489614692 -1.9688129209
19 0.2780778194 -1.6489614692
20 -0.4889347055 0.2780778194
21 0.2437438105 -0.4889347055
22 -0.4354747239 0.2437438105
23 -0.8624855646 -0.4354747239
24 0.0717799772 -0.8624855646
25 0.7864258107 0.0717799772
26 -1.5027987692 0.7864258107
27 2.6987219411 -1.5027987692
28 0.2578902368 2.6987219411
29 -0.4313452434 0.2578902368
30 0.8365219121 -0.4313452434
31 -1.3869171493 0.8365219121
32 -0.5275144095 -1.3869171493
33 0.1737209818 -0.5275144095
34 1.6689841567 0.1737209818
35 -1.2230479173 1.6689841567
36 0.7222893100 -1.2230479173
37 1.6315135858 0.7222893100
38 -1.7282456995 1.6315135858
39 -1.4946027128 -1.7282456995
40 -1.4255615461 -1.4946027128
41 1.3749196035 -1.4255615461
42 1.4074071685 1.3749196035
43 -0.4090027607 1.4074071685
44 -0.4593641183 -0.4090027607
45 2.9625695193 -0.4593641183
46 2.2727176659 2.9625695193
47 -0.2191225226 2.2727176659
48 -0.8059502544 -0.2191225226
49 1.6642070249 -0.8059502544
50 2.1319406108 1.6642070249
51 -0.3575269448 2.1319406108
52 2.6368305392 -0.3575269448
53 1.1221039846 2.6368305392
54 -2.6876960015 1.1221039846
55 -4.6431130791 -2.6876960015
56 0.3221542717 -4.6431130791
57 0.1805118324 0.3221542717
58 -0.0029037056 0.1805118324
59 -1.7672156589 -0.0029037056
60 -2.2951603509 -1.7672156589
61 0.5208077380 -2.2951603509
62 2.1860856570 0.5208077380
63 0.2682666845 2.1860856570
64 0.3198581904 0.2682666845
65 0.8333682437 0.3198581904
66 1.1179098744 0.8333682437
67 -1.5185170738 1.1179098744
68 2.3563038992 -1.5185170738
69 0.4261463452 2.3563038992
70 -0.2347570810 0.4261463452
71 0.2133719440 -0.2347570810
72 1.0303216949 0.2133719440
73 1.2738013077 1.0303216949
74 0.6382789451 1.2738013077
75 -3.1641120922 0.6382789451
76 1.2316614978 -3.1641120922
77 -0.6983156734 1.2316614978
78 2.1215250247 -0.6983156734
79 0.6780455973 2.1215250247
80 0.4239914452 0.6780455973
81 0.1869765325 0.4239914452
82 1.1065126264 0.1869765325
83 -0.2323780734 1.1065126264
84 -1.7024842923 -0.2323780734
85 -1.4881125214 -1.7024842923
86 0.2244471034 -1.4881125214
87 0.2110503714 0.2244471034
88 0.7325382235 0.2110503714
89 -0.6341418372 0.7325382235
90 2.7564957846 -0.6341418372
91 0.0895517267 2.7564957846
92 0.9046722033 0.0895517267
93 1.9017931003 0.9046722033
94 -1.7265581312 1.9017931003
95 1.2349392411 -1.7265581312
96 -2.6759731908 1.2349392411
97 0.5438999149 -2.6759731908
98 -0.4945772215 0.5438999149
99 2.2692732500 -0.4945772215
100 -0.5258909461 2.2692732500
101 1.0199452471 -0.5258909461
102 0.1165812203 1.0199452471
103 -0.8882469719 0.1165812203
104 2.2017138407 -0.8882469719
105 -0.2628009225 2.2017138407
106 0.4569672468 -0.2628009225
107 -0.5573203607 0.4569672468
108 -2.6848209603 -0.5573203607
109 0.1937673831 -2.6848209603
110 2.1950314076 0.1937673831
111 -1.5157322313 2.1950314076
112 0.5031908021 -1.5157322313
113 0.4888385018 0.5031908021
114 2.1729967788 0.4888385018
115 -2.8137476704 2.1729967788
116 0.8421212280 -2.8137476704
117 0.2420124543 0.8421212280
118 1.0144047740 0.2420124543
119 1.3684294120 1.0144047740
120 1.8200611079 1.3684294120
121 2.1891314325 1.8200611079
122 1.8937127339 2.1891314325
123 3.2132052715 1.8937127339
124 -0.8335331388 3.2132052715
125 -1.2716054919 -0.8335331388
126 -2.2314871979 -1.2716054919
127 1.4737751063 -2.2314871979
128 -1.5343693651 1.4737751063
129 2.6846775578 -1.5343693651
130 -3.7467457138 2.6846775578
131 1.6799691174 -3.7467457138
132 1.5229881293 1.6799691174
133 0.8984898657 1.5229881293
134 -0.6956168900 0.8984898657
135 -0.0001402925 -0.6956168900
136 0.8697806641 -0.0001402925
137 -1.5505810951 0.8697806641
138 0.1403798914 -1.5505810951
139 -3.7797215154 0.1403798914
140 -2.0837010345 -3.7797215154
141 2.6129808078 -2.0837010345
142 -0.2858890376 2.6129808078
143 -0.3074609212 -0.2858890376
144 0.3051719426 -0.3074609212
145 2.0694406958 0.3051719426
146 0.5052561059 2.0694406958
147 -0.8645639130 0.5052561059
148 -1.5288216342 -0.8645639130
149 1.8089349659 -1.5288216342
150 -2.6023784750 1.8089349659
151 -5.8889966661 -2.6023784750
152 -2.6600229184 -5.8889966661
153 -0.4275105721 -2.6600229184
154 -4.2198467256 -0.4275105721
155 -2.2314871979 -4.2198467256
156 -0.7226718749 -2.2314871979
157 -1.7150333489 -0.7226718749
158 0.6371918584 -1.7150333489
159 -0.7260947690 0.6371918584
160 2.4776573299 -0.7260947690
161 -0.9205066019 2.4776573299
162 0.1511176584 -0.9205066019
163 0.9954693862 0.1511176584
164 3.5219834442 0.9954693862
165 2.1894495517 3.5219834442
166 0.7181563846 2.1894495517
167 -3.7956032675 0.7181563846
168 0.6618950520 -3.7956032675
169 1.1418202659 0.6618950520
170 -0.3344760876 1.1418202659
171 3.0704746329 -0.3344760876
172 0.4168377109 3.0704746329
173 -1.1016593469 0.4168377109
174 0.1450510107 -1.1016593469
175 -0.1635586091 0.1450510107
176 -0.1676547381 -0.1635586091
177 0.6680773896 -0.1676547381
178 -2.2322408617 0.6680773896
179 2.2080260545 -2.2322408617
180 1.5785265515 2.2080260545
181 5.0742706906 1.5785265515
182 0.6059682547 5.0742706906
183 -0.5913838368 0.6059682547
184 -1.9719142511 -0.5913838368
185 -1.2763173140 -1.9719142511
186 -3.4363185864 -1.2763173140
187 1.6824812749 -3.4363185864
188 0.5971636853 1.6824812749
189 -6.1134155091 0.5971636853
190 -0.3967951584 -6.1134155091
191 0.3635972503 -0.3967951584
192 1.4009599283 0.3635972503
193 -2.2366527047 1.4009599283
194 0.6039545359 -2.2366527047
195 -1.0192855531 0.6039545359
196 0.9284164469 -1.0192855531
197 -0.0019083820 0.9284164469
198 0.7664951138 -0.0019083820
199 3.1584477427 0.7664951138
200 -2.7665464859 3.1584477427
201 0.1979966620 -2.7665464859
202 -0.3695343626 0.1979966620
203 0.9121655154 -0.3695343626
204 1.7079460847 0.9121655154
205 -0.4806779890 1.7079460847
206 2.8585740009 -0.4806779890
207 2.9862229764 2.8585740009
208 0.3012390413 2.9862229764
209 -2.5638655820 0.3012390413
210 0.6519172445 -2.5638655820
211 0.1728948281 0.6519172445
212 -0.8674157075 0.1728948281
213 -0.1487566713 -0.8674157075
214 -1.4808878617 -0.1487566713
215 1.1514391592 -1.4808878617
216 2.5818297862 1.1514391592
217 0.0466190516 2.5818297862
218 1.2367312575 0.0466190516
219 0.1368549543 1.2367312575
220 -0.9880234135 0.1368549543
221 0.0785157979 -0.9880234135
222 -1.0072940269 0.0785157979
223 0.6360003237 -1.0072940269
224 0.0672052745 0.6360003237
225 -3.5702661773 0.0672052745
226 2.0573941397 -3.5702661773
227 -5.0879756659 2.0573941397
228 1.7036499317 -5.0879756659
229 0.3991952837 1.7036499317
230 1.7384846238 0.3991952837
231 1.6165874020 1.7384846238
232 2.7593964094 1.6165874020
233 0.6607218139 2.7593964094
234 -0.6765601820 0.6607218139
235 -0.9434280444 -0.6765601820
236 2.0532000295 -0.9434280444
237 -4.7380608039 2.0532000295
238 -3.4090832819 -4.7380608039
239 -0.9990724792 -3.4090832819
240 -1.0358188471 -0.9990724792
241 1.0495628474 -1.0358188471
242 -1.0388979111 1.0495628474
243 0.0301432557 -1.0388979111
244 -1.3645140066 0.0301432557
245 -0.7601209240 -1.3645140066
246 -3.1029233714 -0.7601209240
247 -2.2833513888 -3.1029233714
248 1.1647712616 -2.2833513888
249 1.0303177253 1.1647712616
250 0.8242007905 1.0303177253
251 -2.2515233339 0.8242007905
252 1.3811626011 -2.2515233339
253 -1.6672673761 1.3811626011
254 0.2422580835 -1.6672673761
255 3.5778278399 0.2422580835
256 -1.3666219081 3.5778278399
257 -0.9934299631 -1.3666219081
258 1.4752925572 -0.9934299631
259 -2.2270259512 1.4752925572
260 1.5605631482 -2.2270259512
261 -0.2676977138 1.5605631482
262 -0.7184425960 -0.2676977138
263 -0.3195970735 -0.7184425960
264 -2.4577956848 -0.3195970735
265 -1.5593067078 -2.4577956848
266 -1.2770278699 -1.5593067078
267 -1.5622937037 -1.2770278699
268 0.6457349069 -1.5622937037
269 2.9356308413 0.6457349069
270 -2.0749044965 2.9356308413
271 -0.2660179426 -2.0749044965
272 -0.0441815761 -0.2660179426
273 -1.4040747848 -0.0441815761
274 -2.1638076832 -1.4040747848
275 0.7483900046 -2.1638076832
276 0.1565752052 0.7483900046
277 0.5387598993 0.1565752052
278 1.7807969562 0.5387598993
279 0.6299591673 1.7807969562
280 1.0646262429 0.6299591673
281 -0.7867340685 1.0646262429
282 0.1387274918 -0.7867340685
283 0.1992137512 0.1387274918
284 -1.1433377381 0.1992137512
285 1.2094817182 -1.1433377381
286 2.1033822678 1.2094817182
287 1.7562995735 2.1033822678
288 NA 1.7562995735
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.6109594616 1.8750970296
[2,] 1.4910331280 -0.6109594616
[3,] -5.0093684057 1.4910331280
[4,] 2.2774947977 -5.0093684057
[5,] 0.0511172658 2.2774947977
[6,] -1.2649813138 0.0511172658
[7,] 2.7363336933 -1.2649813138
[8,] 1.3436313701 2.7363336933
[9,] -2.2635251254 1.3436313701
[10,] -1.3697759301 -2.2635251254
[11,] 0.3096484152 -1.3697759301
[12,] 0.4691907408 0.3096484152
[13,] -0.7752705341 0.4691907408
[14,] 2.6403827848 -0.7752705341
[15,] 1.0220610089 2.6403827848
[16,] -0.9005605867 1.0220610089
[17,] -1.9688129209 -0.9005605867
[18,] -1.6489614692 -1.9688129209
[19,] 0.2780778194 -1.6489614692
[20,] -0.4889347055 0.2780778194
[21,] 0.2437438105 -0.4889347055
[22,] -0.4354747239 0.2437438105
[23,] -0.8624855646 -0.4354747239
[24,] 0.0717799772 -0.8624855646
[25,] 0.7864258107 0.0717799772
[26,] -1.5027987692 0.7864258107
[27,] 2.6987219411 -1.5027987692
[28,] 0.2578902368 2.6987219411
[29,] -0.4313452434 0.2578902368
[30,] 0.8365219121 -0.4313452434
[31,] -1.3869171493 0.8365219121
[32,] -0.5275144095 -1.3869171493
[33,] 0.1737209818 -0.5275144095
[34,] 1.6689841567 0.1737209818
[35,] -1.2230479173 1.6689841567
[36,] 0.7222893100 -1.2230479173
[37,] 1.6315135858 0.7222893100
[38,] -1.7282456995 1.6315135858
[39,] -1.4946027128 -1.7282456995
[40,] -1.4255615461 -1.4946027128
[41,] 1.3749196035 -1.4255615461
[42,] 1.4074071685 1.3749196035
[43,] -0.4090027607 1.4074071685
[44,] -0.4593641183 -0.4090027607
[45,] 2.9625695193 -0.4593641183
[46,] 2.2727176659 2.9625695193
[47,] -0.2191225226 2.2727176659
[48,] -0.8059502544 -0.2191225226
[49,] 1.6642070249 -0.8059502544
[50,] 2.1319406108 1.6642070249
[51,] -0.3575269448 2.1319406108
[52,] 2.6368305392 -0.3575269448
[53,] 1.1221039846 2.6368305392
[54,] -2.6876960015 1.1221039846
[55,] -4.6431130791 -2.6876960015
[56,] 0.3221542717 -4.6431130791
[57,] 0.1805118324 0.3221542717
[58,] -0.0029037056 0.1805118324
[59,] -1.7672156589 -0.0029037056
[60,] -2.2951603509 -1.7672156589
[61,] 0.5208077380 -2.2951603509
[62,] 2.1860856570 0.5208077380
[63,] 0.2682666845 2.1860856570
[64,] 0.3198581904 0.2682666845
[65,] 0.8333682437 0.3198581904
[66,] 1.1179098744 0.8333682437
[67,] -1.5185170738 1.1179098744
[68,] 2.3563038992 -1.5185170738
[69,] 0.4261463452 2.3563038992
[70,] -0.2347570810 0.4261463452
[71,] 0.2133719440 -0.2347570810
[72,] 1.0303216949 0.2133719440
[73,] 1.2738013077 1.0303216949
[74,] 0.6382789451 1.2738013077
[75,] -3.1641120922 0.6382789451
[76,] 1.2316614978 -3.1641120922
[77,] -0.6983156734 1.2316614978
[78,] 2.1215250247 -0.6983156734
[79,] 0.6780455973 2.1215250247
[80,] 0.4239914452 0.6780455973
[81,] 0.1869765325 0.4239914452
[82,] 1.1065126264 0.1869765325
[83,] -0.2323780734 1.1065126264
[84,] -1.7024842923 -0.2323780734
[85,] -1.4881125214 -1.7024842923
[86,] 0.2244471034 -1.4881125214
[87,] 0.2110503714 0.2244471034
[88,] 0.7325382235 0.2110503714
[89,] -0.6341418372 0.7325382235
[90,] 2.7564957846 -0.6341418372
[91,] 0.0895517267 2.7564957846
[92,] 0.9046722033 0.0895517267
[93,] 1.9017931003 0.9046722033
[94,] -1.7265581312 1.9017931003
[95,] 1.2349392411 -1.7265581312
[96,] -2.6759731908 1.2349392411
[97,] 0.5438999149 -2.6759731908
[98,] -0.4945772215 0.5438999149
[99,] 2.2692732500 -0.4945772215
[100,] -0.5258909461 2.2692732500
[101,] 1.0199452471 -0.5258909461
[102,] 0.1165812203 1.0199452471
[103,] -0.8882469719 0.1165812203
[104,] 2.2017138407 -0.8882469719
[105,] -0.2628009225 2.2017138407
[106,] 0.4569672468 -0.2628009225
[107,] -0.5573203607 0.4569672468
[108,] -2.6848209603 -0.5573203607
[109,] 0.1937673831 -2.6848209603
[110,] 2.1950314076 0.1937673831
[111,] -1.5157322313 2.1950314076
[112,] 0.5031908021 -1.5157322313
[113,] 0.4888385018 0.5031908021
[114,] 2.1729967788 0.4888385018
[115,] -2.8137476704 2.1729967788
[116,] 0.8421212280 -2.8137476704
[117,] 0.2420124543 0.8421212280
[118,] 1.0144047740 0.2420124543
[119,] 1.3684294120 1.0144047740
[120,] 1.8200611079 1.3684294120
[121,] 2.1891314325 1.8200611079
[122,] 1.8937127339 2.1891314325
[123,] 3.2132052715 1.8937127339
[124,] -0.8335331388 3.2132052715
[125,] -1.2716054919 -0.8335331388
[126,] -2.2314871979 -1.2716054919
[127,] 1.4737751063 -2.2314871979
[128,] -1.5343693651 1.4737751063
[129,] 2.6846775578 -1.5343693651
[130,] -3.7467457138 2.6846775578
[131,] 1.6799691174 -3.7467457138
[132,] 1.5229881293 1.6799691174
[133,] 0.8984898657 1.5229881293
[134,] -0.6956168900 0.8984898657
[135,] -0.0001402925 -0.6956168900
[136,] 0.8697806641 -0.0001402925
[137,] -1.5505810951 0.8697806641
[138,] 0.1403798914 -1.5505810951
[139,] -3.7797215154 0.1403798914
[140,] -2.0837010345 -3.7797215154
[141,] 2.6129808078 -2.0837010345
[142,] -0.2858890376 2.6129808078
[143,] -0.3074609212 -0.2858890376
[144,] 0.3051719426 -0.3074609212
[145,] 2.0694406958 0.3051719426
[146,] 0.5052561059 2.0694406958
[147,] -0.8645639130 0.5052561059
[148,] -1.5288216342 -0.8645639130
[149,] 1.8089349659 -1.5288216342
[150,] -2.6023784750 1.8089349659
[151,] -5.8889966661 -2.6023784750
[152,] -2.6600229184 -5.8889966661
[153,] -0.4275105721 -2.6600229184
[154,] -4.2198467256 -0.4275105721
[155,] -2.2314871979 -4.2198467256
[156,] -0.7226718749 -2.2314871979
[157,] -1.7150333489 -0.7226718749
[158,] 0.6371918584 -1.7150333489
[159,] -0.7260947690 0.6371918584
[160,] 2.4776573299 -0.7260947690
[161,] -0.9205066019 2.4776573299
[162,] 0.1511176584 -0.9205066019
[163,] 0.9954693862 0.1511176584
[164,] 3.5219834442 0.9954693862
[165,] 2.1894495517 3.5219834442
[166,] 0.7181563846 2.1894495517
[167,] -3.7956032675 0.7181563846
[168,] 0.6618950520 -3.7956032675
[169,] 1.1418202659 0.6618950520
[170,] -0.3344760876 1.1418202659
[171,] 3.0704746329 -0.3344760876
[172,] 0.4168377109 3.0704746329
[173,] -1.1016593469 0.4168377109
[174,] 0.1450510107 -1.1016593469
[175,] -0.1635586091 0.1450510107
[176,] -0.1676547381 -0.1635586091
[177,] 0.6680773896 -0.1676547381
[178,] -2.2322408617 0.6680773896
[179,] 2.2080260545 -2.2322408617
[180,] 1.5785265515 2.2080260545
[181,] 5.0742706906 1.5785265515
[182,] 0.6059682547 5.0742706906
[183,] -0.5913838368 0.6059682547
[184,] -1.9719142511 -0.5913838368
[185,] -1.2763173140 -1.9719142511
[186,] -3.4363185864 -1.2763173140
[187,] 1.6824812749 -3.4363185864
[188,] 0.5971636853 1.6824812749
[189,] -6.1134155091 0.5971636853
[190,] -0.3967951584 -6.1134155091
[191,] 0.3635972503 -0.3967951584
[192,] 1.4009599283 0.3635972503
[193,] -2.2366527047 1.4009599283
[194,] 0.6039545359 -2.2366527047
[195,] -1.0192855531 0.6039545359
[196,] 0.9284164469 -1.0192855531
[197,] -0.0019083820 0.9284164469
[198,] 0.7664951138 -0.0019083820
[199,] 3.1584477427 0.7664951138
[200,] -2.7665464859 3.1584477427
[201,] 0.1979966620 -2.7665464859
[202,] -0.3695343626 0.1979966620
[203,] 0.9121655154 -0.3695343626
[204,] 1.7079460847 0.9121655154
[205,] -0.4806779890 1.7079460847
[206,] 2.8585740009 -0.4806779890
[207,] 2.9862229764 2.8585740009
[208,] 0.3012390413 2.9862229764
[209,] -2.5638655820 0.3012390413
[210,] 0.6519172445 -2.5638655820
[211,] 0.1728948281 0.6519172445
[212,] -0.8674157075 0.1728948281
[213,] -0.1487566713 -0.8674157075
[214,] -1.4808878617 -0.1487566713
[215,] 1.1514391592 -1.4808878617
[216,] 2.5818297862 1.1514391592
[217,] 0.0466190516 2.5818297862
[218,] 1.2367312575 0.0466190516
[219,] 0.1368549543 1.2367312575
[220,] -0.9880234135 0.1368549543
[221,] 0.0785157979 -0.9880234135
[222,] -1.0072940269 0.0785157979
[223,] 0.6360003237 -1.0072940269
[224,] 0.0672052745 0.6360003237
[225,] -3.5702661773 0.0672052745
[226,] 2.0573941397 -3.5702661773
[227,] -5.0879756659 2.0573941397
[228,] 1.7036499317 -5.0879756659
[229,] 0.3991952837 1.7036499317
[230,] 1.7384846238 0.3991952837
[231,] 1.6165874020 1.7384846238
[232,] 2.7593964094 1.6165874020
[233,] 0.6607218139 2.7593964094
[234,] -0.6765601820 0.6607218139
[235,] -0.9434280444 -0.6765601820
[236,] 2.0532000295 -0.9434280444
[237,] -4.7380608039 2.0532000295
[238,] -3.4090832819 -4.7380608039
[239,] -0.9990724792 -3.4090832819
[240,] -1.0358188471 -0.9990724792
[241,] 1.0495628474 -1.0358188471
[242,] -1.0388979111 1.0495628474
[243,] 0.0301432557 -1.0388979111
[244,] -1.3645140066 0.0301432557
[245,] -0.7601209240 -1.3645140066
[246,] -3.1029233714 -0.7601209240
[247,] -2.2833513888 -3.1029233714
[248,] 1.1647712616 -2.2833513888
[249,] 1.0303177253 1.1647712616
[250,] 0.8242007905 1.0303177253
[251,] -2.2515233339 0.8242007905
[252,] 1.3811626011 -2.2515233339
[253,] -1.6672673761 1.3811626011
[254,] 0.2422580835 -1.6672673761
[255,] 3.5778278399 0.2422580835
[256,] -1.3666219081 3.5778278399
[257,] -0.9934299631 -1.3666219081
[258,] 1.4752925572 -0.9934299631
[259,] -2.2270259512 1.4752925572
[260,] 1.5605631482 -2.2270259512
[261,] -0.2676977138 1.5605631482
[262,] -0.7184425960 -0.2676977138
[263,] -0.3195970735 -0.7184425960
[264,] -2.4577956848 -0.3195970735
[265,] -1.5593067078 -2.4577956848
[266,] -1.2770278699 -1.5593067078
[267,] -1.5622937037 -1.2770278699
[268,] 0.6457349069 -1.5622937037
[269,] 2.9356308413 0.6457349069
[270,] -2.0749044965 2.9356308413
[271,] -0.2660179426 -2.0749044965
[272,] -0.0441815761 -0.2660179426
[273,] -1.4040747848 -0.0441815761
[274,] -2.1638076832 -1.4040747848
[275,] 0.7483900046 -2.1638076832
[276,] 0.1565752052 0.7483900046
[277,] 0.5387598993 0.1565752052
[278,] 1.7807969562 0.5387598993
[279,] 0.6299591673 1.7807969562
[280,] 1.0646262429 0.6299591673
[281,] -0.7867340685 1.0646262429
[282,] 0.1387274918 -0.7867340685
[283,] 0.1992137512 0.1387274918
[284,] -1.1433377381 0.1992137512
[285,] 1.2094817182 -1.1433377381
[286,] 2.1033822678 1.2094817182
[287,] 1.7562995735 2.1033822678
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.6109594616 1.8750970296
2 1.4910331280 -0.6109594616
3 -5.0093684057 1.4910331280
4 2.2774947977 -5.0093684057
5 0.0511172658 2.2774947977
6 -1.2649813138 0.0511172658
7 2.7363336933 -1.2649813138
8 1.3436313701 2.7363336933
9 -2.2635251254 1.3436313701
10 -1.3697759301 -2.2635251254
11 0.3096484152 -1.3697759301
12 0.4691907408 0.3096484152
13 -0.7752705341 0.4691907408
14 2.6403827848 -0.7752705341
15 1.0220610089 2.6403827848
16 -0.9005605867 1.0220610089
17 -1.9688129209 -0.9005605867
18 -1.6489614692 -1.9688129209
19 0.2780778194 -1.6489614692
20 -0.4889347055 0.2780778194
21 0.2437438105 -0.4889347055
22 -0.4354747239 0.2437438105
23 -0.8624855646 -0.4354747239
24 0.0717799772 -0.8624855646
25 0.7864258107 0.0717799772
26 -1.5027987692 0.7864258107
27 2.6987219411 -1.5027987692
28 0.2578902368 2.6987219411
29 -0.4313452434 0.2578902368
30 0.8365219121 -0.4313452434
31 -1.3869171493 0.8365219121
32 -0.5275144095 -1.3869171493
33 0.1737209818 -0.5275144095
34 1.6689841567 0.1737209818
35 -1.2230479173 1.6689841567
36 0.7222893100 -1.2230479173
37 1.6315135858 0.7222893100
38 -1.7282456995 1.6315135858
39 -1.4946027128 -1.7282456995
40 -1.4255615461 -1.4946027128
41 1.3749196035 -1.4255615461
42 1.4074071685 1.3749196035
43 -0.4090027607 1.4074071685
44 -0.4593641183 -0.4090027607
45 2.9625695193 -0.4593641183
46 2.2727176659 2.9625695193
47 -0.2191225226 2.2727176659
48 -0.8059502544 -0.2191225226
49 1.6642070249 -0.8059502544
50 2.1319406108 1.6642070249
51 -0.3575269448 2.1319406108
52 2.6368305392 -0.3575269448
53 1.1221039846 2.6368305392
54 -2.6876960015 1.1221039846
55 -4.6431130791 -2.6876960015
56 0.3221542717 -4.6431130791
57 0.1805118324 0.3221542717
58 -0.0029037056 0.1805118324
59 -1.7672156589 -0.0029037056
60 -2.2951603509 -1.7672156589
61 0.5208077380 -2.2951603509
62 2.1860856570 0.5208077380
63 0.2682666845 2.1860856570
64 0.3198581904 0.2682666845
65 0.8333682437 0.3198581904
66 1.1179098744 0.8333682437
67 -1.5185170738 1.1179098744
68 2.3563038992 -1.5185170738
69 0.4261463452 2.3563038992
70 -0.2347570810 0.4261463452
71 0.2133719440 -0.2347570810
72 1.0303216949 0.2133719440
73 1.2738013077 1.0303216949
74 0.6382789451 1.2738013077
75 -3.1641120922 0.6382789451
76 1.2316614978 -3.1641120922
77 -0.6983156734 1.2316614978
78 2.1215250247 -0.6983156734
79 0.6780455973 2.1215250247
80 0.4239914452 0.6780455973
81 0.1869765325 0.4239914452
82 1.1065126264 0.1869765325
83 -0.2323780734 1.1065126264
84 -1.7024842923 -0.2323780734
85 -1.4881125214 -1.7024842923
86 0.2244471034 -1.4881125214
87 0.2110503714 0.2244471034
88 0.7325382235 0.2110503714
89 -0.6341418372 0.7325382235
90 2.7564957846 -0.6341418372
91 0.0895517267 2.7564957846
92 0.9046722033 0.0895517267
93 1.9017931003 0.9046722033
94 -1.7265581312 1.9017931003
95 1.2349392411 -1.7265581312
96 -2.6759731908 1.2349392411
97 0.5438999149 -2.6759731908
98 -0.4945772215 0.5438999149
99 2.2692732500 -0.4945772215
100 -0.5258909461 2.2692732500
101 1.0199452471 -0.5258909461
102 0.1165812203 1.0199452471
103 -0.8882469719 0.1165812203
104 2.2017138407 -0.8882469719
105 -0.2628009225 2.2017138407
106 0.4569672468 -0.2628009225
107 -0.5573203607 0.4569672468
108 -2.6848209603 -0.5573203607
109 0.1937673831 -2.6848209603
110 2.1950314076 0.1937673831
111 -1.5157322313 2.1950314076
112 0.5031908021 -1.5157322313
113 0.4888385018 0.5031908021
114 2.1729967788 0.4888385018
115 -2.8137476704 2.1729967788
116 0.8421212280 -2.8137476704
117 0.2420124543 0.8421212280
118 1.0144047740 0.2420124543
119 1.3684294120 1.0144047740
120 1.8200611079 1.3684294120
121 2.1891314325 1.8200611079
122 1.8937127339 2.1891314325
123 3.2132052715 1.8937127339
124 -0.8335331388 3.2132052715
125 -1.2716054919 -0.8335331388
126 -2.2314871979 -1.2716054919
127 1.4737751063 -2.2314871979
128 -1.5343693651 1.4737751063
129 2.6846775578 -1.5343693651
130 -3.7467457138 2.6846775578
131 1.6799691174 -3.7467457138
132 1.5229881293 1.6799691174
133 0.8984898657 1.5229881293
134 -0.6956168900 0.8984898657
135 -0.0001402925 -0.6956168900
136 0.8697806641 -0.0001402925
137 -1.5505810951 0.8697806641
138 0.1403798914 -1.5505810951
139 -3.7797215154 0.1403798914
140 -2.0837010345 -3.7797215154
141 2.6129808078 -2.0837010345
142 -0.2858890376 2.6129808078
143 -0.3074609212 -0.2858890376
144 0.3051719426 -0.3074609212
145 2.0694406958 0.3051719426
146 0.5052561059 2.0694406958
147 -0.8645639130 0.5052561059
148 -1.5288216342 -0.8645639130
149 1.8089349659 -1.5288216342
150 -2.6023784750 1.8089349659
151 -5.8889966661 -2.6023784750
152 -2.6600229184 -5.8889966661
153 -0.4275105721 -2.6600229184
154 -4.2198467256 -0.4275105721
155 -2.2314871979 -4.2198467256
156 -0.7226718749 -2.2314871979
157 -1.7150333489 -0.7226718749
158 0.6371918584 -1.7150333489
159 -0.7260947690 0.6371918584
160 2.4776573299 -0.7260947690
161 -0.9205066019 2.4776573299
162 0.1511176584 -0.9205066019
163 0.9954693862 0.1511176584
164 3.5219834442 0.9954693862
165 2.1894495517 3.5219834442
166 0.7181563846 2.1894495517
167 -3.7956032675 0.7181563846
168 0.6618950520 -3.7956032675
169 1.1418202659 0.6618950520
170 -0.3344760876 1.1418202659
171 3.0704746329 -0.3344760876
172 0.4168377109 3.0704746329
173 -1.1016593469 0.4168377109
174 0.1450510107 -1.1016593469
175 -0.1635586091 0.1450510107
176 -0.1676547381 -0.1635586091
177 0.6680773896 -0.1676547381
178 -2.2322408617 0.6680773896
179 2.2080260545 -2.2322408617
180 1.5785265515 2.2080260545
181 5.0742706906 1.5785265515
182 0.6059682547 5.0742706906
183 -0.5913838368 0.6059682547
184 -1.9719142511 -0.5913838368
185 -1.2763173140 -1.9719142511
186 -3.4363185864 -1.2763173140
187 1.6824812749 -3.4363185864
188 0.5971636853 1.6824812749
189 -6.1134155091 0.5971636853
190 -0.3967951584 -6.1134155091
191 0.3635972503 -0.3967951584
192 1.4009599283 0.3635972503
193 -2.2366527047 1.4009599283
194 0.6039545359 -2.2366527047
195 -1.0192855531 0.6039545359
196 0.9284164469 -1.0192855531
197 -0.0019083820 0.9284164469
198 0.7664951138 -0.0019083820
199 3.1584477427 0.7664951138
200 -2.7665464859 3.1584477427
201 0.1979966620 -2.7665464859
202 -0.3695343626 0.1979966620
203 0.9121655154 -0.3695343626
204 1.7079460847 0.9121655154
205 -0.4806779890 1.7079460847
206 2.8585740009 -0.4806779890
207 2.9862229764 2.8585740009
208 0.3012390413 2.9862229764
209 -2.5638655820 0.3012390413
210 0.6519172445 -2.5638655820
211 0.1728948281 0.6519172445
212 -0.8674157075 0.1728948281
213 -0.1487566713 -0.8674157075
214 -1.4808878617 -0.1487566713
215 1.1514391592 -1.4808878617
216 2.5818297862 1.1514391592
217 0.0466190516 2.5818297862
218 1.2367312575 0.0466190516
219 0.1368549543 1.2367312575
220 -0.9880234135 0.1368549543
221 0.0785157979 -0.9880234135
222 -1.0072940269 0.0785157979
223 0.6360003237 -1.0072940269
224 0.0672052745 0.6360003237
225 -3.5702661773 0.0672052745
226 2.0573941397 -3.5702661773
227 -5.0879756659 2.0573941397
228 1.7036499317 -5.0879756659
229 0.3991952837 1.7036499317
230 1.7384846238 0.3991952837
231 1.6165874020 1.7384846238
232 2.7593964094 1.6165874020
233 0.6607218139 2.7593964094
234 -0.6765601820 0.6607218139
235 -0.9434280444 -0.6765601820
236 2.0532000295 -0.9434280444
237 -4.7380608039 2.0532000295
238 -3.4090832819 -4.7380608039
239 -0.9990724792 -3.4090832819
240 -1.0358188471 -0.9990724792
241 1.0495628474 -1.0358188471
242 -1.0388979111 1.0495628474
243 0.0301432557 -1.0388979111
244 -1.3645140066 0.0301432557
245 -0.7601209240 -1.3645140066
246 -3.1029233714 -0.7601209240
247 -2.2833513888 -3.1029233714
248 1.1647712616 -2.2833513888
249 1.0303177253 1.1647712616
250 0.8242007905 1.0303177253
251 -2.2515233339 0.8242007905
252 1.3811626011 -2.2515233339
253 -1.6672673761 1.3811626011
254 0.2422580835 -1.6672673761
255 3.5778278399 0.2422580835
256 -1.3666219081 3.5778278399
257 -0.9934299631 -1.3666219081
258 1.4752925572 -0.9934299631
259 -2.2270259512 1.4752925572
260 1.5605631482 -2.2270259512
261 -0.2676977138 1.5605631482
262 -0.7184425960 -0.2676977138
263 -0.3195970735 -0.7184425960
264 -2.4577956848 -0.3195970735
265 -1.5593067078 -2.4577956848
266 -1.2770278699 -1.5593067078
267 -1.5622937037 -1.2770278699
268 0.6457349069 -1.5622937037
269 2.9356308413 0.6457349069
270 -2.0749044965 2.9356308413
271 -0.2660179426 -2.0749044965
272 -0.0441815761 -0.2660179426
273 -1.4040747848 -0.0441815761
274 -2.1638076832 -1.4040747848
275 0.7483900046 -2.1638076832
276 0.1565752052 0.7483900046
277 0.5387598993 0.1565752052
278 1.7807969562 0.5387598993
279 0.6299591673 1.7807969562
280 1.0646262429 0.6299591673
281 -0.7867340685 1.0646262429
282 0.1387274918 -0.7867340685
283 0.1992137512 0.1387274918
284 -1.1433377381 0.1992137512
285 1.2094817182 -1.1433377381
286 2.1033822678 1.2094817182
287 1.7562995735 2.1033822678
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/7njus1386451469.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8gjuh1386451469.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/90cwf1386451469.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/102gkz1386451469.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11o4rc1386451469.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/128xis1386451469.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/132agb1386451469.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/142jhq1386451469.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15z9p31386451469.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/164p2v1386451470.tab")
+ }
>
> try(system("convert tmp/11ysk1386451469.ps tmp/11ysk1386451469.png",intern=TRUE))
character(0)
> try(system("convert tmp/27t1f1386451469.ps tmp/27t1f1386451469.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fn3i1386451469.ps tmp/3fn3i1386451469.png",intern=TRUE))
character(0)
> try(system("convert tmp/4iptx1386451469.ps tmp/4iptx1386451469.png",intern=TRUE))
character(0)
> try(system("convert tmp/5m1uz1386451469.ps tmp/5m1uz1386451469.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pvrt1386451469.ps tmp/6pvrt1386451469.png",intern=TRUE))
character(0)
> try(system("convert tmp/7njus1386451469.ps tmp/7njus1386451469.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gjuh1386451469.ps tmp/8gjuh1386451469.png",intern=TRUE))
character(0)
> try(system("convert tmp/90cwf1386451469.ps tmp/90cwf1386451469.png",intern=TRUE))
character(0)
> try(system("convert tmp/102gkz1386451469.ps tmp/102gkz1386451469.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
21.351 3.290 24.668