R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
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+ ,0
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+ ,12
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+ ,0
+ ,1
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+ ,6
+ ,6
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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 = '3'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) 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
Connected Pop Gender Separate Learning Software Happiness
1 41 1 1 38 13 12 14
2 39 1 1 32 16 11 18
3 30 1 1 35 19 15 11
4 31 1 0 33 15 6 12
5 34 1 1 37 14 13 16
6 35 1 1 29 13 10 18
7 39 1 1 31 19 12 14
8 34 1 1 36 15 14 14
9 36 1 1 35 14 12 15
10 37 1 1 38 15 9 15
11 38 1 0 31 16 10 17
12 36 1 1 34 16 12 19
13 38 1 0 35 16 12 10
14 39 1 1 38 16 11 16
15 33 1 1 37 17 15 18
16 32 1 0 33 15 12 14
17 36 1 0 32 15 10 14
18 38 1 1 38 20 12 17
19 39 1 0 38 18 11 14
20 32 1 1 32 16 12 16
21 32 1 0 33 16 11 18
22 31 1 1 31 16 12 11
23 39 1 1 38 19 13 14
24 37 1 1 39 16 11 12
25 39 1 0 32 17 12 17
26 41 1 1 32 17 13 9
27 36 1 0 35 16 10 16
28 33 1 1 37 15 14 14
29 33 1 1 33 16 12 15
30 34 1 0 33 14 10 11
31 31 1 1 31 15 12 16
32 27 1 0 32 12 8 13
33 37 1 1 31 14 10 17
34 34 1 1 37 16 12 15
35 34 1 0 30 14 12 14
36 32 1 0 33 10 7 16
37 29 1 0 31 10 9 9
38 36 1 0 33 14 12 15
39 29 1 1 31 16 10 17
40 35 1 0 33 16 10 13
41 37 1 0 32 16 10 15
42 34 1 1 33 14 12 16
43 38 1 0 32 20 15 16
44 35 1 0 33 14 10 12
45 38 1 1 28 14 10 15
46 37 1 1 35 11 12 11
47 38 1 1 39 14 13 15
48 33 1 1 34 15 11 15
49 36 1 1 38 16 11 17
50 38 1 0 32 14 12 13
51 32 1 1 38 16 14 16
52 32 1 0 30 14 10 14
53 32 1 0 33 12 12 11
54 34 1 1 38 16 13 12
55 32 1 0 32 9 5 12
56 37 1 1 35 14 6 15
57 39 1 1 34 16 12 16
58 29 1 1 34 16 12 15
59 37 1 0 36 15 11 12
60 35 1 1 34 16 10 12
61 30 1 0 28 12 7 8
62 38 1 0 34 16 12 13
63 34 1 1 35 16 14 11
64 31 1 1 35 14 11 14
65 34 1 1 31 16 12 15
66 35 1 0 37 17 13 10
67 36 1 1 35 18 14 11
68 30 1 0 27 18 11 12
69 39 1 1 40 12 12 15
70 35 1 0 37 16 12 15
71 38 1 0 36 10 8 14
72 31 1 1 38 14 11 16
73 34 1 1 39 18 14 15
74 38 1 0 41 18 14 15
75 34 1 0 27 16 12 13
76 39 1 1 30 17 9 12
77 37 1 1 37 16 13 17
78 34 1 1 31 16 11 13
79 28 1 0 31 13 12 15
80 37 1 0 27 16 12 13
81 33 1 0 36 16 12 15
82 35 1 1 37 16 12 15
83 37 1 0 33 15 12 16
84 32 1 1 34 15 11 15
85 33 1 1 31 16 10 14
86 38 1 0 39 14 9 15
87 33 1 1 34 16 12 14
88 29 1 1 32 16 12 13
89 33 1 1 33 15 12 7
90 31 1 1 36 12 9 17
91 36 1 1 32 17 15 13
92 35 1 1 41 16 12 15
93 32 1 1 28 15 12 14
94 29 1 1 30 13 12 13
95 39 1 1 36 16 10 16
96 37 1 1 35 16 13 12
97 35 1 1 31 16 9 14
98 37 1 0 34 16 12 17
99 32 1 0 36 14 10 15
100 38 1 1 36 16 14 17
101 37 1 0 35 16 11 12
102 36 1 1 37 20 15 16
103 32 1 0 28 15 11 11
104 33 1 1 39 16 11 15
105 40 1 0 32 13 12 9
106 38 1 1 35 17 12 16
107 41 1 0 39 16 12 15
108 36 1 0 35 16 11 10
109 43 1 1 42 12 7 10
110 30 1 1 34 16 12 15
111 31 1 1 33 16 14 11
112 32 1 1 41 17 11 13
113 37 1 1 34 12 10 18
114 37 1 0 32 18 13 16
115 33 1 1 40 14 13 14
116 34 1 1 40 14 8 14
117 33 1 1 35 13 11 14
118 38 1 1 36 16 12 14
119 33 1 0 37 13 11 12
120 31 1 1 27 16 13 14
121 38 1 1 39 13 12 15
122 37 1 1 38 16 14 15
123 36 1 1 31 15 13 15
124 31 1 1 33 16 15 13
125 39 1 0 32 15 10 17
126 44 1 1 39 17 11 17
127 33 1 1 36 15 9 19
128 35 1 1 33 12 11 15
129 32 1 0 33 16 10 13
130 28 1 0 32 10 11 9
131 40 1 1 37 16 8 15
132 27 1 0 30 12 11 15
133 37 1 0 38 14 12 15
134 32 1 1 29 15 12 16
135 28 1 0 22 13 9 11
136 34 1 0 35 15 11 14
137 30 1 1 35 11 10 11
138 35 1 1 34 12 8 15
139 31 1 0 35 11 9 13
140 32 1 1 34 16 8 15
141 30 1 0 37 15 9 16
142 30 1 1 35 17 15 14
143 31 1 0 23 16 11 15
144 40 1 1 31 10 8 16
145 32 1 1 27 18 13 16
146 36 1 0 36 13 12 11
147 32 1 0 31 16 12 12
148 35 1 0 32 13 9 9
149 38 1 1 39 10 7 16
150 42 1 1 37 15 13 13
151 34 1 0 38 16 9 16
152 35 1 1 39 16 6 12
153 38 1 1 34 14 8 9
154 33 1 1 31 10 8 13
155 32 1 1 37 13 6 14
156 33 1 1 36 15 9 19
157 34 1 1 32 16 11 13
158 32 1 1 38 12 8 12
159 27 0 0 26 13 10 10
160 31 0 0 26 12 8 14
161 38 0 0 33 17 14 16
162 34 0 1 39 15 10 10
163 24 0 0 30 10 8 11
164 30 0 0 33 14 11 14
165 26 0 1 25 11 12 12
166 34 0 1 38 13 12 9
167 27 0 0 37 16 12 9
168 37 0 0 31 12 5 11
169 36 0 1 37 16 12 16
170 41 0 0 35 12 10 9
171 29 0 1 25 9 7 13
172 36 0 1 28 12 12 16
173 32 0 0 35 15 11 13
174 37 0 1 33 12 8 9
175 30 0 0 30 12 9 12
176 31 0 1 31 14 10 16
177 38 0 1 37 12 9 11
178 36 0 1 36 16 12 14
179 35 0 0 30 11 6 13
180 31 0 0 36 19 15 15
181 38 0 0 32 15 12 14
182 22 0 1 28 8 12 16
183 32 0 1 36 16 12 13
184 36 0 0 34 17 11 14
185 39 0 1 31 12 7 15
186 28 0 0 28 11 7 13
187 32 0 0 36 11 5 11
188 32 0 1 36 14 12 11
189 38 0 1 40 16 12 14
190 32 0 1 33 12 3 15
191 35 0 1 37 16 11 11
192 32 0 1 32 13 10 15
193 37 0 0 38 15 12 12
194 34 0 1 31 16 9 14
195 33 0 1 37 16 12 14
196 33 0 0 33 14 9 8
197 30 0 0 30 16 12 9
198 24 0 0 30 14 10 15
199 34 0 0 31 11 9 17
200 34 0 0 32 12 12 13
201 33 0 1 34 15 8 15
202 34 0 1 36 15 11 15
203 35 0 1 37 16 11 14
204 35 0 0 36 16 12 16
205 36 0 0 33 11 10 13
206 34 0 0 33 15 10 16
207 34 0 1 33 12 12 9
208 41 0 0 44 12 12 16
209 32 0 0 39 15 11 11
210 30 0 0 32 15 8 10
211 35 0 1 35 16 12 11
212 28 0 0 25 14 10 15
213 33 0 1 35 17 11 17
214 39 0 1 34 14 10 14
215 36 0 0 35 13 8 8
216 36 0 1 39 15 12 15
217 35 0 0 33 13 12 11
218 38 0 0 36 14 10 16
219 33 0 1 32 15 12 10
220 31 0 0 32 12 9 15
221 32 0 1 36 8 6 16
222 31 0 0 32 14 10 19
223 33 0 0 34 14 9 12
224 34 0 0 33 11 9 8
225 34 0 0 35 12 9 11
226 34 0 1 30 13 6 14
227 33 0 0 38 10 10 9
228 32 0 0 34 16 6 15
229 41 0 1 33 18 14 13
230 34 0 1 32 13 10 16
231 36 0 0 31 11 10 11
232 37 0 0 30 4 6 12
233 36 0 0 27 13 12 13
234 29 0 1 31 16 12 10
235 37 0 0 30 10 7 11
236 27 0 0 32 12 8 12
237 35 0 0 35 12 11 8
238 28 0 0 28 10 3 12
239 35 0 0 33 13 6 12
240 29 0 0 35 12 8 11
241 32 0 0 35 14 9 13
242 36 0 1 32 10 9 14
243 19 0 1 21 12 8 10
244 21 0 1 20 12 9 12
245 31 0 0 34 11 7 15
246 33 0 0 32 10 7 13
247 36 0 1 34 12 6 13
248 33 0 1 32 16 9 13
249 37 0 0 33 12 10 12
250 34 0 0 33 14 11 12
251 35 0 0 37 16 12 9
252 31 0 1 32 14 8 9
253 37 0 1 34 13 11 15
254 35 0 1 30 4 3 10
255 27 0 1 30 15 11 14
256 34 0 0 38 11 12 15
257 40 0 0 36 11 7 7
258 29 0 0 32 14 9 14
259 38 0 0 34 15 12 8
260 34 0 1 33 14 8 10
261 21 0 0 27 13 11 13
262 36 0 0 32 11 8 13
263 38 0 1 34 15 10 13
264 30 0 0 29 11 8 8
265 35 0 0 35 13 7 12
266 30 0 1 27 13 8 13
267 36 0 1 33 16 10 12
268 34 0 0 38 13 8 10
269 35 0 1 36 16 12 13
270 34 0 0 33 16 14 12
271 32 0 0 39 12 7 9
272 33 0 1 29 7 6 15
273 33 0 0 32 16 11 13
274 26 0 1 34 5 4 13
275 35 0 0 38 16 9 13
276 21 0 0 17 4 5 15
277 38 0 0 35 12 9 15
278 35 0 0 32 15 11 14
279 33 0 1 34 14 12 15
280 37 0 0 36 11 9 11
281 38 0 0 31 16 12 15
282 34 0 1 35 15 10 14
283 27 0 0 29 12 9 13
284 16 0 1 22 6 6 12
285 40 0 0 41 16 10 16
286 36 0 0 36 10 9 16
287 42 0 1 42 15 13 9
288 30 0 1 33 14 12 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop Gender Separate Learning Software
13.62128 0.62965 -0.38668 0.49766 0.22678 -0.04890
Happiness
0.05687
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.9328 -2.4482 0.0612 2.2586 7.9218
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.62128 2.10124 6.482 4.04e-10 ***
Pop 0.62965 0.45115 1.396 0.1639
Gender -0.38668 0.42154 -0.917 0.3598
Separate 0.49766 0.05444 9.142 < 2e-16 ***
Learning 0.22678 0.10652 2.129 0.0341 *
Software -0.04890 0.11454 -0.427 0.6698
Happiness 0.05687 0.08603 0.661 0.5091
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.418 on 281 degrees of freedom
Multiple R-squared: 0.3055, Adjusted R-squared: 0.2907
F-statistic: 20.6 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,] 0.83676795 0.32646410 0.1632321
[2,] 0.82457868 0.35084265 0.1754213
[3,] 0.80625788 0.38748423 0.1937421
[4,] 0.83969640 0.32060720 0.1603036
[5,] 0.78104956 0.43790088 0.2189504
[6,] 0.79693876 0.40612249 0.2030612
[7,] 0.76241836 0.47516329 0.2375816
[8,] 0.69297548 0.61404904 0.3070245
[9,] 0.61571111 0.76857778 0.3842889
[10,] 0.57436003 0.85127994 0.4256400
[11,] 0.56138829 0.87722342 0.4386117
[12,] 0.57386166 0.85227669 0.4261383
[13,] 0.53335502 0.93328996 0.4666450
[14,] 0.48737575 0.97475150 0.5126242
[15,] 0.41489904 0.82979808 0.5851010
[16,] 0.44089800 0.88179599 0.5591020
[17,] 0.65472218 0.69055564 0.3452778
[18,] 0.59183004 0.81633993 0.4081700
[19,] 0.55971574 0.88056852 0.4402843
[20,] 0.52741254 0.94517491 0.4725875
[21,] 0.46665727 0.93331455 0.5333427
[22,] 0.46175770 0.92351539 0.5382423
[23,] 0.60614437 0.78771125 0.3938556
[24,] 0.58914131 0.82171737 0.4108587
[25,] 0.55730088 0.88539824 0.4426991
[26,] 0.51107894 0.97784213 0.4889211
[27,] 0.45712830 0.91425661 0.5428717
[28,] 0.41486654 0.82973308 0.5851335
[29,] 0.38949475 0.77898951 0.6105052
[30,] 0.49461546 0.98923091 0.5053845
[31,] 0.44158654 0.88317308 0.5584135
[32,] 0.41093722 0.82187444 0.5890628
[33,] 0.36086294 0.72172589 0.6391371
[34,] 0.32194973 0.64389946 0.6780503
[35,] 0.28226702 0.56453405 0.7177330
[36,] 0.35396698 0.70793396 0.6460330
[37,] 0.37706974 0.75413949 0.6229303
[38,] 0.34820205 0.69640411 0.6517979
[39,] 0.31999089 0.63998178 0.6800091
[40,] 0.27912951 0.55825902 0.7208705
[41,] 0.29558836 0.59117672 0.7044116
[42,] 0.32647841 0.65295682 0.6735216
[43,] 0.29535854 0.59071708 0.7046415
[44,] 0.26219475 0.52438951 0.7378052
[45,] 0.23857405 0.47714811 0.7614259
[46,] 0.20435571 0.40871142 0.7956443
[47,] 0.18407278 0.36814555 0.8159272
[48,] 0.19076209 0.38152418 0.8092379
[49,] 0.27699512 0.55399024 0.7230049
[50,] 0.25027777 0.50055553 0.7497222
[51,] 0.21667497 0.43334994 0.7833250
[52,] 0.19502012 0.39004023 0.8049799
[53,] 0.18646013 0.37292026 0.8135399
[54,] 0.16065953 0.32131906 0.8393405
[55,] 0.16375419 0.32750838 0.8362458
[56,] 0.13941576 0.27883152 0.8605842
[57,] 0.11944478 0.23888957 0.8805552
[58,] 0.09982805 0.19965610 0.9001720
[59,] 0.10573649 0.21147298 0.8942635
[60,] 0.10442834 0.20885669 0.8955717
[61,] 0.08975489 0.17950977 0.9102451
[62,] 0.09281099 0.18562197 0.9071890
[63,] 0.11813208 0.23626416 0.8818679
[64,] 0.11588481 0.23176962 0.8841152
[65,] 0.09734650 0.19469301 0.9026535
[66,] 0.08452648 0.16905295 0.9154735
[67,] 0.11211895 0.22423790 0.8878810
[68,] 0.09539796 0.19079592 0.9046020
[69,] 0.08006777 0.16013555 0.9199322
[70,] 0.10326941 0.20653881 0.8967306
[71,] 0.11968248 0.23936497 0.8803175
[72,] 0.11338823 0.22677646 0.8866118
[73,] 0.09693277 0.19386554 0.9030672
[74,] 0.09002272 0.18004545 0.9099773
[75,] 0.08506305 0.17012609 0.9149370
[76,] 0.07308238 0.14616476 0.9269176
[77,] 0.06348615 0.12697230 0.9365138
[78,] 0.05546883 0.11093767 0.9445312
[79,] 0.07029127 0.14058254 0.9297087
[80,] 0.05835278 0.11670557 0.9416472
[81,] 0.06085553 0.12171105 0.9391445
[82,] 0.05409682 0.10819364 0.9459032
[83,] 0.04890102 0.09780204 0.9510990
[84,] 0.04064081 0.08128162 0.9593592
[85,] 0.03987781 0.07975561 0.9601222
[86,] 0.03964826 0.07929651 0.9603517
[87,] 0.03564693 0.07129386 0.9643531
[88,] 0.02996933 0.05993865 0.9700307
[89,] 0.02552402 0.05104804 0.9744760
[90,] 0.02590308 0.05180616 0.9740969
[91,] 0.02366428 0.04732855 0.9763357
[92,] 0.02008297 0.04016593 0.9799170
[93,] 0.01647566 0.03295132 0.9835243
[94,] 0.01338689 0.02677379 0.9866131
[95,] 0.01416557 0.02833113 0.9858344
[96,] 0.03092658 0.06185316 0.9690734
[97,] 0.02852508 0.05705016 0.9714749
[98,] 0.03127285 0.06254570 0.9687272
[99,] 0.02583912 0.05167825 0.9741609
[100,] 0.04420224 0.08840449 0.9557978
[101,] 0.05268028 0.10536056 0.9473197
[102,] 0.04996761 0.09993523 0.9500324
[103,] 0.07174586 0.14349172 0.9282541
[104,] 0.07005098 0.14010195 0.9299490
[105,] 0.06438140 0.12876280 0.9356186
[106,] 0.06521472 0.13042943 0.9347853
[107,] 0.06222169 0.12444338 0.9377783
[108,] 0.05351669 0.10703338 0.9464833
[109,] 0.04998664 0.09997327 0.9500134
[110,] 0.04567855 0.09135710 0.9543215
[111,] 0.03896629 0.07793258 0.9610337
[112,] 0.03540382 0.07080764 0.9645962
[113,] 0.02963500 0.05926999 0.9703650
[114,] 0.02838982 0.05677964 0.9716102
[115,] 0.02688077 0.05376154 0.9731192
[116,] 0.03307337 0.06614674 0.9669266
[117,] 0.05867791 0.11735583 0.9413221
[118,] 0.05585051 0.11170101 0.9441495
[119,] 0.04936720 0.09873441 0.9506328
[120,] 0.04658092 0.09316184 0.9534191
[121,] 0.05081795 0.10163590 0.9491820
[122,] 0.05317709 0.10635417 0.9468229
[123,] 0.06732596 0.13465192 0.9326740
[124,] 0.05754237 0.11508473 0.9424576
[125,] 0.04882550 0.09765100 0.9511745
[126,] 0.04308582 0.08617163 0.9569142
[127,] 0.03656884 0.07313767 0.9634312
[128,] 0.03740295 0.07480591 0.9625970
[129,] 0.03159401 0.06318802 0.9684060
[130,] 0.03082753 0.06165506 0.9691725
[131,] 0.03015593 0.06031186 0.9698441
[132,] 0.04999502 0.09999003 0.9500050
[133,] 0.06532474 0.13064947 0.9346753
[134,] 0.05836152 0.11672303 0.9416385
[135,] 0.11364945 0.22729890 0.8863506
[136,] 0.10164493 0.20328986 0.8983551
[137,] 0.08971637 0.17943275 0.9102836
[138,] 0.07849871 0.15699743 0.9215013
[139,] 0.07103553 0.14207106 0.9289645
[140,] 0.06379666 0.12759332 0.9362033
[141,] 0.10165995 0.20331990 0.8983401
[142,] 0.09575122 0.19150244 0.9042488
[143,] 0.08607173 0.17214345 0.9139283
[144,] 0.09785607 0.19571214 0.9021439
[145,] 0.09011808 0.18023616 0.9098819
[146,] 0.08691140 0.17382280 0.9130886
[147,] 0.07947387 0.15894774 0.9205261
[148,] 0.07577893 0.15155786 0.9242211
[149,] 0.06897720 0.13795440 0.9310228
[150,] 0.05896097 0.11792194 0.9410390
[151,] 0.05486548 0.10973096 0.9451345
[152,] 0.06221752 0.12443504 0.9377825
[153,] 0.05711235 0.11422470 0.9428876
[154,] 0.08929344 0.17858687 0.9107066
[155,] 0.08371842 0.16743685 0.9162816
[156,] 0.07317035 0.14634070 0.9268297
[157,] 0.06737103 0.13474206 0.9326290
[158,] 0.13004344 0.26008688 0.8699566
[159,] 0.17283920 0.34567840 0.8271608
[160,] 0.15576416 0.31152832 0.8442358
[161,] 0.25630764 0.51261529 0.7436924
[162,] 0.23296403 0.46592806 0.7670360
[163,] 0.29453869 0.58907738 0.7054613
[164,] 0.28004868 0.56009736 0.7199513
[165,] 0.30035373 0.60070746 0.6996463
[166,] 0.27481954 0.54963908 0.7251805
[167,] 0.24964622 0.49929244 0.7503538
[168,] 0.24693936 0.49387872 0.7530606
[169,] 0.22224091 0.44448182 0.7777591
[170,] 0.22759510 0.45519020 0.7724049
[171,] 0.25408512 0.50817023 0.7459149
[172,] 0.28993814 0.57987627 0.7100619
[173,] 0.42491127 0.84982253 0.5750887
[174,] 0.42133412 0.84266825 0.5786659
[175,] 0.39682337 0.79364675 0.6031766
[176,] 0.52833358 0.94333284 0.4716664
[177,] 0.50698586 0.98602827 0.4930141
[178,] 0.48833780 0.97667560 0.5116622
[179,] 0.48375621 0.96751241 0.5162438
[180,] 0.45282093 0.90564186 0.5471791
[181,] 0.42285987 0.84571973 0.5771401
[182,] 0.39029025 0.78058050 0.6097098
[183,] 0.35662898 0.71325796 0.6433710
[184,] 0.32806219 0.65612439 0.6719378
[185,] 0.31076527 0.62153054 0.6892347
[186,] 0.30405226 0.60810451 0.6959477
[187,] 0.27266695 0.54533390 0.7273331
[188,] 0.25034062 0.50068125 0.7496594
[189,] 0.37697375 0.75394749 0.6230263
[190,] 0.35620113 0.71240225 0.6437989
[191,] 0.32840925 0.65681850 0.6715907
[192,] 0.29685210 0.59370420 0.7031479
[193,] 0.27004429 0.54008857 0.7299557
[194,] 0.24295454 0.48590908 0.7570455
[195,] 0.21632624 0.43265248 0.7836738
[196,] 0.20967429 0.41934859 0.7903257
[197,] 0.18411029 0.36822058 0.8158897
[198,] 0.16321999 0.32643997 0.8367800
[199,] 0.15359512 0.30719023 0.8464049
[200,] 0.19124181 0.38248363 0.8087582
[201,] 0.17897304 0.35794609 0.8210270
[202,] 0.15582582 0.31165164 0.8441742
[203,] 0.13692459 0.27384917 0.8630754
[204,] 0.12314488 0.24628976 0.8768551
[205,] 0.14748656 0.29497312 0.8525134
[206,] 0.13151141 0.26302282 0.8684886
[207,] 0.11842270 0.23684539 0.8815773
[208,] 0.10330805 0.20661610 0.8966920
[209,] 0.09520534 0.19041068 0.9047947
[210,] 0.07948167 0.15896334 0.9205183
[211,] 0.06827950 0.13655900 0.9317205
[212,] 0.06285965 0.12571931 0.9371403
[213,] 0.05538460 0.11076921 0.9446154
[214,] 0.04557916 0.09115832 0.9544208
[215,] 0.03761745 0.07523490 0.9623825
[216,] 0.03028663 0.06057325 0.9697134
[217,] 0.03013626 0.06027252 0.9698637
[218,] 0.03381270 0.06762539 0.9661873
[219,] 0.02794688 0.05589376 0.9720531
[220,] 0.05608326 0.11216652 0.9439167
[221,] 0.04715615 0.09431229 0.9528439
[222,] 0.04915079 0.09830158 0.9508492
[223,] 0.07797524 0.15595047 0.9220248
[224,] 0.13974188 0.27948377 0.8602581
[225,] 0.13132372 0.26264743 0.8686763
[226,] 0.20766517 0.41533033 0.7923348
[227,] 0.24769675 0.49539351 0.7523032
[228,] 0.21475657 0.42951314 0.7852434
[229,] 0.18774833 0.37549667 0.8122517
[230,] 0.16959915 0.33919830 0.8304009
[231,] 0.21492965 0.42985929 0.7850704
[232,] 0.20388591 0.40777183 0.7961141
[233,] 0.21757163 0.43514325 0.7824284
[234,] 0.25840288 0.51680576 0.7415971
[235,] 0.24441816 0.48883632 0.7555818
[236,] 0.22954048 0.45908095 0.7704595
[237,] 0.19528546 0.39057093 0.8047145
[238,] 0.17594589 0.35189177 0.8240541
[239,] 0.14514403 0.29028806 0.8548560
[240,] 0.15133655 0.30267310 0.8486634
[241,] 0.12375155 0.24750310 0.8762484
[242,] 0.10532716 0.21065433 0.8946728
[243,] 0.08741090 0.17482181 0.9125891
[244,] 0.08682336 0.17364672 0.9131766
[245,] 0.15229721 0.30459442 0.8477028
[246,] 0.16527017 0.33054033 0.8347298
[247,] 0.14504711 0.29009422 0.8549529
[248,] 0.21542330 0.43084660 0.7845767
[249,] 0.23267596 0.46535193 0.7673240
[250,] 0.26442553 0.52885106 0.7355745
[251,] 0.23125696 0.46251392 0.7687430
[252,] 0.54894771 0.90210458 0.4510523
[253,] 0.57762749 0.84474503 0.4223725
[254,] 0.61350886 0.77298227 0.3864911
[255,] 0.58538303 0.82923394 0.4146170
[256,] 0.53323141 0.93353718 0.4667686
[257,] 0.52161092 0.95677817 0.4783891
[258,] 0.64452328 0.71095344 0.3554767
[259,] 0.56627742 0.86744516 0.4337226
[260,] 0.48311217 0.96622434 0.5168878
[261,] 0.44970387 0.89940775 0.5502961
[262,] 0.38436972 0.76873944 0.6156303
[263,] 0.79609493 0.40781015 0.2039051
[264,] 0.72532649 0.54934702 0.2746735
[265,] 0.64224307 0.71551386 0.3577569
[266,] 0.56258246 0.87483508 0.4374175
[267,] 0.65142991 0.69714017 0.3485701
[268,] 0.62879009 0.74241983 0.3712099
[269,] 0.45924075 0.91848149 0.5407593
> postscript(file="/var/fisher/rcomp/tmp/1mzbw1355145401.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/2ixu41355145401.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3h23y1355145401.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4l6is1355145401.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/54nk61355145401.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
5.06713529 5.09639072 -5.48325075 -3.46448146 -1.72681646 3.22081601
7 8 9 10 11 12
5.19008511 -1.29329652 1.27647267 0.40999761 4.21533339 1.09310159
13 14 15 16 17 18
2.72056643 2.22415457 -3.42308881 -2.28480057 2.11505585 0.30906666
19 20 21 22 23 24
1.49764456 -1.74097065 -2.78795574 -1.95896904 1.75535391 -0.04603574
25 26 27 28 29 30
4.58869627 7.47922690 0.28155332 -2.79095852 -1.18176472 0.01477832
31 32 33 34 35 36
-2.01652795 -6.24553972 4.05557924 -2.17241272 1.43496612 -1.50914688
37 38 39 40 41 42
-3.01794185 1.88511221 -4.39798215 0.44748108 2.83140724 0.21492875
43 44 45 46 47 48
3.11193048 0.95791039 6.66230108 3.18428645 1.33472747 -1.50154882
49 50 51 52 53 54
-0.83271335 4.49651005 -4.62913706 -0.66283945 -1.43385471 -2.45056816
55 56 57 58 59 60
-0.65503808 1.98305593 4.26370536 -5.67942672 1.28704649 0.39337147
61 62 63 64 65 66
-1.01945490 3.04762466 -0.85181145 -3.71556220 0.81355927 -1.45263547
67 68 69 70 71 72
0.69462716 -1.91433761 2.24172407 -1.55909718 3.16050576 -5.32228404
73 74 75 76 77 78
-3.52349253 -0.90550098 2.53125865 6.10833597 0.76275423 0.87839233
79 80 81 82 83 84
-4.89278310 5.53125865 -3.06143518 -1.17241272 2.60146359 -2.50154882
85 86 87 88 89 90
-0.22737838 0.75243185 -1.62255880 -4.57036688 -0.50004065 -4.02807215
91 92 93 94 95 96
2.34956079 -3.16306071 0.59019389 -2.89470080 3.17057578 2.04241784
97 98 99 100 101 102
1.72371883 1.82015297 -3.70567937 2.30931902 1.55792780 -0.98969505
103 104 105 106 107 108
0.32521040 -4.21663950 6.95076243 2.53926266 3.44557883 0.67166364
109 110 111 112 113 114
5.28622574 -4.67942672 -2.85648746 -6.32500835 2.95928672 2.46768629
115 116 117 118 119 120
-4.10606661 -3.35058056 -1.48878150 2.38211720 -2.75705411 -0.09002202
121 122 123 124 125 126
1.51260537 0.42773086 3.08924276 -2.92132051 4.94445209 6.44284396
127 128 129 130 131 132
-2.82215008 1.67645527 -2.55251892 -4.41779827 3.63197612 -5.21724320
133 134 135 136 137 138
0.39680221 -0.02120396 -0.33306179 -1.32902735 -3.91351913 1.03208490
139 140 141 142 143 144
-3.46284223 -2.87503788 -6.53589277 -5.20029313 1.35926801 7.92176437
145 146 147 148 149 150
0.34268074 0.84637860 -1.40252142 1.80405406 1.89156559 6.21700661
151 152 153 154 155 156
-3.26033547 -2.29054968 3.91973104 1.09236813 -3.72861945 -2.82215008
157 158 159 160 161 162
0.38073033 -3.78795932 -2.58829052 1.31321291 3.87535634 -2.12477343
163 164 165 166 167 168
-7.05326993 -3.47727410 -2.26631293 -1.01887654 -8.58824109 4.84879832
169 170 171 172 173 174
0.40036792 7.21640011 0.88586659 5.78644869 -2.64251087 4.50060299
175 176 177 178 179 180
-1.51479645 -1.25790428 3.44512194 1.01176576 3.50840796 -4.96542034
181 182 183 184 185 186
4.84250999 -7.30642853 -2.93136632 1.34472181 7.10581667 -2.44736526
187 188 189 190 191 192
-2.41273098 -2.36406908 1.02111777 -1.08511849 -0.36419526 -0.47191766
193 194 195 196 197 198
0.97027384 1.35336739 -2.48589624 -0.23387215 -2.10460710 -8.09005882
199 200 201 202 203 204
1.92998264 1.57972000 -1.01860862 -0.86722425 -0.53479903 -0.48865454
205 206 207 208 209 210
3.21103312 0.13330657 1.69621415 2.43717225 -4.51942302 -3.12562948
211 212 213 214 215 216
0.68003153 -1.60174883 -1.93685949 5.36284557 1.94868176 -0.31130746
217 218 219 220 221 222
1.96901315 2.86710127 0.45666614 -1.68072421 -1.58114125 -2.31285450
223 224 225 226 227 228
-0.95900583 1.44646994 0.05376148 2.38466310 -1.82302449 -2.72987936
229 230 231 232 233 234
7.20586387 1.47121442 4.32009296 7.15274075 5.84124930 -3.27245256
235 236 237 238 239 240
5.89782728 -5.55902324 1.32217083 -2.35932780 1.61872849 -4.99514131
241 242 243 244 245 246
-2.51353576 4.21638956 -7.58432095 -5.15149200 -2.54707309 0.78876744
247 248 249 250 251 252
2.67766373 -0.08742669 4.04112034 0.63646174 -0.58824109 -1.45529640
253 254 255 256 257 258
3.58166113 5.50645268 -4.82438434 -1.29320714 5.91254629 -4.07741768
259 260 261 262 263 264
4.18839353 0.99017368 -9.20765349 3.61088954 4.19293280 -0.61178486
265 266 267 268 269 270
0.67230728 0.03232260 2.52068202 -1.65804008 0.06863368 0.32960872
271 272 273 274 275 276
-3.92095625 3.18614135 -0.37630557 -5.83267698 -1.46008314 -2.59715983
277 278 279 280 281 282
3.82628979 1.79360720 -0.59621677 2.78288018 5.05652337 -0.36159712
283 284 285 286 287 288
-4.07400237 -9.93284020 1.92522989 1.72532126 4.58581687 -3.04168685
> postscript(file="/var/fisher/rcomp/tmp/6tbcj1355145401.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 5.06713529 NA
1 5.09639072 5.06713529
2 -5.48325075 5.09639072
3 -3.46448146 -5.48325075
4 -1.72681646 -3.46448146
5 3.22081601 -1.72681646
6 5.19008511 3.22081601
7 -1.29329652 5.19008511
8 1.27647267 -1.29329652
9 0.40999761 1.27647267
10 4.21533339 0.40999761
11 1.09310159 4.21533339
12 2.72056643 1.09310159
13 2.22415457 2.72056643
14 -3.42308881 2.22415457
15 -2.28480057 -3.42308881
16 2.11505585 -2.28480057
17 0.30906666 2.11505585
18 1.49764456 0.30906666
19 -1.74097065 1.49764456
20 -2.78795574 -1.74097065
21 -1.95896904 -2.78795574
22 1.75535391 -1.95896904
23 -0.04603574 1.75535391
24 4.58869627 -0.04603574
25 7.47922690 4.58869627
26 0.28155332 7.47922690
27 -2.79095852 0.28155332
28 -1.18176472 -2.79095852
29 0.01477832 -1.18176472
30 -2.01652795 0.01477832
31 -6.24553972 -2.01652795
32 4.05557924 -6.24553972
33 -2.17241272 4.05557924
34 1.43496612 -2.17241272
35 -1.50914688 1.43496612
36 -3.01794185 -1.50914688
37 1.88511221 -3.01794185
38 -4.39798215 1.88511221
39 0.44748108 -4.39798215
40 2.83140724 0.44748108
41 0.21492875 2.83140724
42 3.11193048 0.21492875
43 0.95791039 3.11193048
44 6.66230108 0.95791039
45 3.18428645 6.66230108
46 1.33472747 3.18428645
47 -1.50154882 1.33472747
48 -0.83271335 -1.50154882
49 4.49651005 -0.83271335
50 -4.62913706 4.49651005
51 -0.66283945 -4.62913706
52 -1.43385471 -0.66283945
53 -2.45056816 -1.43385471
54 -0.65503808 -2.45056816
55 1.98305593 -0.65503808
56 4.26370536 1.98305593
57 -5.67942672 4.26370536
58 1.28704649 -5.67942672
59 0.39337147 1.28704649
60 -1.01945490 0.39337147
61 3.04762466 -1.01945490
62 -0.85181145 3.04762466
63 -3.71556220 -0.85181145
64 0.81355927 -3.71556220
65 -1.45263547 0.81355927
66 0.69462716 -1.45263547
67 -1.91433761 0.69462716
68 2.24172407 -1.91433761
69 -1.55909718 2.24172407
70 3.16050576 -1.55909718
71 -5.32228404 3.16050576
72 -3.52349253 -5.32228404
73 -0.90550098 -3.52349253
74 2.53125865 -0.90550098
75 6.10833597 2.53125865
76 0.76275423 6.10833597
77 0.87839233 0.76275423
78 -4.89278310 0.87839233
79 5.53125865 -4.89278310
80 -3.06143518 5.53125865
81 -1.17241272 -3.06143518
82 2.60146359 -1.17241272
83 -2.50154882 2.60146359
84 -0.22737838 -2.50154882
85 0.75243185 -0.22737838
86 -1.62255880 0.75243185
87 -4.57036688 -1.62255880
88 -0.50004065 -4.57036688
89 -4.02807215 -0.50004065
90 2.34956079 -4.02807215
91 -3.16306071 2.34956079
92 0.59019389 -3.16306071
93 -2.89470080 0.59019389
94 3.17057578 -2.89470080
95 2.04241784 3.17057578
96 1.72371883 2.04241784
97 1.82015297 1.72371883
98 -3.70567937 1.82015297
99 2.30931902 -3.70567937
100 1.55792780 2.30931902
101 -0.98969505 1.55792780
102 0.32521040 -0.98969505
103 -4.21663950 0.32521040
104 6.95076243 -4.21663950
105 2.53926266 6.95076243
106 3.44557883 2.53926266
107 0.67166364 3.44557883
108 5.28622574 0.67166364
109 -4.67942672 5.28622574
110 -2.85648746 -4.67942672
111 -6.32500835 -2.85648746
112 2.95928672 -6.32500835
113 2.46768629 2.95928672
114 -4.10606661 2.46768629
115 -3.35058056 -4.10606661
116 -1.48878150 -3.35058056
117 2.38211720 -1.48878150
118 -2.75705411 2.38211720
119 -0.09002202 -2.75705411
120 1.51260537 -0.09002202
121 0.42773086 1.51260537
122 3.08924276 0.42773086
123 -2.92132051 3.08924276
124 4.94445209 -2.92132051
125 6.44284396 4.94445209
126 -2.82215008 6.44284396
127 1.67645527 -2.82215008
128 -2.55251892 1.67645527
129 -4.41779827 -2.55251892
130 3.63197612 -4.41779827
131 -5.21724320 3.63197612
132 0.39680221 -5.21724320
133 -0.02120396 0.39680221
134 -0.33306179 -0.02120396
135 -1.32902735 -0.33306179
136 -3.91351913 -1.32902735
137 1.03208490 -3.91351913
138 -3.46284223 1.03208490
139 -2.87503788 -3.46284223
140 -6.53589277 -2.87503788
141 -5.20029313 -6.53589277
142 1.35926801 -5.20029313
143 7.92176437 1.35926801
144 0.34268074 7.92176437
145 0.84637860 0.34268074
146 -1.40252142 0.84637860
147 1.80405406 -1.40252142
148 1.89156559 1.80405406
149 6.21700661 1.89156559
150 -3.26033547 6.21700661
151 -2.29054968 -3.26033547
152 3.91973104 -2.29054968
153 1.09236813 3.91973104
154 -3.72861945 1.09236813
155 -2.82215008 -3.72861945
156 0.38073033 -2.82215008
157 -3.78795932 0.38073033
158 -2.58829052 -3.78795932
159 1.31321291 -2.58829052
160 3.87535634 1.31321291
161 -2.12477343 3.87535634
162 -7.05326993 -2.12477343
163 -3.47727410 -7.05326993
164 -2.26631293 -3.47727410
165 -1.01887654 -2.26631293
166 -8.58824109 -1.01887654
167 4.84879832 -8.58824109
168 0.40036792 4.84879832
169 7.21640011 0.40036792
170 0.88586659 7.21640011
171 5.78644869 0.88586659
172 -2.64251087 5.78644869
173 4.50060299 -2.64251087
174 -1.51479645 4.50060299
175 -1.25790428 -1.51479645
176 3.44512194 -1.25790428
177 1.01176576 3.44512194
178 3.50840796 1.01176576
179 -4.96542034 3.50840796
180 4.84250999 -4.96542034
181 -7.30642853 4.84250999
182 -2.93136632 -7.30642853
183 1.34472181 -2.93136632
184 7.10581667 1.34472181
185 -2.44736526 7.10581667
186 -2.41273098 -2.44736526
187 -2.36406908 -2.41273098
188 1.02111777 -2.36406908
189 -1.08511849 1.02111777
190 -0.36419526 -1.08511849
191 -0.47191766 -0.36419526
192 0.97027384 -0.47191766
193 1.35336739 0.97027384
194 -2.48589624 1.35336739
195 -0.23387215 -2.48589624
196 -2.10460710 -0.23387215
197 -8.09005882 -2.10460710
198 1.92998264 -8.09005882
199 1.57972000 1.92998264
200 -1.01860862 1.57972000
201 -0.86722425 -1.01860862
202 -0.53479903 -0.86722425
203 -0.48865454 -0.53479903
204 3.21103312 -0.48865454
205 0.13330657 3.21103312
206 1.69621415 0.13330657
207 2.43717225 1.69621415
208 -4.51942302 2.43717225
209 -3.12562948 -4.51942302
210 0.68003153 -3.12562948
211 -1.60174883 0.68003153
212 -1.93685949 -1.60174883
213 5.36284557 -1.93685949
214 1.94868176 5.36284557
215 -0.31130746 1.94868176
216 1.96901315 -0.31130746
217 2.86710127 1.96901315
218 0.45666614 2.86710127
219 -1.68072421 0.45666614
220 -1.58114125 -1.68072421
221 -2.31285450 -1.58114125
222 -0.95900583 -2.31285450
223 1.44646994 -0.95900583
224 0.05376148 1.44646994
225 2.38466310 0.05376148
226 -1.82302449 2.38466310
227 -2.72987936 -1.82302449
228 7.20586387 -2.72987936
229 1.47121442 7.20586387
230 4.32009296 1.47121442
231 7.15274075 4.32009296
232 5.84124930 7.15274075
233 -3.27245256 5.84124930
234 5.89782728 -3.27245256
235 -5.55902324 5.89782728
236 1.32217083 -5.55902324
237 -2.35932780 1.32217083
238 1.61872849 -2.35932780
239 -4.99514131 1.61872849
240 -2.51353576 -4.99514131
241 4.21638956 -2.51353576
242 -7.58432095 4.21638956
243 -5.15149200 -7.58432095
244 -2.54707309 -5.15149200
245 0.78876744 -2.54707309
246 2.67766373 0.78876744
247 -0.08742669 2.67766373
248 4.04112034 -0.08742669
249 0.63646174 4.04112034
250 -0.58824109 0.63646174
251 -1.45529640 -0.58824109
252 3.58166113 -1.45529640
253 5.50645268 3.58166113
254 -4.82438434 5.50645268
255 -1.29320714 -4.82438434
256 5.91254629 -1.29320714
257 -4.07741768 5.91254629
258 4.18839353 -4.07741768
259 0.99017368 4.18839353
260 -9.20765349 0.99017368
261 3.61088954 -9.20765349
262 4.19293280 3.61088954
263 -0.61178486 4.19293280
264 0.67230728 -0.61178486
265 0.03232260 0.67230728
266 2.52068202 0.03232260
267 -1.65804008 2.52068202
268 0.06863368 -1.65804008
269 0.32960872 0.06863368
270 -3.92095625 0.32960872
271 3.18614135 -3.92095625
272 -0.37630557 3.18614135
273 -5.83267698 -0.37630557
274 -1.46008314 -5.83267698
275 -2.59715983 -1.46008314
276 3.82628979 -2.59715983
277 1.79360720 3.82628979
278 -0.59621677 1.79360720
279 2.78288018 -0.59621677
280 5.05652337 2.78288018
281 -0.36159712 5.05652337
282 -4.07400237 -0.36159712
283 -9.93284020 -4.07400237
284 1.92522989 -9.93284020
285 1.72532126 1.92522989
286 4.58581687 1.72532126
287 -3.04168685 4.58581687
288 NA -3.04168685
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.09639072 5.06713529
[2,] -5.48325075 5.09639072
[3,] -3.46448146 -5.48325075
[4,] -1.72681646 -3.46448146
[5,] 3.22081601 -1.72681646
[6,] 5.19008511 3.22081601
[7,] -1.29329652 5.19008511
[8,] 1.27647267 -1.29329652
[9,] 0.40999761 1.27647267
[10,] 4.21533339 0.40999761
[11,] 1.09310159 4.21533339
[12,] 2.72056643 1.09310159
[13,] 2.22415457 2.72056643
[14,] -3.42308881 2.22415457
[15,] -2.28480057 -3.42308881
[16,] 2.11505585 -2.28480057
[17,] 0.30906666 2.11505585
[18,] 1.49764456 0.30906666
[19,] -1.74097065 1.49764456
[20,] -2.78795574 -1.74097065
[21,] -1.95896904 -2.78795574
[22,] 1.75535391 -1.95896904
[23,] -0.04603574 1.75535391
[24,] 4.58869627 -0.04603574
[25,] 7.47922690 4.58869627
[26,] 0.28155332 7.47922690
[27,] -2.79095852 0.28155332
[28,] -1.18176472 -2.79095852
[29,] 0.01477832 -1.18176472
[30,] -2.01652795 0.01477832
[31,] -6.24553972 -2.01652795
[32,] 4.05557924 -6.24553972
[33,] -2.17241272 4.05557924
[34,] 1.43496612 -2.17241272
[35,] -1.50914688 1.43496612
[36,] -3.01794185 -1.50914688
[37,] 1.88511221 -3.01794185
[38,] -4.39798215 1.88511221
[39,] 0.44748108 -4.39798215
[40,] 2.83140724 0.44748108
[41,] 0.21492875 2.83140724
[42,] 3.11193048 0.21492875
[43,] 0.95791039 3.11193048
[44,] 6.66230108 0.95791039
[45,] 3.18428645 6.66230108
[46,] 1.33472747 3.18428645
[47,] -1.50154882 1.33472747
[48,] -0.83271335 -1.50154882
[49,] 4.49651005 -0.83271335
[50,] -4.62913706 4.49651005
[51,] -0.66283945 -4.62913706
[52,] -1.43385471 -0.66283945
[53,] -2.45056816 -1.43385471
[54,] -0.65503808 -2.45056816
[55,] 1.98305593 -0.65503808
[56,] 4.26370536 1.98305593
[57,] -5.67942672 4.26370536
[58,] 1.28704649 -5.67942672
[59,] 0.39337147 1.28704649
[60,] -1.01945490 0.39337147
[61,] 3.04762466 -1.01945490
[62,] -0.85181145 3.04762466
[63,] -3.71556220 -0.85181145
[64,] 0.81355927 -3.71556220
[65,] -1.45263547 0.81355927
[66,] 0.69462716 -1.45263547
[67,] -1.91433761 0.69462716
[68,] 2.24172407 -1.91433761
[69,] -1.55909718 2.24172407
[70,] 3.16050576 -1.55909718
[71,] -5.32228404 3.16050576
[72,] -3.52349253 -5.32228404
[73,] -0.90550098 -3.52349253
[74,] 2.53125865 -0.90550098
[75,] 6.10833597 2.53125865
[76,] 0.76275423 6.10833597
[77,] 0.87839233 0.76275423
[78,] -4.89278310 0.87839233
[79,] 5.53125865 -4.89278310
[80,] -3.06143518 5.53125865
[81,] -1.17241272 -3.06143518
[82,] 2.60146359 -1.17241272
[83,] -2.50154882 2.60146359
[84,] -0.22737838 -2.50154882
[85,] 0.75243185 -0.22737838
[86,] -1.62255880 0.75243185
[87,] -4.57036688 -1.62255880
[88,] -0.50004065 -4.57036688
[89,] -4.02807215 -0.50004065
[90,] 2.34956079 -4.02807215
[91,] -3.16306071 2.34956079
[92,] 0.59019389 -3.16306071
[93,] -2.89470080 0.59019389
[94,] 3.17057578 -2.89470080
[95,] 2.04241784 3.17057578
[96,] 1.72371883 2.04241784
[97,] 1.82015297 1.72371883
[98,] -3.70567937 1.82015297
[99,] 2.30931902 -3.70567937
[100,] 1.55792780 2.30931902
[101,] -0.98969505 1.55792780
[102,] 0.32521040 -0.98969505
[103,] -4.21663950 0.32521040
[104,] 6.95076243 -4.21663950
[105,] 2.53926266 6.95076243
[106,] 3.44557883 2.53926266
[107,] 0.67166364 3.44557883
[108,] 5.28622574 0.67166364
[109,] -4.67942672 5.28622574
[110,] -2.85648746 -4.67942672
[111,] -6.32500835 -2.85648746
[112,] 2.95928672 -6.32500835
[113,] 2.46768629 2.95928672
[114,] -4.10606661 2.46768629
[115,] -3.35058056 -4.10606661
[116,] -1.48878150 -3.35058056
[117,] 2.38211720 -1.48878150
[118,] -2.75705411 2.38211720
[119,] -0.09002202 -2.75705411
[120,] 1.51260537 -0.09002202
[121,] 0.42773086 1.51260537
[122,] 3.08924276 0.42773086
[123,] -2.92132051 3.08924276
[124,] 4.94445209 -2.92132051
[125,] 6.44284396 4.94445209
[126,] -2.82215008 6.44284396
[127,] 1.67645527 -2.82215008
[128,] -2.55251892 1.67645527
[129,] -4.41779827 -2.55251892
[130,] 3.63197612 -4.41779827
[131,] -5.21724320 3.63197612
[132,] 0.39680221 -5.21724320
[133,] -0.02120396 0.39680221
[134,] -0.33306179 -0.02120396
[135,] -1.32902735 -0.33306179
[136,] -3.91351913 -1.32902735
[137,] 1.03208490 -3.91351913
[138,] -3.46284223 1.03208490
[139,] -2.87503788 -3.46284223
[140,] -6.53589277 -2.87503788
[141,] -5.20029313 -6.53589277
[142,] 1.35926801 -5.20029313
[143,] 7.92176437 1.35926801
[144,] 0.34268074 7.92176437
[145,] 0.84637860 0.34268074
[146,] -1.40252142 0.84637860
[147,] 1.80405406 -1.40252142
[148,] 1.89156559 1.80405406
[149,] 6.21700661 1.89156559
[150,] -3.26033547 6.21700661
[151,] -2.29054968 -3.26033547
[152,] 3.91973104 -2.29054968
[153,] 1.09236813 3.91973104
[154,] -3.72861945 1.09236813
[155,] -2.82215008 -3.72861945
[156,] 0.38073033 -2.82215008
[157,] -3.78795932 0.38073033
[158,] -2.58829052 -3.78795932
[159,] 1.31321291 -2.58829052
[160,] 3.87535634 1.31321291
[161,] -2.12477343 3.87535634
[162,] -7.05326993 -2.12477343
[163,] -3.47727410 -7.05326993
[164,] -2.26631293 -3.47727410
[165,] -1.01887654 -2.26631293
[166,] -8.58824109 -1.01887654
[167,] 4.84879832 -8.58824109
[168,] 0.40036792 4.84879832
[169,] 7.21640011 0.40036792
[170,] 0.88586659 7.21640011
[171,] 5.78644869 0.88586659
[172,] -2.64251087 5.78644869
[173,] 4.50060299 -2.64251087
[174,] -1.51479645 4.50060299
[175,] -1.25790428 -1.51479645
[176,] 3.44512194 -1.25790428
[177,] 1.01176576 3.44512194
[178,] 3.50840796 1.01176576
[179,] -4.96542034 3.50840796
[180,] 4.84250999 -4.96542034
[181,] -7.30642853 4.84250999
[182,] -2.93136632 -7.30642853
[183,] 1.34472181 -2.93136632
[184,] 7.10581667 1.34472181
[185,] -2.44736526 7.10581667
[186,] -2.41273098 -2.44736526
[187,] -2.36406908 -2.41273098
[188,] 1.02111777 -2.36406908
[189,] -1.08511849 1.02111777
[190,] -0.36419526 -1.08511849
[191,] -0.47191766 -0.36419526
[192,] 0.97027384 -0.47191766
[193,] 1.35336739 0.97027384
[194,] -2.48589624 1.35336739
[195,] -0.23387215 -2.48589624
[196,] -2.10460710 -0.23387215
[197,] -8.09005882 -2.10460710
[198,] 1.92998264 -8.09005882
[199,] 1.57972000 1.92998264
[200,] -1.01860862 1.57972000
[201,] -0.86722425 -1.01860862
[202,] -0.53479903 -0.86722425
[203,] -0.48865454 -0.53479903
[204,] 3.21103312 -0.48865454
[205,] 0.13330657 3.21103312
[206,] 1.69621415 0.13330657
[207,] 2.43717225 1.69621415
[208,] -4.51942302 2.43717225
[209,] -3.12562948 -4.51942302
[210,] 0.68003153 -3.12562948
[211,] -1.60174883 0.68003153
[212,] -1.93685949 -1.60174883
[213,] 5.36284557 -1.93685949
[214,] 1.94868176 5.36284557
[215,] -0.31130746 1.94868176
[216,] 1.96901315 -0.31130746
[217,] 2.86710127 1.96901315
[218,] 0.45666614 2.86710127
[219,] -1.68072421 0.45666614
[220,] -1.58114125 -1.68072421
[221,] -2.31285450 -1.58114125
[222,] -0.95900583 -2.31285450
[223,] 1.44646994 -0.95900583
[224,] 0.05376148 1.44646994
[225,] 2.38466310 0.05376148
[226,] -1.82302449 2.38466310
[227,] -2.72987936 -1.82302449
[228,] 7.20586387 -2.72987936
[229,] 1.47121442 7.20586387
[230,] 4.32009296 1.47121442
[231,] 7.15274075 4.32009296
[232,] 5.84124930 7.15274075
[233,] -3.27245256 5.84124930
[234,] 5.89782728 -3.27245256
[235,] -5.55902324 5.89782728
[236,] 1.32217083 -5.55902324
[237,] -2.35932780 1.32217083
[238,] 1.61872849 -2.35932780
[239,] -4.99514131 1.61872849
[240,] -2.51353576 -4.99514131
[241,] 4.21638956 -2.51353576
[242,] -7.58432095 4.21638956
[243,] -5.15149200 -7.58432095
[244,] -2.54707309 -5.15149200
[245,] 0.78876744 -2.54707309
[246,] 2.67766373 0.78876744
[247,] -0.08742669 2.67766373
[248,] 4.04112034 -0.08742669
[249,] 0.63646174 4.04112034
[250,] -0.58824109 0.63646174
[251,] -1.45529640 -0.58824109
[252,] 3.58166113 -1.45529640
[253,] 5.50645268 3.58166113
[254,] -4.82438434 5.50645268
[255,] -1.29320714 -4.82438434
[256,] 5.91254629 -1.29320714
[257,] -4.07741768 5.91254629
[258,] 4.18839353 -4.07741768
[259,] 0.99017368 4.18839353
[260,] -9.20765349 0.99017368
[261,] 3.61088954 -9.20765349
[262,] 4.19293280 3.61088954
[263,] -0.61178486 4.19293280
[264,] 0.67230728 -0.61178486
[265,] 0.03232260 0.67230728
[266,] 2.52068202 0.03232260
[267,] -1.65804008 2.52068202
[268,] 0.06863368 -1.65804008
[269,] 0.32960872 0.06863368
[270,] -3.92095625 0.32960872
[271,] 3.18614135 -3.92095625
[272,] -0.37630557 3.18614135
[273,] -5.83267698 -0.37630557
[274,] -1.46008314 -5.83267698
[275,] -2.59715983 -1.46008314
[276,] 3.82628979 -2.59715983
[277,] 1.79360720 3.82628979
[278,] -0.59621677 1.79360720
[279,] 2.78288018 -0.59621677
[280,] 5.05652337 2.78288018
[281,] -0.36159712 5.05652337
[282,] -4.07400237 -0.36159712
[283,] -9.93284020 -4.07400237
[284,] 1.92522989 -9.93284020
[285,] 1.72532126 1.92522989
[286,] 4.58581687 1.72532126
[287,] -3.04168685 4.58581687
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.09639072 5.06713529
2 -5.48325075 5.09639072
3 -3.46448146 -5.48325075
4 -1.72681646 -3.46448146
5 3.22081601 -1.72681646
6 5.19008511 3.22081601
7 -1.29329652 5.19008511
8 1.27647267 -1.29329652
9 0.40999761 1.27647267
10 4.21533339 0.40999761
11 1.09310159 4.21533339
12 2.72056643 1.09310159
13 2.22415457 2.72056643
14 -3.42308881 2.22415457
15 -2.28480057 -3.42308881
16 2.11505585 -2.28480057
17 0.30906666 2.11505585
18 1.49764456 0.30906666
19 -1.74097065 1.49764456
20 -2.78795574 -1.74097065
21 -1.95896904 -2.78795574
22 1.75535391 -1.95896904
23 -0.04603574 1.75535391
24 4.58869627 -0.04603574
25 7.47922690 4.58869627
26 0.28155332 7.47922690
27 -2.79095852 0.28155332
28 -1.18176472 -2.79095852
29 0.01477832 -1.18176472
30 -2.01652795 0.01477832
31 -6.24553972 -2.01652795
32 4.05557924 -6.24553972
33 -2.17241272 4.05557924
34 1.43496612 -2.17241272
35 -1.50914688 1.43496612
36 -3.01794185 -1.50914688
37 1.88511221 -3.01794185
38 -4.39798215 1.88511221
39 0.44748108 -4.39798215
40 2.83140724 0.44748108
41 0.21492875 2.83140724
42 3.11193048 0.21492875
43 0.95791039 3.11193048
44 6.66230108 0.95791039
45 3.18428645 6.66230108
46 1.33472747 3.18428645
47 -1.50154882 1.33472747
48 -0.83271335 -1.50154882
49 4.49651005 -0.83271335
50 -4.62913706 4.49651005
51 -0.66283945 -4.62913706
52 -1.43385471 -0.66283945
53 -2.45056816 -1.43385471
54 -0.65503808 -2.45056816
55 1.98305593 -0.65503808
56 4.26370536 1.98305593
57 -5.67942672 4.26370536
58 1.28704649 -5.67942672
59 0.39337147 1.28704649
60 -1.01945490 0.39337147
61 3.04762466 -1.01945490
62 -0.85181145 3.04762466
63 -3.71556220 -0.85181145
64 0.81355927 -3.71556220
65 -1.45263547 0.81355927
66 0.69462716 -1.45263547
67 -1.91433761 0.69462716
68 2.24172407 -1.91433761
69 -1.55909718 2.24172407
70 3.16050576 -1.55909718
71 -5.32228404 3.16050576
72 -3.52349253 -5.32228404
73 -0.90550098 -3.52349253
74 2.53125865 -0.90550098
75 6.10833597 2.53125865
76 0.76275423 6.10833597
77 0.87839233 0.76275423
78 -4.89278310 0.87839233
79 5.53125865 -4.89278310
80 -3.06143518 5.53125865
81 -1.17241272 -3.06143518
82 2.60146359 -1.17241272
83 -2.50154882 2.60146359
84 -0.22737838 -2.50154882
85 0.75243185 -0.22737838
86 -1.62255880 0.75243185
87 -4.57036688 -1.62255880
88 -0.50004065 -4.57036688
89 -4.02807215 -0.50004065
90 2.34956079 -4.02807215
91 -3.16306071 2.34956079
92 0.59019389 -3.16306071
93 -2.89470080 0.59019389
94 3.17057578 -2.89470080
95 2.04241784 3.17057578
96 1.72371883 2.04241784
97 1.82015297 1.72371883
98 -3.70567937 1.82015297
99 2.30931902 -3.70567937
100 1.55792780 2.30931902
101 -0.98969505 1.55792780
102 0.32521040 -0.98969505
103 -4.21663950 0.32521040
104 6.95076243 -4.21663950
105 2.53926266 6.95076243
106 3.44557883 2.53926266
107 0.67166364 3.44557883
108 5.28622574 0.67166364
109 -4.67942672 5.28622574
110 -2.85648746 -4.67942672
111 -6.32500835 -2.85648746
112 2.95928672 -6.32500835
113 2.46768629 2.95928672
114 -4.10606661 2.46768629
115 -3.35058056 -4.10606661
116 -1.48878150 -3.35058056
117 2.38211720 -1.48878150
118 -2.75705411 2.38211720
119 -0.09002202 -2.75705411
120 1.51260537 -0.09002202
121 0.42773086 1.51260537
122 3.08924276 0.42773086
123 -2.92132051 3.08924276
124 4.94445209 -2.92132051
125 6.44284396 4.94445209
126 -2.82215008 6.44284396
127 1.67645527 -2.82215008
128 -2.55251892 1.67645527
129 -4.41779827 -2.55251892
130 3.63197612 -4.41779827
131 -5.21724320 3.63197612
132 0.39680221 -5.21724320
133 -0.02120396 0.39680221
134 -0.33306179 -0.02120396
135 -1.32902735 -0.33306179
136 -3.91351913 -1.32902735
137 1.03208490 -3.91351913
138 -3.46284223 1.03208490
139 -2.87503788 -3.46284223
140 -6.53589277 -2.87503788
141 -5.20029313 -6.53589277
142 1.35926801 -5.20029313
143 7.92176437 1.35926801
144 0.34268074 7.92176437
145 0.84637860 0.34268074
146 -1.40252142 0.84637860
147 1.80405406 -1.40252142
148 1.89156559 1.80405406
149 6.21700661 1.89156559
150 -3.26033547 6.21700661
151 -2.29054968 -3.26033547
152 3.91973104 -2.29054968
153 1.09236813 3.91973104
154 -3.72861945 1.09236813
155 -2.82215008 -3.72861945
156 0.38073033 -2.82215008
157 -3.78795932 0.38073033
158 -2.58829052 -3.78795932
159 1.31321291 -2.58829052
160 3.87535634 1.31321291
161 -2.12477343 3.87535634
162 -7.05326993 -2.12477343
163 -3.47727410 -7.05326993
164 -2.26631293 -3.47727410
165 -1.01887654 -2.26631293
166 -8.58824109 -1.01887654
167 4.84879832 -8.58824109
168 0.40036792 4.84879832
169 7.21640011 0.40036792
170 0.88586659 7.21640011
171 5.78644869 0.88586659
172 -2.64251087 5.78644869
173 4.50060299 -2.64251087
174 -1.51479645 4.50060299
175 -1.25790428 -1.51479645
176 3.44512194 -1.25790428
177 1.01176576 3.44512194
178 3.50840796 1.01176576
179 -4.96542034 3.50840796
180 4.84250999 -4.96542034
181 -7.30642853 4.84250999
182 -2.93136632 -7.30642853
183 1.34472181 -2.93136632
184 7.10581667 1.34472181
185 -2.44736526 7.10581667
186 -2.41273098 -2.44736526
187 -2.36406908 -2.41273098
188 1.02111777 -2.36406908
189 -1.08511849 1.02111777
190 -0.36419526 -1.08511849
191 -0.47191766 -0.36419526
192 0.97027384 -0.47191766
193 1.35336739 0.97027384
194 -2.48589624 1.35336739
195 -0.23387215 -2.48589624
196 -2.10460710 -0.23387215
197 -8.09005882 -2.10460710
198 1.92998264 -8.09005882
199 1.57972000 1.92998264
200 -1.01860862 1.57972000
201 -0.86722425 -1.01860862
202 -0.53479903 -0.86722425
203 -0.48865454 -0.53479903
204 3.21103312 -0.48865454
205 0.13330657 3.21103312
206 1.69621415 0.13330657
207 2.43717225 1.69621415
208 -4.51942302 2.43717225
209 -3.12562948 -4.51942302
210 0.68003153 -3.12562948
211 -1.60174883 0.68003153
212 -1.93685949 -1.60174883
213 5.36284557 -1.93685949
214 1.94868176 5.36284557
215 -0.31130746 1.94868176
216 1.96901315 -0.31130746
217 2.86710127 1.96901315
218 0.45666614 2.86710127
219 -1.68072421 0.45666614
220 -1.58114125 -1.68072421
221 -2.31285450 -1.58114125
222 -0.95900583 -2.31285450
223 1.44646994 -0.95900583
224 0.05376148 1.44646994
225 2.38466310 0.05376148
226 -1.82302449 2.38466310
227 -2.72987936 -1.82302449
228 7.20586387 -2.72987936
229 1.47121442 7.20586387
230 4.32009296 1.47121442
231 7.15274075 4.32009296
232 5.84124930 7.15274075
233 -3.27245256 5.84124930
234 5.89782728 -3.27245256
235 -5.55902324 5.89782728
236 1.32217083 -5.55902324
237 -2.35932780 1.32217083
238 1.61872849 -2.35932780
239 -4.99514131 1.61872849
240 -2.51353576 -4.99514131
241 4.21638956 -2.51353576
242 -7.58432095 4.21638956
243 -5.15149200 -7.58432095
244 -2.54707309 -5.15149200
245 0.78876744 -2.54707309
246 2.67766373 0.78876744
247 -0.08742669 2.67766373
248 4.04112034 -0.08742669
249 0.63646174 4.04112034
250 -0.58824109 0.63646174
251 -1.45529640 -0.58824109
252 3.58166113 -1.45529640
253 5.50645268 3.58166113
254 -4.82438434 5.50645268
255 -1.29320714 -4.82438434
256 5.91254629 -1.29320714
257 -4.07741768 5.91254629
258 4.18839353 -4.07741768
259 0.99017368 4.18839353
260 -9.20765349 0.99017368
261 3.61088954 -9.20765349
262 4.19293280 3.61088954
263 -0.61178486 4.19293280
264 0.67230728 -0.61178486
265 0.03232260 0.67230728
266 2.52068202 0.03232260
267 -1.65804008 2.52068202
268 0.06863368 -1.65804008
269 0.32960872 0.06863368
270 -3.92095625 0.32960872
271 3.18614135 -3.92095625
272 -0.37630557 3.18614135
273 -5.83267698 -0.37630557
274 -1.46008314 -5.83267698
275 -2.59715983 -1.46008314
276 3.82628979 -2.59715983
277 1.79360720 3.82628979
278 -0.59621677 1.79360720
279 2.78288018 -0.59621677
280 5.05652337 2.78288018
281 -0.36159712 5.05652337
282 -4.07400237 -0.36159712
283 -9.93284020 -4.07400237
284 1.92522989 -9.93284020
285 1.72532126 1.92522989
286 4.58581687 1.72532126
287 -3.04168685 4.58581687
> 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/79rg11355145401.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/8a4jw1355145401.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/9wz7d1355145401.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/100fm41355145401.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11t6da1355145401.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12pnte1355145401.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13h0pi1355145401.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14s6pw1355145401.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/15eyy91355145401.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/16cvrk1355145401.tab")
+ }
>
> try(system("convert tmp/1mzbw1355145401.ps tmp/1mzbw1355145401.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ixu41355145401.ps tmp/2ixu41355145401.png",intern=TRUE))
character(0)
> try(system("convert tmp/3h23y1355145401.ps tmp/3h23y1355145401.png",intern=TRUE))
character(0)
> try(system("convert tmp/4l6is1355145401.ps tmp/4l6is1355145401.png",intern=TRUE))
character(0)
> try(system("convert tmp/54nk61355145401.ps tmp/54nk61355145401.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tbcj1355145401.ps tmp/6tbcj1355145401.png",intern=TRUE))
character(0)
> try(system("convert tmp/79rg11355145401.ps tmp/79rg11355145401.png",intern=TRUE))
character(0)
> try(system("convert tmp/8a4jw1355145401.ps tmp/8a4jw1355145401.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wz7d1355145401.ps tmp/9wz7d1355145401.png",intern=TRUE))
character(0)
> try(system("convert tmp/100fm41355145401.ps tmp/100fm41355145401.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.039 1.586 13.618