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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+ ,9
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+ ,1
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+ ,35
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+ ,14
+ ,0
+ ,0
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+ ,29
+ ,12
+ ,9
+ ,13
+ ,0
+ ,1
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+ ,22
+ ,6
+ ,6
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+ ,0
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+ ,41
+ ,16
+ ,10
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+ ,0
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+ ,36
+ ,10
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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])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = 'no'
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> 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
Learning Pop Gender Connected Separate Software Happiness
1 13 1 1 41 38 12 14
2 16 1 1 39 32 11 18
3 19 1 1 30 35 15 11
4 15 1 0 31 33 6 12
5 14 1 1 34 37 13 16
6 13 1 1 35 29 10 18
7 19 1 1 39 31 12 14
8 15 1 1 34 36 14 14
9 14 1 1 36 35 12 15
10 15 1 1 37 38 9 15
11 16 1 0 38 31 10 17
12 16 1 1 36 34 12 19
13 16 1 0 38 35 12 10
14 16 1 1 39 38 11 16
15 17 1 1 33 37 15 18
16 15 1 0 32 33 12 14
17 15 1 0 36 32 10 14
18 20 1 1 38 38 12 17
19 18 1 0 39 38 11 14
20 16 1 1 32 32 12 16
21 16 1 0 32 33 11 18
22 16 1 1 31 31 12 11
23 19 1 1 39 38 13 14
24 16 1 1 37 39 11 12
25 17 1 0 39 32 12 17
26 17 1 1 41 32 13 9
27 16 1 0 36 35 10 16
28 15 1 1 33 37 14 14
29 16 1 1 33 33 12 15
30 14 1 0 34 33 10 11
31 15 1 1 31 31 12 16
32 12 1 0 27 32 8 13
33 14 1 1 37 31 10 17
34 16 1 1 34 37 12 15
35 14 1 0 34 30 12 14
36 10 1 0 32 33 7 16
37 10 1 0 29 31 9 9
38 14 1 0 36 33 12 15
39 16 1 1 29 31 10 17
40 16 1 0 35 33 10 13
41 16 1 0 37 32 10 15
42 14 1 1 34 33 12 16
43 20 1 0 38 32 15 16
44 14 1 0 35 33 10 12
45 14 1 1 38 28 10 15
46 11 1 1 37 35 12 11
47 14 1 1 38 39 13 15
48 15 1 1 33 34 11 15
49 16 1 1 36 38 11 17
50 14 1 0 38 32 12 13
51 16 1 1 32 38 14 16
52 14 1 0 32 30 10 14
53 12 1 0 32 33 12 11
54 16 1 1 34 38 13 12
55 9 1 0 32 32 5 12
56 14 1 1 37 35 6 15
57 16 1 1 39 34 12 16
58 16 1 1 29 34 12 15
59 15 1 0 37 36 11 12
60 16 1 1 35 34 10 12
61 12 1 0 30 28 7 8
62 16 1 0 38 34 12 13
63 16 1 1 34 35 14 11
64 14 1 1 31 35 11 14
65 16 1 1 34 31 12 15
66 17 1 0 35 37 13 10
67 18 1 1 36 35 14 11
68 18 1 0 30 27 11 12
69 12 1 1 39 40 12 15
70 16 1 0 35 37 12 15
71 10 1 0 38 36 8 14
72 14 1 1 31 38 11 16
73 18 1 1 34 39 14 15
74 18 1 0 38 41 14 15
75 16 1 0 34 27 12 13
76 17 1 1 39 30 9 12
77 16 1 1 37 37 13 17
78 16 1 1 34 31 11 13
79 13 1 0 28 31 12 15
80 16 1 0 37 27 12 13
81 16 1 0 33 36 12 15
82 16 1 1 35 37 12 15
83 15 1 0 37 33 12 16
84 15 1 1 32 34 11 15
85 16 1 1 33 31 10 14
86 14 1 0 38 39 9 15
87 16 1 1 33 34 12 14
88 16 1 1 29 32 12 13
89 15 1 1 33 33 12 7
90 12 1 1 31 36 9 17
91 17 1 1 36 32 15 13
92 16 1 1 35 41 12 15
93 15 1 1 32 28 12 14
94 13 1 1 29 30 12 13
95 16 1 1 39 36 10 16
96 16 1 1 37 35 13 12
97 16 1 1 35 31 9 14
98 16 1 0 37 34 12 17
99 14 1 0 32 36 10 15
100 16 1 1 38 36 14 17
101 16 1 0 37 35 11 12
102 20 1 1 36 37 15 16
103 15 1 0 32 28 11 11
104 16 1 1 33 39 11 15
105 13 1 0 40 32 12 9
106 17 1 1 38 35 12 16
107 16 1 0 41 39 12 15
108 16 1 0 36 35 11 10
109 12 1 1 43 42 7 10
110 16 1 1 30 34 12 15
111 16 1 1 31 33 14 11
112 17 1 1 32 41 11 13
113 12 1 1 37 34 10 18
114 18 1 0 37 32 13 16
115 14 1 1 33 40 13 14
116 14 1 1 34 40 8 14
117 13 1 1 33 35 11 14
118 16 1 1 38 36 12 14
119 13 1 0 33 37 11 12
120 16 1 1 31 27 13 14
121 13 1 1 38 39 12 15
122 16 1 1 37 38 14 15
123 15 1 1 36 31 13 15
124 16 1 1 31 33 15 13
125 15 1 0 39 32 10 17
126 17 1 1 44 39 11 17
127 15 1 1 33 36 9 19
128 12 1 1 35 33 11 15
129 16 1 0 32 33 10 13
130 10 1 0 28 32 11 9
131 16 1 1 40 37 8 15
132 12 1 0 27 30 11 15
133 14 1 0 37 38 12 15
134 15 1 1 32 29 12 16
135 13 1 0 28 22 9 11
136 15 1 0 34 35 11 14
137 11 1 1 30 35 10 11
138 12 1 1 35 34 8 15
139 11 1 0 31 35 9 13
140 16 1 1 32 34 8 15
141 15 1 0 30 37 9 16
142 17 1 1 30 35 15 14
143 16 1 0 31 23 11 15
144 10 1 1 40 31 8 16
145 18 1 1 32 27 13 16
146 13 1 0 36 36 12 11
147 16 1 0 32 31 12 12
148 13 1 0 35 32 9 9
149 10 1 1 38 39 7 16
150 15 1 1 42 37 13 13
151 16 1 0 34 38 9 16
152 16 1 1 35 39 6 12
153 14 1 1 38 34 8 9
154 10 1 1 33 31 8 13
155 13 1 1 32 37 6 14
156 15 1 1 33 36 9 19
157 16 1 1 34 32 11 13
158 12 1 1 32 38 8 12
159 13 0 0 27 26 10 10
160 12 0 0 31 26 8 14
161 17 0 0 38 33 14 16
162 15 0 1 34 39 10 10
163 10 0 0 24 30 8 11
164 14 0 0 30 33 11 14
165 11 0 1 26 25 12 12
166 13 0 1 34 38 12 9
167 16 0 0 27 37 12 9
168 12 0 0 37 31 5 11
169 16 0 1 36 37 12 16
170 12 0 0 41 35 10 9
171 9 0 1 29 25 7 13
172 12 0 1 36 28 12 16
173 15 0 0 32 35 11 13
174 12 0 1 37 33 8 9
175 12 0 0 30 30 9 12
176 14 0 1 31 31 10 16
177 12 0 1 38 37 9 11
178 16 0 1 36 36 12 14
179 11 0 0 35 30 6 13
180 19 0 0 31 36 15 15
181 15 0 0 38 32 12 14
182 8 0 1 22 28 12 16
183 16 0 1 32 36 12 13
184 17 0 0 36 34 11 14
185 12 0 1 39 31 7 15
186 11 0 0 28 28 7 13
187 11 0 0 32 36 5 11
188 14 0 1 32 36 12 11
189 16 0 1 38 40 12 14
190 12 0 1 32 33 3 15
191 16 0 1 35 37 11 11
192 13 0 1 32 32 10 15
193 15 0 0 37 38 12 12
194 16 0 1 34 31 9 14
195 16 0 1 33 37 12 14
196 14 0 0 33 33 9 8
197 16 0 0 30 30 12 9
198 14 0 0 24 30 10 15
199 11 0 0 34 31 9 17
200 12 0 0 34 32 12 13
201 15 0 1 33 34 8 15
202 15 0 1 34 36 11 15
203 16 0 1 35 37 11 14
204 16 0 0 35 36 12 16
205 11 0 0 36 33 10 13
206 15 0 0 34 33 10 16
207 12 0 1 34 33 12 9
208 12 0 0 41 44 12 16
209 15 0 0 32 39 11 11
210 15 0 0 30 32 8 10
211 16 0 1 35 35 12 11
212 14 0 0 28 25 10 15
213 17 0 1 33 35 11 17
214 14 0 1 39 34 10 14
215 13 0 0 36 35 8 8
216 15 0 1 36 39 12 15
217 13 0 0 35 33 12 11
218 14 0 0 38 36 10 16
219 15 0 1 33 32 12 10
220 12 0 0 31 32 9 15
221 8 0 1 32 36 6 16
222 14 0 0 31 32 10 19
223 14 0 0 33 34 9 12
224 11 0 0 34 33 9 8
225 12 0 0 34 35 9 11
226 13 0 1 34 30 6 14
227 10 0 0 33 38 10 9
228 16 0 0 32 34 6 15
229 18 0 1 41 33 14 13
230 13 0 1 34 32 10 16
231 11 0 0 36 31 10 11
232 4 0 0 37 30 6 12
233 13 0 0 36 27 12 13
234 16 0 1 29 31 12 10
235 10 0 0 37 30 7 11
236 12 0 0 27 32 8 12
237 12 0 0 35 35 11 8
238 10 0 0 28 28 3 12
239 13 0 0 35 33 6 12
240 12 0 0 29 35 8 11
241 14 0 0 32 35 9 13
242 10 0 1 36 32 9 14
243 12 0 1 19 21 8 10
244 12 0 1 21 20 9 12
245 11 0 0 31 34 7 15
246 10 0 0 33 32 7 13
247 12 0 1 36 34 6 13
248 16 0 1 33 32 9 13
249 12 0 0 37 33 10 12
250 14 0 0 34 33 11 12
251 16 0 0 35 37 12 9
252 14 0 1 31 32 8 9
253 13 0 1 37 34 11 15
254 4 0 1 35 30 3 10
255 15 0 1 27 30 11 14
256 11 0 0 34 38 12 15
257 11 0 0 40 36 7 7
258 14 0 0 29 32 9 14
259 15 0 0 38 34 12 8
260 14 0 1 34 33 8 10
261 13 0 0 21 27 11 13
262 11 0 0 36 32 8 13
263 15 0 1 38 34 10 13
264 11 0 0 30 29 8 8
265 13 0 0 35 35 7 12
266 13 0 1 30 27 8 13
267 16 0 1 36 33 10 12
268 13 0 0 34 38 8 10
269 16 0 1 35 36 12 13
270 16 0 0 34 33 14 12
271 12 0 0 32 39 7 9
272 7 0 1 33 29 6 15
273 16 0 0 33 32 11 13
274 5 0 1 26 34 4 13
275 16 0 0 35 38 9 13
276 4 0 0 21 17 5 15
277 12 0 0 38 35 9 15
278 15 0 0 35 32 11 14
279 14 0 1 33 34 12 15
280 11 0 0 37 36 9 11
281 16 0 0 38 31 12 15
282 15 0 1 34 35 10 14
283 12 0 0 27 29 9 13
284 6 0 1 16 22 6 12
285 16 0 0 40 41 10 16
286 10 0 0 36 36 9 16
287 15 0 1 42 42 13 9
288 14 0 1 30 33 12 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop Gender Connected Separate Software
1.94933 0.65349 0.03052 0.07000 0.05639 0.62331
Happiness
0.07648
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.8888 -1.2435 0.2133 1.2756 5.0063
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.94933 1.24627 1.564 0.11891
Pop 0.65349 0.24848 2.630 0.00901 **
Gender 0.03052 0.23455 0.130 0.89657
Connected 0.07000 0.03288 2.129 0.03412 *
Separate 0.05639 0.03429 1.645 0.10113
Software 0.62331 0.05167 12.063 < 2e-16 ***
Happiness 0.07648 0.04762 1.606 0.10937
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.899 on 281 degrees of freedom
Multiple R-squared: 0.486, Adjusted R-squared: 0.475
F-statistic: 44.28 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,] 0.814086261 0.371827477 0.1859137
[2,] 0.761229326 0.477541349 0.2387707
[3,] 0.784144666 0.431710668 0.2158553
[4,] 0.719161451 0.561677098 0.2808385
[5,] 0.712411879 0.575176242 0.2875881
[6,] 0.669044512 0.661910976 0.3309555
[7,] 0.612600387 0.774799226 0.3873996
[8,] 0.524822802 0.950354395 0.4751772
[9,] 0.855741155 0.288517690 0.1442588
[10,] 0.849998820 0.300002361 0.1500012
[11,] 0.802002810 0.395994379 0.1979972
[12,] 0.745897870 0.508204260 0.2541021
[13,] 0.684099119 0.631801762 0.3159009
[14,] 0.703792204 0.592415592 0.2962078
[15,] 0.640615638 0.718768725 0.3593844
[16,] 0.577482789 0.845034422 0.4225172
[17,] 0.510840200 0.978319600 0.4891598
[18,] 0.447186102 0.894372204 0.5528139
[19,] 0.430402656 0.860805312 0.5695973
[20,] 0.369874071 0.739748142 0.6301259
[21,] 0.356279887 0.712559774 0.6437201
[22,] 0.300856874 0.601713748 0.6991431
[23,] 0.285203192 0.570406384 0.7147968
[24,] 0.251839766 0.503679532 0.7481602
[25,] 0.207049744 0.414099488 0.7929503
[26,] 0.205931839 0.411863678 0.7940682
[27,] 0.294191590 0.588383180 0.7058084
[28,] 0.381722840 0.763445680 0.6182772
[29,] 0.383989809 0.767979618 0.6160102
[30,] 0.412444715 0.824889429 0.5875553
[31,] 0.391693744 0.783387488 0.6083063
[32,] 0.357991619 0.715983237 0.6420084
[33,] 0.344927714 0.689855428 0.6550723
[34,] 0.357907769 0.715815537 0.6420922
[35,] 0.317195446 0.634390891 0.6828046
[36,] 0.281112065 0.562224130 0.7188879
[37,] 0.516421228 0.967157543 0.4835788
[38,] 0.567873752 0.864252496 0.4321262
[39,] 0.521401394 0.957197212 0.4785986
[40,] 0.477155337 0.954310674 0.5228447
[41,] 0.480679222 0.961358445 0.5193208
[42,] 0.438573971 0.877147942 0.5614260
[43,] 0.393324924 0.786649849 0.6066751
[44,] 0.446961455 0.893922910 0.5530385
[45,] 0.404471451 0.808942903 0.5955285
[46,] 0.406474981 0.812949963 0.5935250
[47,] 0.390812561 0.781625122 0.6091874
[48,] 0.349516668 0.699033335 0.6504833
[49,] 0.326711949 0.653423899 0.6732881
[50,] 0.287653472 0.575306944 0.7123465
[51,] 0.289704324 0.579408649 0.7102957
[52,] 0.260293166 0.520586333 0.7397068
[53,] 0.226680753 0.453361507 0.7733192
[54,] 0.195968930 0.391937860 0.8040311
[55,] 0.169447927 0.338895854 0.8305521
[56,] 0.146350811 0.292701622 0.8536492
[57,] 0.134138662 0.268277324 0.8658613
[58,] 0.127503793 0.255007586 0.8724962
[59,] 0.218035433 0.436070866 0.7819646
[60,] 0.342188535 0.684377069 0.6578115
[61,] 0.306235927 0.612471855 0.6937641
[62,] 0.390194196 0.780388393 0.6098058
[63,] 0.356334238 0.712668476 0.6436658
[64,] 0.338884780 0.677769561 0.6611152
[65,] 0.311393238 0.622786475 0.6886068
[66,] 0.280709104 0.561418208 0.7192909
[67,] 0.335687512 0.671375023 0.6643125
[68,] 0.303569759 0.607139518 0.6964302
[69,] 0.281518156 0.563036311 0.7184818
[70,] 0.285126827 0.570253653 0.7148732
[71,] 0.257586402 0.515172803 0.7424136
[72,] 0.231165044 0.462330088 0.7688350
[73,] 0.203898145 0.407796290 0.7961019
[74,] 0.184079681 0.368159362 0.8159203
[75,] 0.159801219 0.319602438 0.8401988
[76,] 0.156752145 0.313504290 0.8432479
[77,] 0.134875696 0.269751391 0.8651243
[78,] 0.116908596 0.233817192 0.8830914
[79,] 0.103935724 0.207871447 0.8960643
[80,] 0.087982579 0.175965159 0.9120174
[81,] 0.083526027 0.167052053 0.9164740
[82,] 0.071594285 0.143188569 0.9284057
[83,] 0.060963792 0.121927585 0.9390362
[84,] 0.051702019 0.103404038 0.9482980
[85,] 0.053240891 0.106481782 0.9467591
[86,] 0.046734753 0.093469506 0.9532652
[87,] 0.038512707 0.077025414 0.9614873
[88,] 0.041736268 0.083472536 0.9582637
[89,] 0.034171955 0.068343910 0.9658280
[90,] 0.027750279 0.055500558 0.9722497
[91,] 0.024878295 0.049756590 0.9751217
[92,] 0.021493140 0.042986280 0.9785069
[93,] 0.025415873 0.050831745 0.9745841
[94,] 0.021323148 0.042646295 0.9786769
[95,] 0.018844085 0.037688171 0.9811559
[96,] 0.023909467 0.047818933 0.9760905
[97,] 0.020477894 0.040955788 0.9795221
[98,] 0.016393892 0.032787784 0.9836061
[99,] 0.014925212 0.029850425 0.9850748
[100,] 0.012739701 0.025479402 0.9872603
[101,] 0.010505172 0.021010344 0.9894948
[102,] 0.008264614 0.016529227 0.9917354
[103,] 0.009715276 0.019430552 0.9902847
[104,] 0.013634877 0.027269753 0.9863651
[105,] 0.013176572 0.026353144 0.9868234
[106,] 0.013994326 0.027988653 0.9860057
[107,] 0.011734841 0.023469683 0.9882652
[108,] 0.011625949 0.023251897 0.9883741
[109,] 0.009221128 0.018442257 0.9907789
[110,] 0.008611334 0.017222667 0.9913887
[111,] 0.006928218 0.013856437 0.9930718
[112,] 0.009996397 0.019992795 0.9900036
[113,] 0.008508087 0.017016174 0.9914919
[114,] 0.007583086 0.015166172 0.9924169
[115,] 0.006323853 0.012647706 0.9936761
[116,] 0.005013055 0.010026111 0.9949869
[117,] 0.004200403 0.008400805 0.9957996
[118,] 0.003470473 0.006940946 0.9965295
[119,] 0.005358243 0.010716485 0.9946418
[120,] 0.005686990 0.011373980 0.9943130
[121,] 0.012427026 0.024854053 0.9875730
[122,] 0.013781515 0.027563030 0.9862185
[123,] 0.014818087 0.029636174 0.9851819
[124,] 0.014641765 0.029283530 0.9853582
[125,] 0.011871640 0.023743280 0.9881284
[126,] 0.009855848 0.019711695 0.9901442
[127,] 0.007829475 0.015658949 0.9921705
[128,] 0.010436722 0.020873444 0.9895633
[129,] 0.009335755 0.018671511 0.9906642
[130,] 0.010730875 0.021461751 0.9892691
[131,] 0.014799578 0.029599155 0.9852004
[132,] 0.013737221 0.027474443 0.9862628
[133,] 0.011368732 0.022737464 0.9886313
[134,] 0.011390066 0.022780132 0.9886099
[135,] 0.021437033 0.042874066 0.9785630
[136,] 0.023037788 0.046075576 0.9769622
[137,] 0.026102764 0.052205527 0.9738972
[138,] 0.022577864 0.045155727 0.9774221
[139,] 0.018377801 0.036755602 0.9816222
[140,] 0.027090985 0.054181971 0.9729090
[141,] 0.027386042 0.054772084 0.9726140
[142,] 0.027495660 0.054991321 0.9725043
[143,] 0.050873200 0.101746401 0.9491268
[144,] 0.045233530 0.090467061 0.9547665
[145,] 0.056643534 0.113287067 0.9433565
[146,] 0.049761281 0.099522563 0.9502387
[147,] 0.042769043 0.085538085 0.9572310
[148,] 0.040657050 0.081314101 0.9593429
[149,] 0.034182477 0.068364954 0.9658175
[150,] 0.028872170 0.057744341 0.9711278
[151,] 0.024613635 0.049227270 0.9753864
[152,] 0.020641001 0.041282001 0.9793590
[153,] 0.018120523 0.036241046 0.9818795
[154,] 0.016738207 0.033476415 0.9832618
[155,] 0.013470075 0.026940149 0.9865299
[156,] 0.016783515 0.033567030 0.9832165
[157,] 0.016632753 0.033265506 0.9833672
[158,] 0.017050873 0.034101745 0.9829491
[159,] 0.017120433 0.034240867 0.9828796
[160,] 0.014027253 0.028054505 0.9859727
[161,] 0.013326680 0.026653361 0.9866733
[162,] 0.013158568 0.026317136 0.9868414
[163,] 0.015814713 0.031629426 0.9841853
[164,] 0.013620341 0.027240682 0.9863797
[165,] 0.010880816 0.021761633 0.9891192
[166,] 0.008641516 0.017283033 0.9913585
[167,] 0.007006757 0.014013514 0.9929932
[168,] 0.006047189 0.012094379 0.9939528
[169,] 0.005039130 0.010078261 0.9949609
[170,] 0.004017474 0.008034948 0.9959825
[171,] 0.004510273 0.009020546 0.9954897
[172,] 0.003508500 0.007016999 0.9964915
[173,] 0.027560078 0.055120156 0.9724399
[174,] 0.024492803 0.048985606 0.9755072
[175,] 0.030840616 0.061681231 0.9691594
[176,] 0.025660095 0.051320191 0.9743399
[177,] 0.020985043 0.041970086 0.9790150
[178,] 0.017700483 0.035400966 0.9822995
[179,] 0.015546434 0.031092868 0.9844536
[180,] 0.012604127 0.025208254 0.9873959
[181,] 0.017848677 0.035697353 0.9821513
[182,] 0.016361046 0.032722093 0.9836390
[183,] 0.013322581 0.026645163 0.9866774
[184,] 0.010581794 0.021163588 0.9894182
[185,] 0.016019144 0.032038288 0.9839809
[186,] 0.013208414 0.026416828 0.9867916
[187,] 0.012259546 0.024519092 0.9877405
[188,] 0.012019439 0.024038877 0.9879806
[189,] 0.010045137 0.020090273 0.9899549
[190,] 0.009926722 0.019853444 0.9900733
[191,] 0.012262614 0.024525227 0.9877374
[192,] 0.014708934 0.029417869 0.9852911
[193,] 0.011691882 0.023383763 0.9883081
[194,] 0.010158255 0.020316511 0.9898417
[195,] 0.008182881 0.016365762 0.9918171
[196,] 0.009555560 0.019111119 0.9904444
[197,] 0.008558420 0.017116840 0.9914416
[198,] 0.011226190 0.022452380 0.9887738
[199,] 0.025524797 0.051049594 0.9744752
[200,] 0.020842136 0.041684272 0.9791579
[201,] 0.032616972 0.065233945 0.9673830
[202,] 0.027450962 0.054901925 0.9725490
[203,] 0.025254050 0.050508100 0.9747460
[204,] 0.027089655 0.054179311 0.9729103
[205,] 0.021715753 0.043431506 0.9782842
[206,] 0.018580140 0.037160280 0.9814199
[207,] 0.015442639 0.030885279 0.9845574
[208,] 0.014541980 0.029083960 0.9854580
[209,] 0.011382272 0.022764544 0.9886177
[210,] 0.008832455 0.017664909 0.9911675
[211,] 0.006945925 0.013891849 0.9930541
[212,] 0.011079604 0.022159209 0.9889204
[213,] 0.008605177 0.017210354 0.9913948
[214,] 0.007446686 0.014893373 0.9925533
[215,] 0.006339805 0.012679610 0.9936602
[216,] 0.004940182 0.009880364 0.9950598
[217,] 0.006294580 0.012589161 0.9937054
[218,] 0.014254882 0.028509764 0.9857451
[219,] 0.078645740 0.157291481 0.9213543
[220,] 0.071046729 0.142093458 0.9289533
[221,] 0.058423443 0.116846886 0.9415766
[222,] 0.059090845 0.118181689 0.9409092
[223,] 0.242418527 0.484837055 0.7575815
[224,] 0.223150129 0.446300259 0.7768499
[225,] 0.203024330 0.406048660 0.7969757
[226,] 0.180125768 0.360251536 0.8198742
[227,] 0.153650275 0.307300550 0.8463497
[228,] 0.184216521 0.368433043 0.8157835
[229,] 0.236459002 0.472918004 0.7635410
[230,] 0.298129055 0.596258111 0.7018709
[231,] 0.258940353 0.517880706 0.7410596
[232,] 0.243732363 0.487464726 0.7562676
[233,] 0.278933514 0.557867028 0.7210665
[234,] 0.267274511 0.534549022 0.7327255
[235,] 0.242977297 0.485954593 0.7570227
[236,] 0.214090077 0.428180154 0.7859099
[237,] 0.183488832 0.366977664 0.8165112
[238,] 0.182159755 0.364319510 0.8178402
[239,] 0.278669369 0.557338737 0.7213306
[240,] 0.262232422 0.524464844 0.7377676
[241,] 0.222656633 0.445313266 0.7773434
[242,] 0.189286492 0.378572984 0.8107135
[243,] 0.212337552 0.424675105 0.7876624
[244,] 0.194095848 0.388191696 0.8059042
[245,] 0.258985607 0.517971214 0.7410144
[246,] 0.248278850 0.496557700 0.7517212
[247,] 0.500867585 0.998264831 0.4991324
[248,] 0.453196418 0.906392836 0.5468036
[249,] 0.461761174 0.923522349 0.5382388
[250,] 0.440693714 0.881387428 0.5593063
[251,] 0.463087072 0.926174144 0.5369129
[252,] 0.403509463 0.807018926 0.5964905
[253,] 0.354189024 0.708378049 0.6458110
[254,] 0.316609907 0.633219814 0.6833901
[255,] 0.259566944 0.519133888 0.7404331
[256,] 0.257080674 0.514161348 0.7429193
[257,] 0.368586065 0.737172130 0.6314139
[258,] 0.613750885 0.772498230 0.3862491
[259,] 0.552043750 0.895912499 0.4479562
[260,] 0.503051090 0.993897819 0.4969489
[261,] 0.513661354 0.972677291 0.4863386
[262,] 0.441941819 0.883883639 0.5580582
[263,] 0.418639515 0.837279030 0.5813605
[264,] 0.364864531 0.729729062 0.6351355
[265,] 0.288946915 0.577893830 0.7110531
[266,] 0.425595101 0.851190203 0.5744049
[267,] 0.376777276 0.753554551 0.6232227
[268,] 0.266605613 0.533211226 0.7333944
[269,] 0.171092306 0.342184612 0.8289077
> postscript(file="/var/fisher/rcomp/tmp/1mxbf1386624250.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/2qvrj1386624250.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3vc0o1386624250.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4ghea1386624250.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5w34l1386624250.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-3.196720526 0.599048387 2.101982563 3.708589526 -2.426569443 -1.328454956
7 8 9 10 11 12
3.338031851 -1.840536023 -1.754005451 0.876741887 1.455745221 -0.003513068
13 14 15 16 17 18
0.518882294 0.413643599 -0.756135145 -0.254217635 0.768782887 3.783862016
19 20 21 22 23 24
2.597111350 0.618707087 1.063191374 1.127477205 2.319975450 0.803156287
25 26 27 28 29 30
1.082732019 0.900700321 1.446655427 -1.826925959 0.568787101 0.081820554
31 32 33 34 35 36
-0.254897905 -0.278101073 -0.504769950 0.273214676 -1.225045290 -2.290622195
37 38 39 40 41 42
-2.679122681 -1.610702801 2.055250338 1.858867973 1.622305329 -1.577690457
43 44 45 46 47 48
2.359282287 -0.064657005 -0.252645025 -4.518107899 -2.742889509 0.135703725
49 50 51 52 53 54
0.547176185 -1.541365356 -0.966265940 0.161577975 -3.024792568 -0.177061826
55 56 57 58 59 60
-1.681711441 1.915846593 0.015904391 0.792404772 0.002851410 1.848432817
61 62 63 64 65 66
0.743145415 0.345849700 -0.554718485 -0.704208653 0.611569509 0.992795861
67 68 69 70 71 72
1.305276444 4.000401411 -4.245975420 0.233729847 -3.350173880 -1.026336113
73 74 75 76 77 78
0.913811538 0.551534157 1.020607148 3.417301658 -0.713052073 1.387828650
79 80 81 82 83 84
-1.937897569 0.810599541 0.430127391 0.203212140 -0.757180359 0.205706261
85 86 87 88 89 90
2.004665261 -0.219135415 0.588869651 1.058139761 0.180587278 -1.743407998
91 92 93 94 95 96
-0.301805281 -0.022357748 -0.002772981 -1.829075295 1.149737640 -0.217892018
97 98 99 100 101 102
2.487969285 0.109952147 -0.253251879 -1.349971233 1.059243882 2.186807292
103 104 105 106 107 108
0.880478889 0.853741365 -2.375470339 1.029514454 -0.299070313 1.282196463
109 110 111 112 113 114
-1.139849913 0.722402236 -0.231925932 1.963909001 -2.750422388 1.675903017
115 116 117 118 119 120
-2.372794279 0.673748669 -1.844213725 0.126072027 -1.773530918 0.500312931
121 122 123 124 125 126
-3.119580412 -1.239803597 -1.151744660 -1.008185073 0.329350213 0.930763425
127 128 129 130 131 132
0.963636886 -2.947908874 2.068875581 -3.912130811 2.346435848 -2.188193464
133 134 135 136 137 138
-1.962667697 -0.212115497 0.745462059 0.116301446 -2.781471954 -1.134374056
139 140 141 142 143 144
-2.350597730 3.075633552 1.377194795 -0.127442504 1.926543697 -3.391684342
145 146 147 148 149 150
2.277360350 -2.473980128 1.011517354 -0.155530369 -3.079509950 -1.757164664
151 152 153 154 155 156
2.040792179 4.059706843 1.114468469 -2.672241524 1.229549351 0.963636886
157 158 159 160 161 162
1.331436178 -0.920511270 0.696553779 0.357261740 0.579692059 1.442916183
163 164 165 166 167 168
-1.148865330 0.162589681 -2.607137157 -1.670834516 1.906093414 1.754636521
169 170 171 172 173 174
0.710227729 -1.714538951 -1.777074303 -2.782240022 0.986274687 -0.105643376
175 176 177 178 179 180
-0.268664665 0.645213435 -1.177474942 0.919570245 0.174774924 2.353698319
181 182 183 184 185 186
0.035652769 -5.802204518 1.276055411 2.686181993 0.031595460 0.154268523
187 188 189 190 191 192
0.822686840 -0.570994544 0.553995285 2.902064655 1.785914473 -0.404706551
193 194 195 196 197 198
-0.079749484 3.211464968 1.073185381 1.658050400 2.090833111 1.298616388
199 200 201 202 203 204
-1.987442391 -2.607862065 2.659124163 0.606409392 1.556489406 0.867140444
205 206 207 208 209 210
-2.557641416 1.352938590 -2.388872155 -4.004014549 0.913654843 3.394809532
211 212 213 214 215 216
1.275390320 1.300568605 2.579854356 0.068965775 0.958566944 -0.326082193
217 218 219 220 221 222
-1.581307029 -0.096248970 0.661047830 -0.680877211 -3.213515074 0.389913603
223 224 225 226 227 228
1.295757839 -1.411952136 -0.754162147 2.137784731 -3.323696080 5.006262599
229 230 231 232 233 234
1.568591811 -0.621186645 -2.291906427 -6.888755126 -1.465904777 1.997450446
235 236 237 238 239 240
-1.435589201 0.451867096 -1.841357810 1.723979932 2.082072530 0.219159630
241 242 243 244 245 246
1.232892881 -2.984932576 1.754636914 0.894765173 -0.547043962 -1.421314045
247 248 249 250 251 252
0.848684793 3.301550054 -1.551168930 0.035529582 1.346073127 2.370764312
253 254 255 256 257 258
-1.490813272 -4.756390426 1.511256999 -4.099166942 -0.678051553 1.535602883
259 260 261 262 263 264
0.381717957 2.027889210 0.207442360 -1.254630750 1.215443334 -0.283063007
265 266 267 268 269 270
1.345978489 1.416829120 2.488315900 0.776444556 1.066047804 0.165602291
271 272 273 274 275 276
0.559841274 -3.812295283 2.085449567 -4.204671654 2.853707856 -4.641728383
277 278 279 280 281 282
-1.340072379 0.868969473 -0.834112225 -2.020562227 1.015570219 1.362585983
283 284 285 286 287 288
-0.078739607 -2.998079801 1.481783597 -3.332934802 -1.079733789 -0.491237122
> postscript(file="/var/fisher/rcomp/tmp/69z5t1386624250.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 -3.196720526 NA
1 0.599048387 -3.196720526
2 2.101982563 0.599048387
3 3.708589526 2.101982563
4 -2.426569443 3.708589526
5 -1.328454956 -2.426569443
6 3.338031851 -1.328454956
7 -1.840536023 3.338031851
8 -1.754005451 -1.840536023
9 0.876741887 -1.754005451
10 1.455745221 0.876741887
11 -0.003513068 1.455745221
12 0.518882294 -0.003513068
13 0.413643599 0.518882294
14 -0.756135145 0.413643599
15 -0.254217635 -0.756135145
16 0.768782887 -0.254217635
17 3.783862016 0.768782887
18 2.597111350 3.783862016
19 0.618707087 2.597111350
20 1.063191374 0.618707087
21 1.127477205 1.063191374
22 2.319975450 1.127477205
23 0.803156287 2.319975450
24 1.082732019 0.803156287
25 0.900700321 1.082732019
26 1.446655427 0.900700321
27 -1.826925959 1.446655427
28 0.568787101 -1.826925959
29 0.081820554 0.568787101
30 -0.254897905 0.081820554
31 -0.278101073 -0.254897905
32 -0.504769950 -0.278101073
33 0.273214676 -0.504769950
34 -1.225045290 0.273214676
35 -2.290622195 -1.225045290
36 -2.679122681 -2.290622195
37 -1.610702801 -2.679122681
38 2.055250338 -1.610702801
39 1.858867973 2.055250338
40 1.622305329 1.858867973
41 -1.577690457 1.622305329
42 2.359282287 -1.577690457
43 -0.064657005 2.359282287
44 -0.252645025 -0.064657005
45 -4.518107899 -0.252645025
46 -2.742889509 -4.518107899
47 0.135703725 -2.742889509
48 0.547176185 0.135703725
49 -1.541365356 0.547176185
50 -0.966265940 -1.541365356
51 0.161577975 -0.966265940
52 -3.024792568 0.161577975
53 -0.177061826 -3.024792568
54 -1.681711441 -0.177061826
55 1.915846593 -1.681711441
56 0.015904391 1.915846593
57 0.792404772 0.015904391
58 0.002851410 0.792404772
59 1.848432817 0.002851410
60 0.743145415 1.848432817
61 0.345849700 0.743145415
62 -0.554718485 0.345849700
63 -0.704208653 -0.554718485
64 0.611569509 -0.704208653
65 0.992795861 0.611569509
66 1.305276444 0.992795861
67 4.000401411 1.305276444
68 -4.245975420 4.000401411
69 0.233729847 -4.245975420
70 -3.350173880 0.233729847
71 -1.026336113 -3.350173880
72 0.913811538 -1.026336113
73 0.551534157 0.913811538
74 1.020607148 0.551534157
75 3.417301658 1.020607148
76 -0.713052073 3.417301658
77 1.387828650 -0.713052073
78 -1.937897569 1.387828650
79 0.810599541 -1.937897569
80 0.430127391 0.810599541
81 0.203212140 0.430127391
82 -0.757180359 0.203212140
83 0.205706261 -0.757180359
84 2.004665261 0.205706261
85 -0.219135415 2.004665261
86 0.588869651 -0.219135415
87 1.058139761 0.588869651
88 0.180587278 1.058139761
89 -1.743407998 0.180587278
90 -0.301805281 -1.743407998
91 -0.022357748 -0.301805281
92 -0.002772981 -0.022357748
93 -1.829075295 -0.002772981
94 1.149737640 -1.829075295
95 -0.217892018 1.149737640
96 2.487969285 -0.217892018
97 0.109952147 2.487969285
98 -0.253251879 0.109952147
99 -1.349971233 -0.253251879
100 1.059243882 -1.349971233
101 2.186807292 1.059243882
102 0.880478889 2.186807292
103 0.853741365 0.880478889
104 -2.375470339 0.853741365
105 1.029514454 -2.375470339
106 -0.299070313 1.029514454
107 1.282196463 -0.299070313
108 -1.139849913 1.282196463
109 0.722402236 -1.139849913
110 -0.231925932 0.722402236
111 1.963909001 -0.231925932
112 -2.750422388 1.963909001
113 1.675903017 -2.750422388
114 -2.372794279 1.675903017
115 0.673748669 -2.372794279
116 -1.844213725 0.673748669
117 0.126072027 -1.844213725
118 -1.773530918 0.126072027
119 0.500312931 -1.773530918
120 -3.119580412 0.500312931
121 -1.239803597 -3.119580412
122 -1.151744660 -1.239803597
123 -1.008185073 -1.151744660
124 0.329350213 -1.008185073
125 0.930763425 0.329350213
126 0.963636886 0.930763425
127 -2.947908874 0.963636886
128 2.068875581 -2.947908874
129 -3.912130811 2.068875581
130 2.346435848 -3.912130811
131 -2.188193464 2.346435848
132 -1.962667697 -2.188193464
133 -0.212115497 -1.962667697
134 0.745462059 -0.212115497
135 0.116301446 0.745462059
136 -2.781471954 0.116301446
137 -1.134374056 -2.781471954
138 -2.350597730 -1.134374056
139 3.075633552 -2.350597730
140 1.377194795 3.075633552
141 -0.127442504 1.377194795
142 1.926543697 -0.127442504
143 -3.391684342 1.926543697
144 2.277360350 -3.391684342
145 -2.473980128 2.277360350
146 1.011517354 -2.473980128
147 -0.155530369 1.011517354
148 -3.079509950 -0.155530369
149 -1.757164664 -3.079509950
150 2.040792179 -1.757164664
151 4.059706843 2.040792179
152 1.114468469 4.059706843
153 -2.672241524 1.114468469
154 1.229549351 -2.672241524
155 0.963636886 1.229549351
156 1.331436178 0.963636886
157 -0.920511270 1.331436178
158 0.696553779 -0.920511270
159 0.357261740 0.696553779
160 0.579692059 0.357261740
161 1.442916183 0.579692059
162 -1.148865330 1.442916183
163 0.162589681 -1.148865330
164 -2.607137157 0.162589681
165 -1.670834516 -2.607137157
166 1.906093414 -1.670834516
167 1.754636521 1.906093414
168 0.710227729 1.754636521
169 -1.714538951 0.710227729
170 -1.777074303 -1.714538951
171 -2.782240022 -1.777074303
172 0.986274687 -2.782240022
173 -0.105643376 0.986274687
174 -0.268664665 -0.105643376
175 0.645213435 -0.268664665
176 -1.177474942 0.645213435
177 0.919570245 -1.177474942
178 0.174774924 0.919570245
179 2.353698319 0.174774924
180 0.035652769 2.353698319
181 -5.802204518 0.035652769
182 1.276055411 -5.802204518
183 2.686181993 1.276055411
184 0.031595460 2.686181993
185 0.154268523 0.031595460
186 0.822686840 0.154268523
187 -0.570994544 0.822686840
188 0.553995285 -0.570994544
189 2.902064655 0.553995285
190 1.785914473 2.902064655
191 -0.404706551 1.785914473
192 -0.079749484 -0.404706551
193 3.211464968 -0.079749484
194 1.073185381 3.211464968
195 1.658050400 1.073185381
196 2.090833111 1.658050400
197 1.298616388 2.090833111
198 -1.987442391 1.298616388
199 -2.607862065 -1.987442391
200 2.659124163 -2.607862065
201 0.606409392 2.659124163
202 1.556489406 0.606409392
203 0.867140444 1.556489406
204 -2.557641416 0.867140444
205 1.352938590 -2.557641416
206 -2.388872155 1.352938590
207 -4.004014549 -2.388872155
208 0.913654843 -4.004014549
209 3.394809532 0.913654843
210 1.275390320 3.394809532
211 1.300568605 1.275390320
212 2.579854356 1.300568605
213 0.068965775 2.579854356
214 0.958566944 0.068965775
215 -0.326082193 0.958566944
216 -1.581307029 -0.326082193
217 -0.096248970 -1.581307029
218 0.661047830 -0.096248970
219 -0.680877211 0.661047830
220 -3.213515074 -0.680877211
221 0.389913603 -3.213515074
222 1.295757839 0.389913603
223 -1.411952136 1.295757839
224 -0.754162147 -1.411952136
225 2.137784731 -0.754162147
226 -3.323696080 2.137784731
227 5.006262599 -3.323696080
228 1.568591811 5.006262599
229 -0.621186645 1.568591811
230 -2.291906427 -0.621186645
231 -6.888755126 -2.291906427
232 -1.465904777 -6.888755126
233 1.997450446 -1.465904777
234 -1.435589201 1.997450446
235 0.451867096 -1.435589201
236 -1.841357810 0.451867096
237 1.723979932 -1.841357810
238 2.082072530 1.723979932
239 0.219159630 2.082072530
240 1.232892881 0.219159630
241 -2.984932576 1.232892881
242 1.754636914 -2.984932576
243 0.894765173 1.754636914
244 -0.547043962 0.894765173
245 -1.421314045 -0.547043962
246 0.848684793 -1.421314045
247 3.301550054 0.848684793
248 -1.551168930 3.301550054
249 0.035529582 -1.551168930
250 1.346073127 0.035529582
251 2.370764312 1.346073127
252 -1.490813272 2.370764312
253 -4.756390426 -1.490813272
254 1.511256999 -4.756390426
255 -4.099166942 1.511256999
256 -0.678051553 -4.099166942
257 1.535602883 -0.678051553
258 0.381717957 1.535602883
259 2.027889210 0.381717957
260 0.207442360 2.027889210
261 -1.254630750 0.207442360
262 1.215443334 -1.254630750
263 -0.283063007 1.215443334
264 1.345978489 -0.283063007
265 1.416829120 1.345978489
266 2.488315900 1.416829120
267 0.776444556 2.488315900
268 1.066047804 0.776444556
269 0.165602291 1.066047804
270 0.559841274 0.165602291
271 -3.812295283 0.559841274
272 2.085449567 -3.812295283
273 -4.204671654 2.085449567
274 2.853707856 -4.204671654
275 -4.641728383 2.853707856
276 -1.340072379 -4.641728383
277 0.868969473 -1.340072379
278 -0.834112225 0.868969473
279 -2.020562227 -0.834112225
280 1.015570219 -2.020562227
281 1.362585983 1.015570219
282 -0.078739607 1.362585983
283 -2.998079801 -0.078739607
284 1.481783597 -2.998079801
285 -3.332934802 1.481783597
286 -1.079733789 -3.332934802
287 -0.491237122 -1.079733789
288 NA -0.491237122
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.599048387 -3.196720526
[2,] 2.101982563 0.599048387
[3,] 3.708589526 2.101982563
[4,] -2.426569443 3.708589526
[5,] -1.328454956 -2.426569443
[6,] 3.338031851 -1.328454956
[7,] -1.840536023 3.338031851
[8,] -1.754005451 -1.840536023
[9,] 0.876741887 -1.754005451
[10,] 1.455745221 0.876741887
[11,] -0.003513068 1.455745221
[12,] 0.518882294 -0.003513068
[13,] 0.413643599 0.518882294
[14,] -0.756135145 0.413643599
[15,] -0.254217635 -0.756135145
[16,] 0.768782887 -0.254217635
[17,] 3.783862016 0.768782887
[18,] 2.597111350 3.783862016
[19,] 0.618707087 2.597111350
[20,] 1.063191374 0.618707087
[21,] 1.127477205 1.063191374
[22,] 2.319975450 1.127477205
[23,] 0.803156287 2.319975450
[24,] 1.082732019 0.803156287
[25,] 0.900700321 1.082732019
[26,] 1.446655427 0.900700321
[27,] -1.826925959 1.446655427
[28,] 0.568787101 -1.826925959
[29,] 0.081820554 0.568787101
[30,] -0.254897905 0.081820554
[31,] -0.278101073 -0.254897905
[32,] -0.504769950 -0.278101073
[33,] 0.273214676 -0.504769950
[34,] -1.225045290 0.273214676
[35,] -2.290622195 -1.225045290
[36,] -2.679122681 -2.290622195
[37,] -1.610702801 -2.679122681
[38,] 2.055250338 -1.610702801
[39,] 1.858867973 2.055250338
[40,] 1.622305329 1.858867973
[41,] -1.577690457 1.622305329
[42,] 2.359282287 -1.577690457
[43,] -0.064657005 2.359282287
[44,] -0.252645025 -0.064657005
[45,] -4.518107899 -0.252645025
[46,] -2.742889509 -4.518107899
[47,] 0.135703725 -2.742889509
[48,] 0.547176185 0.135703725
[49,] -1.541365356 0.547176185
[50,] -0.966265940 -1.541365356
[51,] 0.161577975 -0.966265940
[52,] -3.024792568 0.161577975
[53,] -0.177061826 -3.024792568
[54,] -1.681711441 -0.177061826
[55,] 1.915846593 -1.681711441
[56,] 0.015904391 1.915846593
[57,] 0.792404772 0.015904391
[58,] 0.002851410 0.792404772
[59,] 1.848432817 0.002851410
[60,] 0.743145415 1.848432817
[61,] 0.345849700 0.743145415
[62,] -0.554718485 0.345849700
[63,] -0.704208653 -0.554718485
[64,] 0.611569509 -0.704208653
[65,] 0.992795861 0.611569509
[66,] 1.305276444 0.992795861
[67,] 4.000401411 1.305276444
[68,] -4.245975420 4.000401411
[69,] 0.233729847 -4.245975420
[70,] -3.350173880 0.233729847
[71,] -1.026336113 -3.350173880
[72,] 0.913811538 -1.026336113
[73,] 0.551534157 0.913811538
[74,] 1.020607148 0.551534157
[75,] 3.417301658 1.020607148
[76,] -0.713052073 3.417301658
[77,] 1.387828650 -0.713052073
[78,] -1.937897569 1.387828650
[79,] 0.810599541 -1.937897569
[80,] 0.430127391 0.810599541
[81,] 0.203212140 0.430127391
[82,] -0.757180359 0.203212140
[83,] 0.205706261 -0.757180359
[84,] 2.004665261 0.205706261
[85,] -0.219135415 2.004665261
[86,] 0.588869651 -0.219135415
[87,] 1.058139761 0.588869651
[88,] 0.180587278 1.058139761
[89,] -1.743407998 0.180587278
[90,] -0.301805281 -1.743407998
[91,] -0.022357748 -0.301805281
[92,] -0.002772981 -0.022357748
[93,] -1.829075295 -0.002772981
[94,] 1.149737640 -1.829075295
[95,] -0.217892018 1.149737640
[96,] 2.487969285 -0.217892018
[97,] 0.109952147 2.487969285
[98,] -0.253251879 0.109952147
[99,] -1.349971233 -0.253251879
[100,] 1.059243882 -1.349971233
[101,] 2.186807292 1.059243882
[102,] 0.880478889 2.186807292
[103,] 0.853741365 0.880478889
[104,] -2.375470339 0.853741365
[105,] 1.029514454 -2.375470339
[106,] -0.299070313 1.029514454
[107,] 1.282196463 -0.299070313
[108,] -1.139849913 1.282196463
[109,] 0.722402236 -1.139849913
[110,] -0.231925932 0.722402236
[111,] 1.963909001 -0.231925932
[112,] -2.750422388 1.963909001
[113,] 1.675903017 -2.750422388
[114,] -2.372794279 1.675903017
[115,] 0.673748669 -2.372794279
[116,] -1.844213725 0.673748669
[117,] 0.126072027 -1.844213725
[118,] -1.773530918 0.126072027
[119,] 0.500312931 -1.773530918
[120,] -3.119580412 0.500312931
[121,] -1.239803597 -3.119580412
[122,] -1.151744660 -1.239803597
[123,] -1.008185073 -1.151744660
[124,] 0.329350213 -1.008185073
[125,] 0.930763425 0.329350213
[126,] 0.963636886 0.930763425
[127,] -2.947908874 0.963636886
[128,] 2.068875581 -2.947908874
[129,] -3.912130811 2.068875581
[130,] 2.346435848 -3.912130811
[131,] -2.188193464 2.346435848
[132,] -1.962667697 -2.188193464
[133,] -0.212115497 -1.962667697
[134,] 0.745462059 -0.212115497
[135,] 0.116301446 0.745462059
[136,] -2.781471954 0.116301446
[137,] -1.134374056 -2.781471954
[138,] -2.350597730 -1.134374056
[139,] 3.075633552 -2.350597730
[140,] 1.377194795 3.075633552
[141,] -0.127442504 1.377194795
[142,] 1.926543697 -0.127442504
[143,] -3.391684342 1.926543697
[144,] 2.277360350 -3.391684342
[145,] -2.473980128 2.277360350
[146,] 1.011517354 -2.473980128
[147,] -0.155530369 1.011517354
[148,] -3.079509950 -0.155530369
[149,] -1.757164664 -3.079509950
[150,] 2.040792179 -1.757164664
[151,] 4.059706843 2.040792179
[152,] 1.114468469 4.059706843
[153,] -2.672241524 1.114468469
[154,] 1.229549351 -2.672241524
[155,] 0.963636886 1.229549351
[156,] 1.331436178 0.963636886
[157,] -0.920511270 1.331436178
[158,] 0.696553779 -0.920511270
[159,] 0.357261740 0.696553779
[160,] 0.579692059 0.357261740
[161,] 1.442916183 0.579692059
[162,] -1.148865330 1.442916183
[163,] 0.162589681 -1.148865330
[164,] -2.607137157 0.162589681
[165,] -1.670834516 -2.607137157
[166,] 1.906093414 -1.670834516
[167,] 1.754636521 1.906093414
[168,] 0.710227729 1.754636521
[169,] -1.714538951 0.710227729
[170,] -1.777074303 -1.714538951
[171,] -2.782240022 -1.777074303
[172,] 0.986274687 -2.782240022
[173,] -0.105643376 0.986274687
[174,] -0.268664665 -0.105643376
[175,] 0.645213435 -0.268664665
[176,] -1.177474942 0.645213435
[177,] 0.919570245 -1.177474942
[178,] 0.174774924 0.919570245
[179,] 2.353698319 0.174774924
[180,] 0.035652769 2.353698319
[181,] -5.802204518 0.035652769
[182,] 1.276055411 -5.802204518
[183,] 2.686181993 1.276055411
[184,] 0.031595460 2.686181993
[185,] 0.154268523 0.031595460
[186,] 0.822686840 0.154268523
[187,] -0.570994544 0.822686840
[188,] 0.553995285 -0.570994544
[189,] 2.902064655 0.553995285
[190,] 1.785914473 2.902064655
[191,] -0.404706551 1.785914473
[192,] -0.079749484 -0.404706551
[193,] 3.211464968 -0.079749484
[194,] 1.073185381 3.211464968
[195,] 1.658050400 1.073185381
[196,] 2.090833111 1.658050400
[197,] 1.298616388 2.090833111
[198,] -1.987442391 1.298616388
[199,] -2.607862065 -1.987442391
[200,] 2.659124163 -2.607862065
[201,] 0.606409392 2.659124163
[202,] 1.556489406 0.606409392
[203,] 0.867140444 1.556489406
[204,] -2.557641416 0.867140444
[205,] 1.352938590 -2.557641416
[206,] -2.388872155 1.352938590
[207,] -4.004014549 -2.388872155
[208,] 0.913654843 -4.004014549
[209,] 3.394809532 0.913654843
[210,] 1.275390320 3.394809532
[211,] 1.300568605 1.275390320
[212,] 2.579854356 1.300568605
[213,] 0.068965775 2.579854356
[214,] 0.958566944 0.068965775
[215,] -0.326082193 0.958566944
[216,] -1.581307029 -0.326082193
[217,] -0.096248970 -1.581307029
[218,] 0.661047830 -0.096248970
[219,] -0.680877211 0.661047830
[220,] -3.213515074 -0.680877211
[221,] 0.389913603 -3.213515074
[222,] 1.295757839 0.389913603
[223,] -1.411952136 1.295757839
[224,] -0.754162147 -1.411952136
[225,] 2.137784731 -0.754162147
[226,] -3.323696080 2.137784731
[227,] 5.006262599 -3.323696080
[228,] 1.568591811 5.006262599
[229,] -0.621186645 1.568591811
[230,] -2.291906427 -0.621186645
[231,] -6.888755126 -2.291906427
[232,] -1.465904777 -6.888755126
[233,] 1.997450446 -1.465904777
[234,] -1.435589201 1.997450446
[235,] 0.451867096 -1.435589201
[236,] -1.841357810 0.451867096
[237,] 1.723979932 -1.841357810
[238,] 2.082072530 1.723979932
[239,] 0.219159630 2.082072530
[240,] 1.232892881 0.219159630
[241,] -2.984932576 1.232892881
[242,] 1.754636914 -2.984932576
[243,] 0.894765173 1.754636914
[244,] -0.547043962 0.894765173
[245,] -1.421314045 -0.547043962
[246,] 0.848684793 -1.421314045
[247,] 3.301550054 0.848684793
[248,] -1.551168930 3.301550054
[249,] 0.035529582 -1.551168930
[250,] 1.346073127 0.035529582
[251,] 2.370764312 1.346073127
[252,] -1.490813272 2.370764312
[253,] -4.756390426 -1.490813272
[254,] 1.511256999 -4.756390426
[255,] -4.099166942 1.511256999
[256,] -0.678051553 -4.099166942
[257,] 1.535602883 -0.678051553
[258,] 0.381717957 1.535602883
[259,] 2.027889210 0.381717957
[260,] 0.207442360 2.027889210
[261,] -1.254630750 0.207442360
[262,] 1.215443334 -1.254630750
[263,] -0.283063007 1.215443334
[264,] 1.345978489 -0.283063007
[265,] 1.416829120 1.345978489
[266,] 2.488315900 1.416829120
[267,] 0.776444556 2.488315900
[268,] 1.066047804 0.776444556
[269,] 0.165602291 1.066047804
[270,] 0.559841274 0.165602291
[271,] -3.812295283 0.559841274
[272,] 2.085449567 -3.812295283
[273,] -4.204671654 2.085449567
[274,] 2.853707856 -4.204671654
[275,] -4.641728383 2.853707856
[276,] -1.340072379 -4.641728383
[277,] 0.868969473 -1.340072379
[278,] -0.834112225 0.868969473
[279,] -2.020562227 -0.834112225
[280,] 1.015570219 -2.020562227
[281,] 1.362585983 1.015570219
[282,] -0.078739607 1.362585983
[283,] -2.998079801 -0.078739607
[284,] 1.481783597 -2.998079801
[285,] -3.332934802 1.481783597
[286,] -1.079733789 -3.332934802
[287,] -0.491237122 -1.079733789
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.599048387 -3.196720526
2 2.101982563 0.599048387
3 3.708589526 2.101982563
4 -2.426569443 3.708589526
5 -1.328454956 -2.426569443
6 3.338031851 -1.328454956
7 -1.840536023 3.338031851
8 -1.754005451 -1.840536023
9 0.876741887 -1.754005451
10 1.455745221 0.876741887
11 -0.003513068 1.455745221
12 0.518882294 -0.003513068
13 0.413643599 0.518882294
14 -0.756135145 0.413643599
15 -0.254217635 -0.756135145
16 0.768782887 -0.254217635
17 3.783862016 0.768782887
18 2.597111350 3.783862016
19 0.618707087 2.597111350
20 1.063191374 0.618707087
21 1.127477205 1.063191374
22 2.319975450 1.127477205
23 0.803156287 2.319975450
24 1.082732019 0.803156287
25 0.900700321 1.082732019
26 1.446655427 0.900700321
27 -1.826925959 1.446655427
28 0.568787101 -1.826925959
29 0.081820554 0.568787101
30 -0.254897905 0.081820554
31 -0.278101073 -0.254897905
32 -0.504769950 -0.278101073
33 0.273214676 -0.504769950
34 -1.225045290 0.273214676
35 -2.290622195 -1.225045290
36 -2.679122681 -2.290622195
37 -1.610702801 -2.679122681
38 2.055250338 -1.610702801
39 1.858867973 2.055250338
40 1.622305329 1.858867973
41 -1.577690457 1.622305329
42 2.359282287 -1.577690457
43 -0.064657005 2.359282287
44 -0.252645025 -0.064657005
45 -4.518107899 -0.252645025
46 -2.742889509 -4.518107899
47 0.135703725 -2.742889509
48 0.547176185 0.135703725
49 -1.541365356 0.547176185
50 -0.966265940 -1.541365356
51 0.161577975 -0.966265940
52 -3.024792568 0.161577975
53 -0.177061826 -3.024792568
54 -1.681711441 -0.177061826
55 1.915846593 -1.681711441
56 0.015904391 1.915846593
57 0.792404772 0.015904391
58 0.002851410 0.792404772
59 1.848432817 0.002851410
60 0.743145415 1.848432817
61 0.345849700 0.743145415
62 -0.554718485 0.345849700
63 -0.704208653 -0.554718485
64 0.611569509 -0.704208653
65 0.992795861 0.611569509
66 1.305276444 0.992795861
67 4.000401411 1.305276444
68 -4.245975420 4.000401411
69 0.233729847 -4.245975420
70 -3.350173880 0.233729847
71 -1.026336113 -3.350173880
72 0.913811538 -1.026336113
73 0.551534157 0.913811538
74 1.020607148 0.551534157
75 3.417301658 1.020607148
76 -0.713052073 3.417301658
77 1.387828650 -0.713052073
78 -1.937897569 1.387828650
79 0.810599541 -1.937897569
80 0.430127391 0.810599541
81 0.203212140 0.430127391
82 -0.757180359 0.203212140
83 0.205706261 -0.757180359
84 2.004665261 0.205706261
85 -0.219135415 2.004665261
86 0.588869651 -0.219135415
87 1.058139761 0.588869651
88 0.180587278 1.058139761
89 -1.743407998 0.180587278
90 -0.301805281 -1.743407998
91 -0.022357748 -0.301805281
92 -0.002772981 -0.022357748
93 -1.829075295 -0.002772981
94 1.149737640 -1.829075295
95 -0.217892018 1.149737640
96 2.487969285 -0.217892018
97 0.109952147 2.487969285
98 -0.253251879 0.109952147
99 -1.349971233 -0.253251879
100 1.059243882 -1.349971233
101 2.186807292 1.059243882
102 0.880478889 2.186807292
103 0.853741365 0.880478889
104 -2.375470339 0.853741365
105 1.029514454 -2.375470339
106 -0.299070313 1.029514454
107 1.282196463 -0.299070313
108 -1.139849913 1.282196463
109 0.722402236 -1.139849913
110 -0.231925932 0.722402236
111 1.963909001 -0.231925932
112 -2.750422388 1.963909001
113 1.675903017 -2.750422388
114 -2.372794279 1.675903017
115 0.673748669 -2.372794279
116 -1.844213725 0.673748669
117 0.126072027 -1.844213725
118 -1.773530918 0.126072027
119 0.500312931 -1.773530918
120 -3.119580412 0.500312931
121 -1.239803597 -3.119580412
122 -1.151744660 -1.239803597
123 -1.008185073 -1.151744660
124 0.329350213 -1.008185073
125 0.930763425 0.329350213
126 0.963636886 0.930763425
127 -2.947908874 0.963636886
128 2.068875581 -2.947908874
129 -3.912130811 2.068875581
130 2.346435848 -3.912130811
131 -2.188193464 2.346435848
132 -1.962667697 -2.188193464
133 -0.212115497 -1.962667697
134 0.745462059 -0.212115497
135 0.116301446 0.745462059
136 -2.781471954 0.116301446
137 -1.134374056 -2.781471954
138 -2.350597730 -1.134374056
139 3.075633552 -2.350597730
140 1.377194795 3.075633552
141 -0.127442504 1.377194795
142 1.926543697 -0.127442504
143 -3.391684342 1.926543697
144 2.277360350 -3.391684342
145 -2.473980128 2.277360350
146 1.011517354 -2.473980128
147 -0.155530369 1.011517354
148 -3.079509950 -0.155530369
149 -1.757164664 -3.079509950
150 2.040792179 -1.757164664
151 4.059706843 2.040792179
152 1.114468469 4.059706843
153 -2.672241524 1.114468469
154 1.229549351 -2.672241524
155 0.963636886 1.229549351
156 1.331436178 0.963636886
157 -0.920511270 1.331436178
158 0.696553779 -0.920511270
159 0.357261740 0.696553779
160 0.579692059 0.357261740
161 1.442916183 0.579692059
162 -1.148865330 1.442916183
163 0.162589681 -1.148865330
164 -2.607137157 0.162589681
165 -1.670834516 -2.607137157
166 1.906093414 -1.670834516
167 1.754636521 1.906093414
168 0.710227729 1.754636521
169 -1.714538951 0.710227729
170 -1.777074303 -1.714538951
171 -2.782240022 -1.777074303
172 0.986274687 -2.782240022
173 -0.105643376 0.986274687
174 -0.268664665 -0.105643376
175 0.645213435 -0.268664665
176 -1.177474942 0.645213435
177 0.919570245 -1.177474942
178 0.174774924 0.919570245
179 2.353698319 0.174774924
180 0.035652769 2.353698319
181 -5.802204518 0.035652769
182 1.276055411 -5.802204518
183 2.686181993 1.276055411
184 0.031595460 2.686181993
185 0.154268523 0.031595460
186 0.822686840 0.154268523
187 -0.570994544 0.822686840
188 0.553995285 -0.570994544
189 2.902064655 0.553995285
190 1.785914473 2.902064655
191 -0.404706551 1.785914473
192 -0.079749484 -0.404706551
193 3.211464968 -0.079749484
194 1.073185381 3.211464968
195 1.658050400 1.073185381
196 2.090833111 1.658050400
197 1.298616388 2.090833111
198 -1.987442391 1.298616388
199 -2.607862065 -1.987442391
200 2.659124163 -2.607862065
201 0.606409392 2.659124163
202 1.556489406 0.606409392
203 0.867140444 1.556489406
204 -2.557641416 0.867140444
205 1.352938590 -2.557641416
206 -2.388872155 1.352938590
207 -4.004014549 -2.388872155
208 0.913654843 -4.004014549
209 3.394809532 0.913654843
210 1.275390320 3.394809532
211 1.300568605 1.275390320
212 2.579854356 1.300568605
213 0.068965775 2.579854356
214 0.958566944 0.068965775
215 -0.326082193 0.958566944
216 -1.581307029 -0.326082193
217 -0.096248970 -1.581307029
218 0.661047830 -0.096248970
219 -0.680877211 0.661047830
220 -3.213515074 -0.680877211
221 0.389913603 -3.213515074
222 1.295757839 0.389913603
223 -1.411952136 1.295757839
224 -0.754162147 -1.411952136
225 2.137784731 -0.754162147
226 -3.323696080 2.137784731
227 5.006262599 -3.323696080
228 1.568591811 5.006262599
229 -0.621186645 1.568591811
230 -2.291906427 -0.621186645
231 -6.888755126 -2.291906427
232 -1.465904777 -6.888755126
233 1.997450446 -1.465904777
234 -1.435589201 1.997450446
235 0.451867096 -1.435589201
236 -1.841357810 0.451867096
237 1.723979932 -1.841357810
238 2.082072530 1.723979932
239 0.219159630 2.082072530
240 1.232892881 0.219159630
241 -2.984932576 1.232892881
242 1.754636914 -2.984932576
243 0.894765173 1.754636914
244 -0.547043962 0.894765173
245 -1.421314045 -0.547043962
246 0.848684793 -1.421314045
247 3.301550054 0.848684793
248 -1.551168930 3.301550054
249 0.035529582 -1.551168930
250 1.346073127 0.035529582
251 2.370764312 1.346073127
252 -1.490813272 2.370764312
253 -4.756390426 -1.490813272
254 1.511256999 -4.756390426
255 -4.099166942 1.511256999
256 -0.678051553 -4.099166942
257 1.535602883 -0.678051553
258 0.381717957 1.535602883
259 2.027889210 0.381717957
260 0.207442360 2.027889210
261 -1.254630750 0.207442360
262 1.215443334 -1.254630750
263 -0.283063007 1.215443334
264 1.345978489 -0.283063007
265 1.416829120 1.345978489
266 2.488315900 1.416829120
267 0.776444556 2.488315900
268 1.066047804 0.776444556
269 0.165602291 1.066047804
270 0.559841274 0.165602291
271 -3.812295283 0.559841274
272 2.085449567 -3.812295283
273 -4.204671654 2.085449567
274 2.853707856 -4.204671654
275 -4.641728383 2.853707856
276 -1.340072379 -4.641728383
277 0.868969473 -1.340072379
278 -0.834112225 0.868969473
279 -2.020562227 -0.834112225
280 1.015570219 -2.020562227
281 1.362585983 1.015570219
282 -0.078739607 1.362585983
283 -2.998079801 -0.078739607
284 1.481783597 -2.998079801
285 -3.332934802 1.481783597
286 -1.079733789 -3.332934802
287 -0.491237122 -1.079733789
> 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/73h051386624250.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/8ykkc1386624250.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/9xkv81386624250.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/107evt1386624250.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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11gwg41386624250.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12qrbg1386624250.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13ft2z1386624251.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14yfck1386624251.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/153lea1386624251.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/1623ap1386624251.tab")
+ }
>
> try(system("convert tmp/1mxbf1386624250.ps tmp/1mxbf1386624250.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qvrj1386624250.ps tmp/2qvrj1386624250.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vc0o1386624250.ps tmp/3vc0o1386624250.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ghea1386624250.ps tmp/4ghea1386624250.png",intern=TRUE))
character(0)
> try(system("convert tmp/5w34l1386624250.ps tmp/5w34l1386624250.png",intern=TRUE))
character(0)
> try(system("convert tmp/69z5t1386624250.ps tmp/69z5t1386624250.png",intern=TRUE))
character(0)
> try(system("convert tmp/73h051386624250.ps tmp/73h051386624250.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ykkc1386624250.ps tmp/8ykkc1386624250.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xkv81386624250.ps tmp/9xkv81386624250.png",intern=TRUE))
character(0)
> try(system("convert tmp/107evt1386624250.ps tmp/107evt1386624250.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
20.824 3.153 24.000