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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+ ,0
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+ ,1
+ ,0
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+ ,9
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+ ,0
+ ,0
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+ ,6
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+ ,22
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+ ,1
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+ ,0
+ ,0
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+ ,36
+ ,0
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+ ,42
+ ,1
+ ,0
+ ,14
+ ,12
+ ,14
+ ,33
+ ,30
+ ,1
+ ,0)
+ ,dim=c(7
+ ,288)
+ ,dimnames=list(c('Happiness'
+ ,'Software'
+ ,'Learning'
+ ,'Separate'
+ ,'Connected'
+ ,'Gender'
+ ,'Pop')
+ ,1:288))
> y <- array(NA,dim=c(7,288),dimnames=list(c('Happiness','Software','Learning','Separate','Connected','Gender','Pop'),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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Happiness Software Learning Separate Connected Gender Pop
1 14 12 13 38 41 1 1
2 18 11 16 32 39 1 1
3 11 15 19 35 30 1 1
4 12 6 15 33 31 0 1
5 16 13 14 37 34 1 1
6 18 10 13 29 35 1 1
7 14 12 19 31 39 1 1
8 14 14 15 36 34 1 1
9 15 12 14 35 36 1 1
10 15 9 15 38 37 1 1
11 17 10 16 31 38 0 1
12 19 12 16 34 36 1 1
13 10 12 16 35 38 0 1
14 16 11 16 38 39 1 1
15 18 15 17 37 33 1 1
16 14 12 15 33 32 0 1
17 14 10 15 32 36 0 1
18 17 12 20 38 38 1 1
19 14 11 18 38 39 0 1
20 16 12 16 32 32 1 1
21 18 11 16 33 32 0 1
22 11 12 16 31 31 1 1
23 14 13 19 38 39 1 1
24 12 11 16 39 37 1 1
25 17 12 17 32 39 0 1
26 9 13 17 32 41 1 1
27 16 10 16 35 36 0 1
28 14 14 15 37 33 1 1
29 15 12 16 33 33 1 1
30 11 10 14 33 34 0 1
31 16 12 15 31 31 1 1
32 13 8 12 32 27 0 1
33 17 10 14 31 37 1 1
34 15 12 16 37 34 1 1
35 14 12 14 30 34 0 1
36 16 7 10 33 32 0 1
37 9 9 10 31 29 0 1
38 15 12 14 33 36 0 1
39 17 10 16 31 29 1 1
40 13 10 16 33 35 0 1
41 15 10 16 32 37 0 1
42 16 12 14 33 34 1 1
43 16 15 20 32 38 0 1
44 12 10 14 33 35 0 1
45 15 10 14 28 38 1 1
46 11 12 11 35 37 1 1
47 15 13 14 39 38 1 1
48 15 11 15 34 33 1 1
49 17 11 16 38 36 1 1
50 13 12 14 32 38 0 1
51 16 14 16 38 32 1 1
52 14 10 14 30 32 0 1
53 11 12 12 33 32 0 1
54 12 13 16 38 34 1 1
55 12 5 9 32 32 0 1
56 15 6 14 35 37 1 1
57 16 12 16 34 39 1 1
58 15 12 16 34 29 1 1
59 12 11 15 36 37 0 1
60 12 10 16 34 35 1 1
61 8 7 12 28 30 0 1
62 13 12 16 34 38 0 1
63 11 14 16 35 34 1 1
64 14 11 14 35 31 1 1
65 15 12 16 31 34 1 1
66 10 13 17 37 35 0 1
67 11 14 18 35 36 1 1
68 12 11 18 27 30 0 1
69 15 12 12 40 39 1 1
70 15 12 16 37 35 0 1
71 14 8 10 36 38 0 1
72 16 11 14 38 31 1 1
73 15 14 18 39 34 1 1
74 15 14 18 41 38 0 1
75 13 12 16 27 34 0 1
76 12 9 17 30 39 1 1
77 17 13 16 37 37 1 1
78 13 11 16 31 34 1 1
79 15 12 13 31 28 0 1
80 13 12 16 27 37 0 1
81 15 12 16 36 33 0 1
82 15 12 16 37 35 1 1
83 16 12 15 33 37 0 1
84 15 11 15 34 32 1 1
85 14 10 16 31 33 1 1
86 15 9 14 39 38 0 1
87 14 12 16 34 33 1 1
88 13 12 16 32 29 1 1
89 7 12 15 33 33 1 1
90 17 9 12 36 31 1 1
91 13 15 17 32 36 1 1
92 15 12 16 41 35 1 1
93 14 12 15 28 32 1 1
94 13 12 13 30 29 1 1
95 16 10 16 36 39 1 1
96 12 13 16 35 37 1 1
97 14 9 16 31 35 1 1
98 17 12 16 34 37 0 1
99 15 10 14 36 32 0 1
100 17 14 16 36 38 1 1
101 12 11 16 35 37 0 1
102 16 15 20 37 36 1 1
103 11 11 15 28 32 0 1
104 15 11 16 39 33 1 1
105 9 12 13 32 40 0 1
106 16 12 17 35 38 1 1
107 15 12 16 39 41 0 1
108 10 11 16 35 36 0 1
109 10 7 12 42 43 1 1
110 15 12 16 34 30 1 1
111 11 14 16 33 31 1 1
112 13 11 17 41 32 1 1
113 18 10 12 34 37 1 1
114 16 13 18 32 37 0 1
115 14 13 14 40 33 1 1
116 14 8 14 40 34 1 1
117 14 11 13 35 33 1 1
118 14 12 16 36 38 1 1
119 12 11 13 37 33 0 1
120 14 13 16 27 31 1 1
121 15 12 13 39 38 1 1
122 15 14 16 38 37 1 1
123 15 13 15 31 36 1 1
124 13 15 16 33 31 1 1
125 17 10 15 32 39 0 1
126 17 11 17 39 44 1 1
127 19 9 15 36 33 1 1
128 15 11 12 33 35 1 1
129 13 10 16 33 32 0 1
130 9 11 10 32 28 0 1
131 15 8 16 37 40 1 1
132 15 11 12 30 27 0 1
133 15 12 14 38 37 0 1
134 16 12 15 29 32 1 1
135 11 9 13 22 28 0 1
136 14 11 15 35 34 0 1
137 11 10 11 35 30 1 1
138 15 8 12 34 35 1 1
139 13 9 11 35 31 0 1
140 15 8 16 34 32 1 1
141 16 9 15 37 30 0 1
142 14 15 17 35 30 1 1
143 15 11 16 23 31 0 1
144 16 8 10 31 40 1 1
145 16 13 18 27 32 1 1
146 11 12 13 36 36 0 1
147 12 12 16 31 32 0 1
148 9 9 13 32 35 0 1
149 16 7 10 39 38 1 1
150 13 13 15 37 42 1 1
151 16 9 16 38 34 0 1
152 12 6 16 39 35 1 1
153 9 8 14 34 38 1 1
154 13 8 10 31 33 1 1
155 14 6 13 37 32 1 1
156 19 9 15 36 33 1 1
157 13 11 16 32 34 1 1
158 12 8 12 38 32 1 1
159 10 10 13 26 27 0 0
160 14 8 12 26 31 0 0
161 16 14 17 33 38 0 0
162 10 10 15 39 34 1 0
163 11 8 10 30 24 0 0
164 14 11 14 33 30 0 0
165 12 12 11 25 26 1 0
166 9 12 13 38 34 1 0
167 9 12 16 37 27 0 0
168 11 5 12 31 37 0 0
169 16 12 16 37 36 1 0
170 9 10 12 35 41 0 0
171 13 7 9 25 29 1 0
172 16 12 12 28 36 1 0
173 13 11 15 35 32 0 0
174 9 8 12 33 37 1 0
175 12 9 12 30 30 0 0
176 16 10 14 31 31 1 0
177 11 9 12 37 38 1 0
178 14 12 16 36 36 1 0
179 13 6 11 30 35 0 0
180 15 15 19 36 31 0 0
181 14 12 15 32 38 0 0
182 16 12 8 28 22 1 0
183 13 12 16 36 32 1 0
184 14 11 17 34 36 0 0
185 15 7 12 31 39 1 0
186 13 7 11 28 28 0 0
187 11 5 11 36 32 0 0
188 11 12 14 36 32 1 0
189 14 12 16 40 38 1 0
190 15 3 12 33 32 1 0
191 11 11 16 37 35 1 0
192 15 10 13 32 32 1 0
193 12 12 15 38 37 0 0
194 14 9 16 31 34 1 0
195 14 12 16 37 33 1 0
196 8 9 14 33 33 0 0
197 9 12 16 30 30 0 0
198 15 10 14 30 24 0 0
199 17 9 11 31 34 0 0
200 13 12 12 32 34 0 0
201 15 8 15 34 33 1 0
202 15 11 15 36 34 1 0
203 14 11 16 37 35 1 0
204 16 12 16 36 35 0 0
205 13 10 11 33 36 0 0
206 16 10 15 33 34 0 0
207 9 12 12 33 34 1 0
208 16 12 12 44 41 0 0
209 11 11 15 39 32 0 0
210 10 8 15 32 30 0 0
211 11 12 16 35 35 1 0
212 15 10 14 25 28 0 0
213 17 11 17 35 33 1 0
214 14 10 14 34 39 1 0
215 8 8 13 35 36 0 0
216 15 12 15 39 36 1 0
217 11 12 13 33 35 0 0
218 16 10 14 36 38 0 0
219 10 12 15 32 33 1 0
220 15 9 12 32 31 0 0
221 16 6 8 36 32 1 0
222 19 10 14 32 31 0 0
223 12 9 14 34 33 0 0
224 8 9 11 33 34 0 0
225 11 9 12 35 34 0 0
226 14 6 13 30 34 1 0
227 9 10 10 38 33 0 0
228 15 6 16 34 32 0 0
229 13 14 18 33 41 1 0
230 16 10 13 32 34 1 0
231 11 10 11 31 36 0 0
232 12 6 4 30 37 0 0
233 13 12 13 27 36 0 0
234 10 12 16 31 29 1 0
235 11 7 10 30 37 0 0
236 12 8 12 32 27 0 0
237 8 11 12 35 35 0 0
238 12 3 10 28 28 0 0
239 12 6 13 33 35 0 0
240 11 8 12 35 29 0 0
241 13 9 14 35 32 0 0
242 14 9 10 32 36 1 0
243 10 8 12 21 19 1 0
244 12 9 12 20 21 1 0
245 15 7 11 34 31 0 0
246 13 7 10 32 33 0 0
247 13 6 12 34 36 1 0
248 13 9 16 32 33 1 0
249 12 10 12 33 37 0 0
250 12 11 14 33 34 0 0
251 9 12 16 37 35 0 0
252 9 8 14 32 31 1 0
253 15 11 13 34 37 1 0
254 10 3 4 30 35 1 0
255 14 11 15 30 27 1 0
256 15 12 11 38 34 0 0
257 7 7 11 36 40 0 0
258 14 9 14 32 29 0 0
259 8 12 15 34 38 0 0
260 10 8 14 33 34 1 0
261 13 11 13 27 21 0 0
262 13 8 11 32 36 0 0
263 13 10 15 34 38 1 0
264 8 8 11 29 30 0 0
265 12 7 13 35 35 0 0
266 13 8 13 27 30 1 0
267 12 10 16 33 36 1 0
268 10 8 13 38 34 0 0
269 13 12 16 36 35 1 0
270 12 14 16 33 34 0 0
271 9 7 12 39 32 0 0
272 15 6 7 29 33 1 0
273 13 11 16 32 33 0 0
274 13 4 5 34 26 1 0
275 13 9 16 38 35 0 0
276 15 5 4 17 21 0 0
277 15 9 12 35 38 0 0
278 14 11 15 32 35 0 0
279 15 12 14 34 33 1 0
280 11 9 11 36 37 0 0
281 15 12 16 31 38 0 0
282 14 10 15 35 34 1 0
283 13 9 12 29 27 0 0
284 12 6 6 22 16 1 0
285 16 10 16 41 40 0 0
286 16 9 10 36 36 0 0
287 9 13 15 42 42 1 0
288 14 12 14 33 30 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Software Learning Separate Connected Gender
10.56678 -0.01610 0.11895 -0.01599 0.02730 0.79035
Pop
0.99814
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.3195 -1.4882 0.4788 1.6463 6.5943
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.56678 1.42810 7.399 1.59e-12 ***
Software -0.01610 0.07939 -0.203 0.83948
Learning 0.11895 0.07406 1.606 0.10937
Separate -0.01599 0.04295 -0.372 0.70991
Connected 0.02730 0.04130 0.661 0.50913
Gender 0.79035 0.28870 2.738 0.00658 **
Pop 0.99814 0.30798 3.241 0.00133 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.369 on 281 degrees of freedom
Multiple R-squared: 0.1167, Adjusted R-squared: 0.09789
F-statistic: 6.19 on 6 and 281 DF, p-value: 4.068e-06
> 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,] 0.35893020 0.71786039 0.64106980
[2,] 0.44873969 0.89747938 0.55126031
[3,] 0.67636287 0.64727427 0.32363713
[4,] 0.69597349 0.60805303 0.30402651
[5,] 0.63418501 0.73162999 0.36581499
[6,] 0.83022306 0.33955387 0.16977694
[7,] 0.77380653 0.45238694 0.22619347
[8,] 0.69832945 0.60334109 0.30167055
[9,] 0.71915372 0.56169257 0.28084628
[10,] 0.66600268 0.66799464 0.33399732
[11,] 0.59042677 0.81914645 0.40957323
[12,] 0.75460142 0.49079716 0.24539858
[13,] 0.86523117 0.26953767 0.13476883
[14,] 0.83159936 0.33680128 0.16840064
[15,] 0.83785534 0.32428931 0.16214466
[16,] 0.81685972 0.36628056 0.18314028
[17,] 0.96855152 0.06289696 0.03144848
[18,] 0.96083366 0.07833268 0.03916634
[19,] 0.94672417 0.10655166 0.05327583
[20,] 0.92840887 0.14318227 0.07159113
[21,] 0.94382718 0.11234565 0.05617282
[22,] 0.92913330 0.14173339 0.07086670
[23,] 0.91249933 0.17500135 0.08750067
[24,] 0.89822208 0.20355583 0.10177792
[25,] 0.87201769 0.25596461 0.12798231
[26,] 0.84162201 0.31675597 0.15837799
[27,] 0.82158866 0.35682268 0.17841134
[28,] 0.90046270 0.19907459 0.09953730
[29,] 0.88184604 0.23630792 0.11815396
[30,] 0.87340022 0.25319956 0.12659978
[31,] 0.85047163 0.29905675 0.14952837
[32,] 0.82199782 0.35600436 0.17800218
[33,] 0.79574948 0.40850103 0.20425052
[34,] 0.77857048 0.44285904 0.22142952
[35,] 0.76354660 0.47290681 0.23645340
[36,] 0.73126937 0.53746125 0.26873063
[37,] 0.75371369 0.49257261 0.24628631
[38,] 0.72085763 0.55828475 0.27914237
[39,] 0.68027912 0.63944175 0.31972088
[40,] 0.67252545 0.65494911 0.32747455
[41,] 0.63181528 0.73636944 0.36818472
[42,] 0.60794036 0.78411927 0.39205964
[43,] 0.56359535 0.87280929 0.43640465
[44,] 0.54445659 0.91108682 0.45554341
[45,] 0.54953698 0.90092605 0.45046302
[46,] 0.51544999 0.96910002 0.48455001
[47,] 0.47404604 0.94809208 0.52595396
[48,] 0.43720267 0.87440534 0.56279733
[49,] 0.39491763 0.78983527 0.60508237
[50,] 0.36922185 0.73844370 0.63077815
[51,] 0.40447445 0.80894889 0.59552555
[52,] 0.57562777 0.84874447 0.42437223
[53,] 0.53818791 0.92362419 0.46181209
[54,] 0.58857338 0.82285323 0.41142662
[55,] 0.54707519 0.90584962 0.45292481
[56,] 0.50595789 0.98808422 0.49404211
[57,] 0.55151082 0.89697835 0.44848918
[58,] 0.61879691 0.76240619 0.38120309
[59,] 0.60373981 0.79252037 0.39626019
[60,] 0.56989081 0.86021837 0.43010919
[61,] 0.55065146 0.89869708 0.44934854
[62,] 0.51577338 0.96845324 0.48422662
[63,] 0.50104131 0.99791738 0.49895869
[64,] 0.46200342 0.92400684 0.53799658
[65,] 0.43393413 0.86786825 0.56606587
[66,] 0.39642595 0.79285189 0.60357405
[67,] 0.43141730 0.86283461 0.56858270
[68,] 0.42899442 0.85798884 0.57100558
[69,] 0.40666775 0.81333550 0.59333225
[70,] 0.40219736 0.80439472 0.59780264
[71,] 0.36708516 0.73417032 0.63291484
[72,] 0.34376482 0.68752964 0.65623518
[73,] 0.30983244 0.61966488 0.69016756
[74,] 0.31095675 0.62191350 0.68904325
[75,] 0.28019753 0.56039506 0.71980247
[76,] 0.25002625 0.50005249 0.74997375
[77,] 0.22779077 0.45558154 0.77220923
[78,] 0.20109811 0.40219622 0.79890189
[79,] 0.18162897 0.36325794 0.81837103
[80,] 0.45115236 0.90230472 0.54884764
[81,] 0.47078979 0.94157959 0.52921021
[82,] 0.44548482 0.89096965 0.55451518
[83,] 0.41056999 0.82113999 0.58943001
[84,] 0.37586681 0.75173363 0.62413319
[85,] 0.34416551 0.68833101 0.65583449
[86,] 0.31793672 0.63587344 0.68206328
[87,] 0.32272404 0.64544807 0.67727596
[88,] 0.29229364 0.58458728 0.70770636
[89,] 0.31862170 0.63724339 0.68137830
[90,] 0.29841894 0.59683788 0.70158106
[91,] 0.30057794 0.60115587 0.69942206
[92,] 0.29058223 0.58116446 0.70941777
[93,] 0.26659057 0.53318113 0.73340943
[94,] 0.26290719 0.52581438 0.73709281
[95,] 0.23575556 0.47151112 0.76424444
[96,] 0.30976115 0.61952230 0.69023885
[97,] 0.28671217 0.57342434 0.71328783
[98,] 0.26127912 0.52255824 0.73872088
[99,] 0.31106348 0.62212695 0.68893652
[100,] 0.41444151 0.82888303 0.58555849
[101,] 0.38245824 0.76491649 0.61754176
[102,] 0.41297338 0.82594676 0.58702662
[103,] 0.39976152 0.79952304 0.60023848
[104,] 0.45828337 0.91656674 0.54171663
[105,] 0.44921229 0.89842457 0.55078771
[106,] 0.41533093 0.83066186 0.58466907
[107,] 0.38327994 0.76655987 0.61672006
[108,] 0.35029163 0.70058326 0.64970837
[109,] 0.32055326 0.64110653 0.67944674
[110,] 0.29864242 0.59728485 0.70135758
[111,] 0.27033218 0.54066435 0.72966782
[112,] 0.24518512 0.49037024 0.75481488
[113,] 0.21968157 0.43936313 0.78031843
[114,] 0.19685891 0.39371782 0.80314109
[115,] 0.17963955 0.35927910 0.82036045
[116,] 0.19884750 0.39769501 0.80115250
[117,] 0.19136899 0.38273798 0.80863101
[118,] 0.26521015 0.53042030 0.73478985
[119,] 0.24367866 0.48735733 0.75632134
[120,] 0.21876308 0.43752617 0.78123692
[121,] 0.24912784 0.49825567 0.75087216
[122,] 0.22544308 0.45088615 0.77455692
[123,] 0.22501796 0.45003592 0.77498204
[124,] 0.21102826 0.42205652 0.78897174
[125,] 0.20283938 0.40567875 0.79716062
[126,] 0.19694632 0.39389263 0.80305368
[127,] 0.17513781 0.35027562 0.82486219
[128,] 0.17906556 0.35813113 0.82093444
[129,] 0.16137248 0.32274496 0.83862752
[130,] 0.14131748 0.28263497 0.85868252
[131,] 0.12446343 0.24892686 0.87553657
[132,] 0.12902666 0.25805332 0.87097334
[133,] 0.11186986 0.22373971 0.88813014
[134,] 0.10399254 0.20798509 0.89600746
[135,] 0.10252795 0.20505590 0.89747205
[136,] 0.09577651 0.19155303 0.90422349
[137,] 0.09157718 0.18315435 0.90842282
[138,] 0.08210684 0.16421368 0.91789316
[139,] 0.11526413 0.23052826 0.88473587
[140,] 0.11414730 0.22829460 0.88585270
[141,] 0.10418923 0.20837846 0.89581077
[142,] 0.10592215 0.21184430 0.89407785
[143,] 0.11051263 0.22102527 0.88948737
[144,] 0.18940462 0.37880923 0.81059538
[145,] 0.17085241 0.34170483 0.82914759
[146,] 0.15021813 0.30043626 0.84978187
[147,] 0.22425537 0.44851075 0.77574463
[148,] 0.20404718 0.40809435 0.79595282
[149,] 0.18932694 0.37865388 0.81067306
[150,] 0.18403388 0.36806775 0.81596612
[151,] 0.17822278 0.35644557 0.82177722
[152,] 0.18211945 0.36423891 0.81788055
[153,] 0.21079790 0.42159580 0.78920210
[154,] 0.19021589 0.38043179 0.80978411
[155,] 0.17734649 0.35469297 0.82265351
[156,] 0.16010012 0.32020024 0.83989988
[157,] 0.20034862 0.40069725 0.79965138
[158,] 0.22468681 0.44937362 0.77531319
[159,] 0.20365618 0.40731235 0.79634382
[160,] 0.21310166 0.42620331 0.78689834
[161,] 0.23232685 0.46465370 0.76767315
[162,] 0.21121224 0.42242448 0.78878776
[163,] 0.22788650 0.45577300 0.77211350
[164,] 0.20418823 0.40837646 0.79581177
[165,] 0.24590348 0.49180696 0.75409652
[166,] 0.22084194 0.44168388 0.77915806
[167,] 0.23528454 0.47056908 0.76471546
[168,] 0.22452653 0.44905307 0.77547347
[169,] 0.20134225 0.40268450 0.79865775
[170,] 0.18114538 0.36229076 0.81885462
[171,] 0.17689703 0.35379406 0.82310297
[172,] 0.16187094 0.32374188 0.83812906
[173,] 0.19423282 0.38846563 0.80576718
[174,] 0.17123250 0.34246499 0.82876750
[175,] 0.15471001 0.30942002 0.84528999
[176,] 0.14599585 0.29199169 0.85400415
[177,] 0.12896473 0.25792945 0.87103527
[178,] 0.11531721 0.23063441 0.88468279
[179,] 0.11084970 0.22169940 0.88915030
[180,] 0.09619140 0.19238281 0.90380860
[181,] 0.09171580 0.18343160 0.90828420
[182,] 0.09060778 0.18121557 0.90939222
[183,] 0.08548082 0.17096163 0.91451918
[184,] 0.07289973 0.14579945 0.92710027
[185,] 0.06226340 0.12452679 0.93773660
[186,] 0.05281755 0.10563510 0.94718245
[187,] 0.08002638 0.16005276 0.91997362
[188,] 0.09942237 0.19884473 0.90057763
[189,] 0.10144820 0.20289641 0.89855180
[190,] 0.15522521 0.31045042 0.84477479
[191,] 0.13575117 0.27150234 0.86424883
[192,] 0.12738305 0.25476610 0.87261695
[193,] 0.11972493 0.23944986 0.88027507
[194,] 0.10440235 0.20880471 0.89559765
[195,] 0.11976043 0.23952086 0.88023957
[196,] 0.10343615 0.20687230 0.89656385
[197,] 0.12091884 0.24183768 0.87908116
[198,] 0.15521911 0.31043821 0.84478089
[199,] 0.19079029 0.38158058 0.80920971
[200,] 0.17308083 0.34616165 0.82691917
[201,] 0.17479064 0.34958127 0.82520936
[202,] 0.17091085 0.34182171 0.82908915
[203,] 0.17063239 0.34126478 0.82936761
[204,] 0.20734218 0.41468437 0.79265782
[205,] 0.18555760 0.37111520 0.81444240
[206,] 0.24821828 0.49643657 0.75178172
[207,] 0.24347273 0.48694547 0.75652727
[208,] 0.22225572 0.44451145 0.77774428
[209,] 0.26079280 0.52158559 0.73920720
[210,] 0.27827227 0.55654454 0.72172773
[211,] 0.28801537 0.57603075 0.71198463
[212,] 0.34084632 0.68169264 0.65915368
[213,] 0.61450754 0.77098492 0.38549246
[214,] 0.57468001 0.85063997 0.42531999
[215,] 0.65609018 0.68781963 0.34390982
[216,] 0.62469025 0.75061951 0.37530975
[217,] 0.59399781 0.81200437 0.40600219
[218,] 0.61797354 0.76405292 0.38202646
[219,] 0.65346629 0.69306742 0.34653371
[220,] 0.61435804 0.77128391 0.38564196
[221,] 0.65385644 0.69228713 0.34614356
[222,] 0.62946209 0.74107583 0.37053791
[223,] 0.59087108 0.81825784 0.40912892
[224,] 0.54673585 0.90652831 0.45326415
[225,] 0.56668433 0.86663135 0.43331567
[226,] 0.53410492 0.93179015 0.46589508
[227,] 0.48801958 0.97603917 0.51198042
[228,] 0.62688899 0.74622203 0.37311101
[229,] 0.58628983 0.82742034 0.41371017
[230,] 0.54345895 0.91308210 0.45654105
[231,] 0.50480871 0.99038258 0.49519129
[232,] 0.46425005 0.92850010 0.53574995
[233,] 0.42412497 0.84824995 0.57587503
[234,] 0.44147601 0.88295202 0.55852399
[235,] 0.41267026 0.82534051 0.58732974
[236,] 0.44886921 0.89773843 0.55113079
[237,] 0.40774250 0.81548500 0.59225750
[238,] 0.37832752 0.75665503 0.62167248
[239,] 0.33908592 0.67817184 0.66091408
[240,] 0.29567187 0.59134373 0.70432813
[241,] 0.25562588 0.51125175 0.74437412
[242,] 0.31166156 0.62332312 0.68833844
[243,] 0.34584472 0.69168944 0.65415528
[244,] 0.32605361 0.65210722 0.67394639
[245,] 0.30292838 0.60585677 0.69707162
[246,] 0.25936681 0.51873362 0.74063319
[247,] 0.24980207 0.49960414 0.75019793
[248,] 0.44199418 0.88398837 0.55800582
[249,] 0.41671385 0.83342769 0.58328615
[250,] 0.66403695 0.67192611 0.33596305
[251,] 0.68062298 0.63875405 0.31937702
[252,] 0.62877737 0.74244527 0.37122263
[253,] 0.56783548 0.86432903 0.43216452
[254,] 0.50473408 0.99053184 0.49526592
[255,] 0.78477469 0.43045061 0.21522531
[256,] 0.73683520 0.52632960 0.26316480
[257,] 0.68931839 0.62136321 0.31068161
[258,] 0.70924024 0.58151951 0.29075976
[259,] 0.71104222 0.57791555 0.28895778
[260,] 0.63361166 0.73277668 0.36638834
[261,] 0.55549211 0.88901577 0.44450789
[262,] 0.66119615 0.67760771 0.33880385
[263,] 0.57344967 0.85310066 0.42655033
[264,] 0.50288675 0.99422651 0.49711325
[265,] 0.40712836 0.81425672 0.59287164
[266,] 0.35147399 0.70294798 0.64852601
[267,] 0.26409678 0.52819357 0.73590322
[268,] 0.18973939 0.37947878 0.81026061
[269,] 0.11203010 0.22406019 0.88796990
> postscript(file="/var/fisher/rcomp/tmp/1z58r1386543953.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2yb0o1386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3x6fu1386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4d51d1386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/53bbl1386543953.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
-0.22010233 3.36560198 -3.63315023 -1.57115678 1.85217238 3.76757867
7 8 9 10 11 12
-0.99113900 -0.26667386 0.74948307 0.60292935 3.15116752 4.49559428
13 14 15 16 17 18
-3.75266585 1.46156637 3.55482365 0.49811135 0.34071494 2.02917508
19 20 21 22 23 24
0.01402344 1.57281812 4.36306880 -3.41587295 -0.86308536 -2.46783358
25 26 27 28 29 30
3.05310160 -5.77576100 2.26974977 -0.22337680 0.56150919 -2.46973764
31 32 33 34 35 36
1.70307441 -0.08890639 2.62601344 0.59818246 0.51447054 3.01237225
37 38 39 40 41 42
-3.90551653 1.50784674 2.60654266 -0.73493537 1.19446458 1.77210093
43 44 45 46 47 48
1.77184805 -1.49704064 0.55072825 -2.92097782 0.77494854 0.68035544
49 50 51 52 53 54
2.54347535 -0.56275331 1.70097289 0.53688615 -2.14504655 -2.36972829
55 56 57 58 59 60
-0.91686482 0.62560896 1.41368529 0.68671523 -1.60651661 -2.50929311
61 62 63 64 65 66
-5.25088682 -0.76865991 -3.40161530 -0.13009714 0.50221807 -3.74162091
67 68 69 70 71 72
-3.69411602 -1.91618434 0.98543915 1.36123127 0.91263167 1.91788505
73 74 75 76 77 78
0.42446623 1.13759419 -0.77140639 -2.81752390 2.53236867 -1.51387712
79 80 81 82 83 84
1.81322993 -0.85331538 1.39984319 0.57087947 2.36159638 0.70765843
85 86 87 88 89 90
-0.50266931 1.50091959 -0.42249674 -1.34527290 -7.31954344 3.09160128
91 92 93 94 95 96
-1.60705566 0.63485573 -0.37221078 -1.02041893 1.41348305 -2.49961947
97 98 99 100 101 102
-0.57337049 3.25864308 1.63285054 2.50516679 -1.74145804 1.11607257
103 104 105 106 107 108
-2.59795416 0.64137840 -4.49841193 1.33803499 1.22940144 -3.71415505
109 110 111 112 113 114
-4.17226063 0.65941224 -3.35169445 -1.41827784 3.91189037 2.00485541
115 116 117 118 119 120
-0.07254243 -0.18032136 -0.06575576 -0.52702358 -1.24341583 -0.46375403
121 122 123 124 125 126
0.87780072 0.56445792 0.58265463 -1.33559926 3.25880596 2.22209810
127 128 129 130 131 132
4.68015319 0.96659748 -0.65302639 -3.83002909 0.36998375 1.92739104
133 134 135 136 137 138
1.56051408 1.64378329 -2.37900222 0.45939831 -2.76204724 0.93430599
139 140 141 142 143 144
-0.01509362 0.54042550 2.56840804 -0.39525550 1.23043114 1.98770355
145 146 147 148 149 150
1.27104825 -2.32522369 -1.65282414 -4.41018252 2.15416688 -1.48519894
151 152 153 154 155 156
2.35624276 -2.49370353 -5.38549773 -0.82117549 -0.08694058 4.68015319
157 158 159 160 161 162
-1.49788306 -1.91980877 -2.27348350 1.70406152 3.12673331 -3.28492814
163 164 165 166 167 168
-0.80294653 1.65371380 -0.78244127 -4.03083710 -3.42220050 -1.42807168
169 170 171 172 173 174
2.54172075 -3.39283146 0.29306855 2.87356363 0.51214857 -4.13814979
175 176 177 178 179 180
-0.18856404 2.78797568 -2.08538133 0.52572668 0.74558279 2.14403692
181 182 183 184 185 186
1.31644360 3.73159501 -0.36506134 1.14904780 1.75916090 0.92081081
187 188 189 190 191 192
-1.09263902 -2.12716661 0.53509696 1.91788924 -2.44707144 1.89561412
193 194 195 196 197 198
-0.56028901 0.45207678 0.62362973 -4.46038556 -3.61606795 2.75345438
199 200 201 202 203 204
4.83716542 0.78249768 1.63021415 1.68318485 0.55292856 3.34338148
205 206 207 208 209 210
0.83064275 3.40945927 -3.99186006 3.78330551 -1.42387516 -2.52951320
211 212 213 214 215 216
-2.46296439 2.56427207 3.45659905 0.61753393 -4.40745423 1.69265625
217 218 219 220 221 222
-1.34775862 3.46717686 -3.33739323 2.81612110 3.48994647 6.59432155
223 224 225 226 227 228
-0.44439149 -4.13084645 -1.21780569 0.74463925 -2.88853058 2.29673120
229 230 231 232 233 234
-0.86447484 2.84100813 -1.20134538 0.52360837 0.52897400 -3.36312269
235 236 237 238 239 240
-1.17398064 -0.09076212 -4.21291831 -0.02462258 -0.44432974 -1.09738591
241 242 243 244 245 246
0.59890557 1.12714906 -2.83862469 -0.89312955 2.93486622 0.96721947
247 248 249 250 251 252
-0.12704311 -0.50462616 -0.31560761 -0.45549818 -3.64062446 -4.22822063
253 254 255 256 257 258
1.80718247 -2.26042300 0.77834141 2.99740944 -5.27887259 1.63283235
259 260 261 262 263 264
-4.65156827 -3.29413554 0.92242371 0.78245831 -0.47411044 -4.10170593
265 266 267 268 269 270
-0.39624642 -0.16194059 -1.55444589 -2.30486605 -0.44697032 -0.64510735
271 272 273 274 275 276
-3.13141381 2.46963238 0.31791602 0.94642802 0.32708405 3.73643823
277 278 279 280 281 282
2.67298234 1.38225740 1.81354227 -1.16477324 2.18150217 0.65109560
283 284 285 286 287 288
0.87735087 -0.05922782 3.25464646 3.98147712 -4.40708433 0.87945718
> postscript(file="/var/fisher/rcomp/tmp/6zvmt1386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 288
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.22010233 NA
1 3.36560198 -0.22010233
2 -3.63315023 3.36560198
3 -1.57115678 -3.63315023
4 1.85217238 -1.57115678
5 3.76757867 1.85217238
6 -0.99113900 3.76757867
7 -0.26667386 -0.99113900
8 0.74948307 -0.26667386
9 0.60292935 0.74948307
10 3.15116752 0.60292935
11 4.49559428 3.15116752
12 -3.75266585 4.49559428
13 1.46156637 -3.75266585
14 3.55482365 1.46156637
15 0.49811135 3.55482365
16 0.34071494 0.49811135
17 2.02917508 0.34071494
18 0.01402344 2.02917508
19 1.57281812 0.01402344
20 4.36306880 1.57281812
21 -3.41587295 4.36306880
22 -0.86308536 -3.41587295
23 -2.46783358 -0.86308536
24 3.05310160 -2.46783358
25 -5.77576100 3.05310160
26 2.26974977 -5.77576100
27 -0.22337680 2.26974977
28 0.56150919 -0.22337680
29 -2.46973764 0.56150919
30 1.70307441 -2.46973764
31 -0.08890639 1.70307441
32 2.62601344 -0.08890639
33 0.59818246 2.62601344
34 0.51447054 0.59818246
35 3.01237225 0.51447054
36 -3.90551653 3.01237225
37 1.50784674 -3.90551653
38 2.60654266 1.50784674
39 -0.73493537 2.60654266
40 1.19446458 -0.73493537
41 1.77210093 1.19446458
42 1.77184805 1.77210093
43 -1.49704064 1.77184805
44 0.55072825 -1.49704064
45 -2.92097782 0.55072825
46 0.77494854 -2.92097782
47 0.68035544 0.77494854
48 2.54347535 0.68035544
49 -0.56275331 2.54347535
50 1.70097289 -0.56275331
51 0.53688615 1.70097289
52 -2.14504655 0.53688615
53 -2.36972829 -2.14504655
54 -0.91686482 -2.36972829
55 0.62560896 -0.91686482
56 1.41368529 0.62560896
57 0.68671523 1.41368529
58 -1.60651661 0.68671523
59 -2.50929311 -1.60651661
60 -5.25088682 -2.50929311
61 -0.76865991 -5.25088682
62 -3.40161530 -0.76865991
63 -0.13009714 -3.40161530
64 0.50221807 -0.13009714
65 -3.74162091 0.50221807
66 -3.69411602 -3.74162091
67 -1.91618434 -3.69411602
68 0.98543915 -1.91618434
69 1.36123127 0.98543915
70 0.91263167 1.36123127
71 1.91788505 0.91263167
72 0.42446623 1.91788505
73 1.13759419 0.42446623
74 -0.77140639 1.13759419
75 -2.81752390 -0.77140639
76 2.53236867 -2.81752390
77 -1.51387712 2.53236867
78 1.81322993 -1.51387712
79 -0.85331538 1.81322993
80 1.39984319 -0.85331538
81 0.57087947 1.39984319
82 2.36159638 0.57087947
83 0.70765843 2.36159638
84 -0.50266931 0.70765843
85 1.50091959 -0.50266931
86 -0.42249674 1.50091959
87 -1.34527290 -0.42249674
88 -7.31954344 -1.34527290
89 3.09160128 -7.31954344
90 -1.60705566 3.09160128
91 0.63485573 -1.60705566
92 -0.37221078 0.63485573
93 -1.02041893 -0.37221078
94 1.41348305 -1.02041893
95 -2.49961947 1.41348305
96 -0.57337049 -2.49961947
97 3.25864308 -0.57337049
98 1.63285054 3.25864308
99 2.50516679 1.63285054
100 -1.74145804 2.50516679
101 1.11607257 -1.74145804
102 -2.59795416 1.11607257
103 0.64137840 -2.59795416
104 -4.49841193 0.64137840
105 1.33803499 -4.49841193
106 1.22940144 1.33803499
107 -3.71415505 1.22940144
108 -4.17226063 -3.71415505
109 0.65941224 -4.17226063
110 -3.35169445 0.65941224
111 -1.41827784 -3.35169445
112 3.91189037 -1.41827784
113 2.00485541 3.91189037
114 -0.07254243 2.00485541
115 -0.18032136 -0.07254243
116 -0.06575576 -0.18032136
117 -0.52702358 -0.06575576
118 -1.24341583 -0.52702358
119 -0.46375403 -1.24341583
120 0.87780072 -0.46375403
121 0.56445792 0.87780072
122 0.58265463 0.56445792
123 -1.33559926 0.58265463
124 3.25880596 -1.33559926
125 2.22209810 3.25880596
126 4.68015319 2.22209810
127 0.96659748 4.68015319
128 -0.65302639 0.96659748
129 -3.83002909 -0.65302639
130 0.36998375 -3.83002909
131 1.92739104 0.36998375
132 1.56051408 1.92739104
133 1.64378329 1.56051408
134 -2.37900222 1.64378329
135 0.45939831 -2.37900222
136 -2.76204724 0.45939831
137 0.93430599 -2.76204724
138 -0.01509362 0.93430599
139 0.54042550 -0.01509362
140 2.56840804 0.54042550
141 -0.39525550 2.56840804
142 1.23043114 -0.39525550
143 1.98770355 1.23043114
144 1.27104825 1.98770355
145 -2.32522369 1.27104825
146 -1.65282414 -2.32522369
147 -4.41018252 -1.65282414
148 2.15416688 -4.41018252
149 -1.48519894 2.15416688
150 2.35624276 -1.48519894
151 -2.49370353 2.35624276
152 -5.38549773 -2.49370353
153 -0.82117549 -5.38549773
154 -0.08694058 -0.82117549
155 4.68015319 -0.08694058
156 -1.49788306 4.68015319
157 -1.91980877 -1.49788306
158 -2.27348350 -1.91980877
159 1.70406152 -2.27348350
160 3.12673331 1.70406152
161 -3.28492814 3.12673331
162 -0.80294653 -3.28492814
163 1.65371380 -0.80294653
164 -0.78244127 1.65371380
165 -4.03083710 -0.78244127
166 -3.42220050 -4.03083710
167 -1.42807168 -3.42220050
168 2.54172075 -1.42807168
169 -3.39283146 2.54172075
170 0.29306855 -3.39283146
171 2.87356363 0.29306855
172 0.51214857 2.87356363
173 -4.13814979 0.51214857
174 -0.18856404 -4.13814979
175 2.78797568 -0.18856404
176 -2.08538133 2.78797568
177 0.52572668 -2.08538133
178 0.74558279 0.52572668
179 2.14403692 0.74558279
180 1.31644360 2.14403692
181 3.73159501 1.31644360
182 -0.36506134 3.73159501
183 1.14904780 -0.36506134
184 1.75916090 1.14904780
185 0.92081081 1.75916090
186 -1.09263902 0.92081081
187 -2.12716661 -1.09263902
188 0.53509696 -2.12716661
189 1.91788924 0.53509696
190 -2.44707144 1.91788924
191 1.89561412 -2.44707144
192 -0.56028901 1.89561412
193 0.45207678 -0.56028901
194 0.62362973 0.45207678
195 -4.46038556 0.62362973
196 -3.61606795 -4.46038556
197 2.75345438 -3.61606795
198 4.83716542 2.75345438
199 0.78249768 4.83716542
200 1.63021415 0.78249768
201 1.68318485 1.63021415
202 0.55292856 1.68318485
203 3.34338148 0.55292856
204 0.83064275 3.34338148
205 3.40945927 0.83064275
206 -3.99186006 3.40945927
207 3.78330551 -3.99186006
208 -1.42387516 3.78330551
209 -2.52951320 -1.42387516
210 -2.46296439 -2.52951320
211 2.56427207 -2.46296439
212 3.45659905 2.56427207
213 0.61753393 3.45659905
214 -4.40745423 0.61753393
215 1.69265625 -4.40745423
216 -1.34775862 1.69265625
217 3.46717686 -1.34775862
218 -3.33739323 3.46717686
219 2.81612110 -3.33739323
220 3.48994647 2.81612110
221 6.59432155 3.48994647
222 -0.44439149 6.59432155
223 -4.13084645 -0.44439149
224 -1.21780569 -4.13084645
225 0.74463925 -1.21780569
226 -2.88853058 0.74463925
227 2.29673120 -2.88853058
228 -0.86447484 2.29673120
229 2.84100813 -0.86447484
230 -1.20134538 2.84100813
231 0.52360837 -1.20134538
232 0.52897400 0.52360837
233 -3.36312269 0.52897400
234 -1.17398064 -3.36312269
235 -0.09076212 -1.17398064
236 -4.21291831 -0.09076212
237 -0.02462258 -4.21291831
238 -0.44432974 -0.02462258
239 -1.09738591 -0.44432974
240 0.59890557 -1.09738591
241 1.12714906 0.59890557
242 -2.83862469 1.12714906
243 -0.89312955 -2.83862469
244 2.93486622 -0.89312955
245 0.96721947 2.93486622
246 -0.12704311 0.96721947
247 -0.50462616 -0.12704311
248 -0.31560761 -0.50462616
249 -0.45549818 -0.31560761
250 -3.64062446 -0.45549818
251 -4.22822063 -3.64062446
252 1.80718247 -4.22822063
253 -2.26042300 1.80718247
254 0.77834141 -2.26042300
255 2.99740944 0.77834141
256 -5.27887259 2.99740944
257 1.63283235 -5.27887259
258 -4.65156827 1.63283235
259 -3.29413554 -4.65156827
260 0.92242371 -3.29413554
261 0.78245831 0.92242371
262 -0.47411044 0.78245831
263 -4.10170593 -0.47411044
264 -0.39624642 -4.10170593
265 -0.16194059 -0.39624642
266 -1.55444589 -0.16194059
267 -2.30486605 -1.55444589
268 -0.44697032 -2.30486605
269 -0.64510735 -0.44697032
270 -3.13141381 -0.64510735
271 2.46963238 -3.13141381
272 0.31791602 2.46963238
273 0.94642802 0.31791602
274 0.32708405 0.94642802
275 3.73643823 0.32708405
276 2.67298234 3.73643823
277 1.38225740 2.67298234
278 1.81354227 1.38225740
279 -1.16477324 1.81354227
280 2.18150217 -1.16477324
281 0.65109560 2.18150217
282 0.87735087 0.65109560
283 -0.05922782 0.87735087
284 3.25464646 -0.05922782
285 3.98147712 3.25464646
286 -4.40708433 3.98147712
287 0.87945718 -4.40708433
288 NA 0.87945718
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.36560198 -0.22010233
[2,] -3.63315023 3.36560198
[3,] -1.57115678 -3.63315023
[4,] 1.85217238 -1.57115678
[5,] 3.76757867 1.85217238
[6,] -0.99113900 3.76757867
[7,] -0.26667386 -0.99113900
[8,] 0.74948307 -0.26667386
[9,] 0.60292935 0.74948307
[10,] 3.15116752 0.60292935
[11,] 4.49559428 3.15116752
[12,] -3.75266585 4.49559428
[13,] 1.46156637 -3.75266585
[14,] 3.55482365 1.46156637
[15,] 0.49811135 3.55482365
[16,] 0.34071494 0.49811135
[17,] 2.02917508 0.34071494
[18,] 0.01402344 2.02917508
[19,] 1.57281812 0.01402344
[20,] 4.36306880 1.57281812
[21,] -3.41587295 4.36306880
[22,] -0.86308536 -3.41587295
[23,] -2.46783358 -0.86308536
[24,] 3.05310160 -2.46783358
[25,] -5.77576100 3.05310160
[26,] 2.26974977 -5.77576100
[27,] -0.22337680 2.26974977
[28,] 0.56150919 -0.22337680
[29,] -2.46973764 0.56150919
[30,] 1.70307441 -2.46973764
[31,] -0.08890639 1.70307441
[32,] 2.62601344 -0.08890639
[33,] 0.59818246 2.62601344
[34,] 0.51447054 0.59818246
[35,] 3.01237225 0.51447054
[36,] -3.90551653 3.01237225
[37,] 1.50784674 -3.90551653
[38,] 2.60654266 1.50784674
[39,] -0.73493537 2.60654266
[40,] 1.19446458 -0.73493537
[41,] 1.77210093 1.19446458
[42,] 1.77184805 1.77210093
[43,] -1.49704064 1.77184805
[44,] 0.55072825 -1.49704064
[45,] -2.92097782 0.55072825
[46,] 0.77494854 -2.92097782
[47,] 0.68035544 0.77494854
[48,] 2.54347535 0.68035544
[49,] -0.56275331 2.54347535
[50,] 1.70097289 -0.56275331
[51,] 0.53688615 1.70097289
[52,] -2.14504655 0.53688615
[53,] -2.36972829 -2.14504655
[54,] -0.91686482 -2.36972829
[55,] 0.62560896 -0.91686482
[56,] 1.41368529 0.62560896
[57,] 0.68671523 1.41368529
[58,] -1.60651661 0.68671523
[59,] -2.50929311 -1.60651661
[60,] -5.25088682 -2.50929311
[61,] -0.76865991 -5.25088682
[62,] -3.40161530 -0.76865991
[63,] -0.13009714 -3.40161530
[64,] 0.50221807 -0.13009714
[65,] -3.74162091 0.50221807
[66,] -3.69411602 -3.74162091
[67,] -1.91618434 -3.69411602
[68,] 0.98543915 -1.91618434
[69,] 1.36123127 0.98543915
[70,] 0.91263167 1.36123127
[71,] 1.91788505 0.91263167
[72,] 0.42446623 1.91788505
[73,] 1.13759419 0.42446623
[74,] -0.77140639 1.13759419
[75,] -2.81752390 -0.77140639
[76,] 2.53236867 -2.81752390
[77,] -1.51387712 2.53236867
[78,] 1.81322993 -1.51387712
[79,] -0.85331538 1.81322993
[80,] 1.39984319 -0.85331538
[81,] 0.57087947 1.39984319
[82,] 2.36159638 0.57087947
[83,] 0.70765843 2.36159638
[84,] -0.50266931 0.70765843
[85,] 1.50091959 -0.50266931
[86,] -0.42249674 1.50091959
[87,] -1.34527290 -0.42249674
[88,] -7.31954344 -1.34527290
[89,] 3.09160128 -7.31954344
[90,] -1.60705566 3.09160128
[91,] 0.63485573 -1.60705566
[92,] -0.37221078 0.63485573
[93,] -1.02041893 -0.37221078
[94,] 1.41348305 -1.02041893
[95,] -2.49961947 1.41348305
[96,] -0.57337049 -2.49961947
[97,] 3.25864308 -0.57337049
[98,] 1.63285054 3.25864308
[99,] 2.50516679 1.63285054
[100,] -1.74145804 2.50516679
[101,] 1.11607257 -1.74145804
[102,] -2.59795416 1.11607257
[103,] 0.64137840 -2.59795416
[104,] -4.49841193 0.64137840
[105,] 1.33803499 -4.49841193
[106,] 1.22940144 1.33803499
[107,] -3.71415505 1.22940144
[108,] -4.17226063 -3.71415505
[109,] 0.65941224 -4.17226063
[110,] -3.35169445 0.65941224
[111,] -1.41827784 -3.35169445
[112,] 3.91189037 -1.41827784
[113,] 2.00485541 3.91189037
[114,] -0.07254243 2.00485541
[115,] -0.18032136 -0.07254243
[116,] -0.06575576 -0.18032136
[117,] -0.52702358 -0.06575576
[118,] -1.24341583 -0.52702358
[119,] -0.46375403 -1.24341583
[120,] 0.87780072 -0.46375403
[121,] 0.56445792 0.87780072
[122,] 0.58265463 0.56445792
[123,] -1.33559926 0.58265463
[124,] 3.25880596 -1.33559926
[125,] 2.22209810 3.25880596
[126,] 4.68015319 2.22209810
[127,] 0.96659748 4.68015319
[128,] -0.65302639 0.96659748
[129,] -3.83002909 -0.65302639
[130,] 0.36998375 -3.83002909
[131,] 1.92739104 0.36998375
[132,] 1.56051408 1.92739104
[133,] 1.64378329 1.56051408
[134,] -2.37900222 1.64378329
[135,] 0.45939831 -2.37900222
[136,] -2.76204724 0.45939831
[137,] 0.93430599 -2.76204724
[138,] -0.01509362 0.93430599
[139,] 0.54042550 -0.01509362
[140,] 2.56840804 0.54042550
[141,] -0.39525550 2.56840804
[142,] 1.23043114 -0.39525550
[143,] 1.98770355 1.23043114
[144,] 1.27104825 1.98770355
[145,] -2.32522369 1.27104825
[146,] -1.65282414 -2.32522369
[147,] -4.41018252 -1.65282414
[148,] 2.15416688 -4.41018252
[149,] -1.48519894 2.15416688
[150,] 2.35624276 -1.48519894
[151,] -2.49370353 2.35624276
[152,] -5.38549773 -2.49370353
[153,] -0.82117549 -5.38549773
[154,] -0.08694058 -0.82117549
[155,] 4.68015319 -0.08694058
[156,] -1.49788306 4.68015319
[157,] -1.91980877 -1.49788306
[158,] -2.27348350 -1.91980877
[159,] 1.70406152 -2.27348350
[160,] 3.12673331 1.70406152
[161,] -3.28492814 3.12673331
[162,] -0.80294653 -3.28492814
[163,] 1.65371380 -0.80294653
[164,] -0.78244127 1.65371380
[165,] -4.03083710 -0.78244127
[166,] -3.42220050 -4.03083710
[167,] -1.42807168 -3.42220050
[168,] 2.54172075 -1.42807168
[169,] -3.39283146 2.54172075
[170,] 0.29306855 -3.39283146
[171,] 2.87356363 0.29306855
[172,] 0.51214857 2.87356363
[173,] -4.13814979 0.51214857
[174,] -0.18856404 -4.13814979
[175,] 2.78797568 -0.18856404
[176,] -2.08538133 2.78797568
[177,] 0.52572668 -2.08538133
[178,] 0.74558279 0.52572668
[179,] 2.14403692 0.74558279
[180,] 1.31644360 2.14403692
[181,] 3.73159501 1.31644360
[182,] -0.36506134 3.73159501
[183,] 1.14904780 -0.36506134
[184,] 1.75916090 1.14904780
[185,] 0.92081081 1.75916090
[186,] -1.09263902 0.92081081
[187,] -2.12716661 -1.09263902
[188,] 0.53509696 -2.12716661
[189,] 1.91788924 0.53509696
[190,] -2.44707144 1.91788924
[191,] 1.89561412 -2.44707144
[192,] -0.56028901 1.89561412
[193,] 0.45207678 -0.56028901
[194,] 0.62362973 0.45207678
[195,] -4.46038556 0.62362973
[196,] -3.61606795 -4.46038556
[197,] 2.75345438 -3.61606795
[198,] 4.83716542 2.75345438
[199,] 0.78249768 4.83716542
[200,] 1.63021415 0.78249768
[201,] 1.68318485 1.63021415
[202,] 0.55292856 1.68318485
[203,] 3.34338148 0.55292856
[204,] 0.83064275 3.34338148
[205,] 3.40945927 0.83064275
[206,] -3.99186006 3.40945927
[207,] 3.78330551 -3.99186006
[208,] -1.42387516 3.78330551
[209,] -2.52951320 -1.42387516
[210,] -2.46296439 -2.52951320
[211,] 2.56427207 -2.46296439
[212,] 3.45659905 2.56427207
[213,] 0.61753393 3.45659905
[214,] -4.40745423 0.61753393
[215,] 1.69265625 -4.40745423
[216,] -1.34775862 1.69265625
[217,] 3.46717686 -1.34775862
[218,] -3.33739323 3.46717686
[219,] 2.81612110 -3.33739323
[220,] 3.48994647 2.81612110
[221,] 6.59432155 3.48994647
[222,] -0.44439149 6.59432155
[223,] -4.13084645 -0.44439149
[224,] -1.21780569 -4.13084645
[225,] 0.74463925 -1.21780569
[226,] -2.88853058 0.74463925
[227,] 2.29673120 -2.88853058
[228,] -0.86447484 2.29673120
[229,] 2.84100813 -0.86447484
[230,] -1.20134538 2.84100813
[231,] 0.52360837 -1.20134538
[232,] 0.52897400 0.52360837
[233,] -3.36312269 0.52897400
[234,] -1.17398064 -3.36312269
[235,] -0.09076212 -1.17398064
[236,] -4.21291831 -0.09076212
[237,] -0.02462258 -4.21291831
[238,] -0.44432974 -0.02462258
[239,] -1.09738591 -0.44432974
[240,] 0.59890557 -1.09738591
[241,] 1.12714906 0.59890557
[242,] -2.83862469 1.12714906
[243,] -0.89312955 -2.83862469
[244,] 2.93486622 -0.89312955
[245,] 0.96721947 2.93486622
[246,] -0.12704311 0.96721947
[247,] -0.50462616 -0.12704311
[248,] -0.31560761 -0.50462616
[249,] -0.45549818 -0.31560761
[250,] -3.64062446 -0.45549818
[251,] -4.22822063 -3.64062446
[252,] 1.80718247 -4.22822063
[253,] -2.26042300 1.80718247
[254,] 0.77834141 -2.26042300
[255,] 2.99740944 0.77834141
[256,] -5.27887259 2.99740944
[257,] 1.63283235 -5.27887259
[258,] -4.65156827 1.63283235
[259,] -3.29413554 -4.65156827
[260,] 0.92242371 -3.29413554
[261,] 0.78245831 0.92242371
[262,] -0.47411044 0.78245831
[263,] -4.10170593 -0.47411044
[264,] -0.39624642 -4.10170593
[265,] -0.16194059 -0.39624642
[266,] -1.55444589 -0.16194059
[267,] -2.30486605 -1.55444589
[268,] -0.44697032 -2.30486605
[269,] -0.64510735 -0.44697032
[270,] -3.13141381 -0.64510735
[271,] 2.46963238 -3.13141381
[272,] 0.31791602 2.46963238
[273,] 0.94642802 0.31791602
[274,] 0.32708405 0.94642802
[275,] 3.73643823 0.32708405
[276,] 2.67298234 3.73643823
[277,] 1.38225740 2.67298234
[278,] 1.81354227 1.38225740
[279,] -1.16477324 1.81354227
[280,] 2.18150217 -1.16477324
[281,] 0.65109560 2.18150217
[282,] 0.87735087 0.65109560
[283,] -0.05922782 0.87735087
[284,] 3.25464646 -0.05922782
[285,] 3.98147712 3.25464646
[286,] -4.40708433 3.98147712
[287,] 0.87945718 -4.40708433
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.36560198 -0.22010233
2 -3.63315023 3.36560198
3 -1.57115678 -3.63315023
4 1.85217238 -1.57115678
5 3.76757867 1.85217238
6 -0.99113900 3.76757867
7 -0.26667386 -0.99113900
8 0.74948307 -0.26667386
9 0.60292935 0.74948307
10 3.15116752 0.60292935
11 4.49559428 3.15116752
12 -3.75266585 4.49559428
13 1.46156637 -3.75266585
14 3.55482365 1.46156637
15 0.49811135 3.55482365
16 0.34071494 0.49811135
17 2.02917508 0.34071494
18 0.01402344 2.02917508
19 1.57281812 0.01402344
20 4.36306880 1.57281812
21 -3.41587295 4.36306880
22 -0.86308536 -3.41587295
23 -2.46783358 -0.86308536
24 3.05310160 -2.46783358
25 -5.77576100 3.05310160
26 2.26974977 -5.77576100
27 -0.22337680 2.26974977
28 0.56150919 -0.22337680
29 -2.46973764 0.56150919
30 1.70307441 -2.46973764
31 -0.08890639 1.70307441
32 2.62601344 -0.08890639
33 0.59818246 2.62601344
34 0.51447054 0.59818246
35 3.01237225 0.51447054
36 -3.90551653 3.01237225
37 1.50784674 -3.90551653
38 2.60654266 1.50784674
39 -0.73493537 2.60654266
40 1.19446458 -0.73493537
41 1.77210093 1.19446458
42 1.77184805 1.77210093
43 -1.49704064 1.77184805
44 0.55072825 -1.49704064
45 -2.92097782 0.55072825
46 0.77494854 -2.92097782
47 0.68035544 0.77494854
48 2.54347535 0.68035544
49 -0.56275331 2.54347535
50 1.70097289 -0.56275331
51 0.53688615 1.70097289
52 -2.14504655 0.53688615
53 -2.36972829 -2.14504655
54 -0.91686482 -2.36972829
55 0.62560896 -0.91686482
56 1.41368529 0.62560896
57 0.68671523 1.41368529
58 -1.60651661 0.68671523
59 -2.50929311 -1.60651661
60 -5.25088682 -2.50929311
61 -0.76865991 -5.25088682
62 -3.40161530 -0.76865991
63 -0.13009714 -3.40161530
64 0.50221807 -0.13009714
65 -3.74162091 0.50221807
66 -3.69411602 -3.74162091
67 -1.91618434 -3.69411602
68 0.98543915 -1.91618434
69 1.36123127 0.98543915
70 0.91263167 1.36123127
71 1.91788505 0.91263167
72 0.42446623 1.91788505
73 1.13759419 0.42446623
74 -0.77140639 1.13759419
75 -2.81752390 -0.77140639
76 2.53236867 -2.81752390
77 -1.51387712 2.53236867
78 1.81322993 -1.51387712
79 -0.85331538 1.81322993
80 1.39984319 -0.85331538
81 0.57087947 1.39984319
82 2.36159638 0.57087947
83 0.70765843 2.36159638
84 -0.50266931 0.70765843
85 1.50091959 -0.50266931
86 -0.42249674 1.50091959
87 -1.34527290 -0.42249674
88 -7.31954344 -1.34527290
89 3.09160128 -7.31954344
90 -1.60705566 3.09160128
91 0.63485573 -1.60705566
92 -0.37221078 0.63485573
93 -1.02041893 -0.37221078
94 1.41348305 -1.02041893
95 -2.49961947 1.41348305
96 -0.57337049 -2.49961947
97 3.25864308 -0.57337049
98 1.63285054 3.25864308
99 2.50516679 1.63285054
100 -1.74145804 2.50516679
101 1.11607257 -1.74145804
102 -2.59795416 1.11607257
103 0.64137840 -2.59795416
104 -4.49841193 0.64137840
105 1.33803499 -4.49841193
106 1.22940144 1.33803499
107 -3.71415505 1.22940144
108 -4.17226063 -3.71415505
109 0.65941224 -4.17226063
110 -3.35169445 0.65941224
111 -1.41827784 -3.35169445
112 3.91189037 -1.41827784
113 2.00485541 3.91189037
114 -0.07254243 2.00485541
115 -0.18032136 -0.07254243
116 -0.06575576 -0.18032136
117 -0.52702358 -0.06575576
118 -1.24341583 -0.52702358
119 -0.46375403 -1.24341583
120 0.87780072 -0.46375403
121 0.56445792 0.87780072
122 0.58265463 0.56445792
123 -1.33559926 0.58265463
124 3.25880596 -1.33559926
125 2.22209810 3.25880596
126 4.68015319 2.22209810
127 0.96659748 4.68015319
128 -0.65302639 0.96659748
129 -3.83002909 -0.65302639
130 0.36998375 -3.83002909
131 1.92739104 0.36998375
132 1.56051408 1.92739104
133 1.64378329 1.56051408
134 -2.37900222 1.64378329
135 0.45939831 -2.37900222
136 -2.76204724 0.45939831
137 0.93430599 -2.76204724
138 -0.01509362 0.93430599
139 0.54042550 -0.01509362
140 2.56840804 0.54042550
141 -0.39525550 2.56840804
142 1.23043114 -0.39525550
143 1.98770355 1.23043114
144 1.27104825 1.98770355
145 -2.32522369 1.27104825
146 -1.65282414 -2.32522369
147 -4.41018252 -1.65282414
148 2.15416688 -4.41018252
149 -1.48519894 2.15416688
150 2.35624276 -1.48519894
151 -2.49370353 2.35624276
152 -5.38549773 -2.49370353
153 -0.82117549 -5.38549773
154 -0.08694058 -0.82117549
155 4.68015319 -0.08694058
156 -1.49788306 4.68015319
157 -1.91980877 -1.49788306
158 -2.27348350 -1.91980877
159 1.70406152 -2.27348350
160 3.12673331 1.70406152
161 -3.28492814 3.12673331
162 -0.80294653 -3.28492814
163 1.65371380 -0.80294653
164 -0.78244127 1.65371380
165 -4.03083710 -0.78244127
166 -3.42220050 -4.03083710
167 -1.42807168 -3.42220050
168 2.54172075 -1.42807168
169 -3.39283146 2.54172075
170 0.29306855 -3.39283146
171 2.87356363 0.29306855
172 0.51214857 2.87356363
173 -4.13814979 0.51214857
174 -0.18856404 -4.13814979
175 2.78797568 -0.18856404
176 -2.08538133 2.78797568
177 0.52572668 -2.08538133
178 0.74558279 0.52572668
179 2.14403692 0.74558279
180 1.31644360 2.14403692
181 3.73159501 1.31644360
182 -0.36506134 3.73159501
183 1.14904780 -0.36506134
184 1.75916090 1.14904780
185 0.92081081 1.75916090
186 -1.09263902 0.92081081
187 -2.12716661 -1.09263902
188 0.53509696 -2.12716661
189 1.91788924 0.53509696
190 -2.44707144 1.91788924
191 1.89561412 -2.44707144
192 -0.56028901 1.89561412
193 0.45207678 -0.56028901
194 0.62362973 0.45207678
195 -4.46038556 0.62362973
196 -3.61606795 -4.46038556
197 2.75345438 -3.61606795
198 4.83716542 2.75345438
199 0.78249768 4.83716542
200 1.63021415 0.78249768
201 1.68318485 1.63021415
202 0.55292856 1.68318485
203 3.34338148 0.55292856
204 0.83064275 3.34338148
205 3.40945927 0.83064275
206 -3.99186006 3.40945927
207 3.78330551 -3.99186006
208 -1.42387516 3.78330551
209 -2.52951320 -1.42387516
210 -2.46296439 -2.52951320
211 2.56427207 -2.46296439
212 3.45659905 2.56427207
213 0.61753393 3.45659905
214 -4.40745423 0.61753393
215 1.69265625 -4.40745423
216 -1.34775862 1.69265625
217 3.46717686 -1.34775862
218 -3.33739323 3.46717686
219 2.81612110 -3.33739323
220 3.48994647 2.81612110
221 6.59432155 3.48994647
222 -0.44439149 6.59432155
223 -4.13084645 -0.44439149
224 -1.21780569 -4.13084645
225 0.74463925 -1.21780569
226 -2.88853058 0.74463925
227 2.29673120 -2.88853058
228 -0.86447484 2.29673120
229 2.84100813 -0.86447484
230 -1.20134538 2.84100813
231 0.52360837 -1.20134538
232 0.52897400 0.52360837
233 -3.36312269 0.52897400
234 -1.17398064 -3.36312269
235 -0.09076212 -1.17398064
236 -4.21291831 -0.09076212
237 -0.02462258 -4.21291831
238 -0.44432974 -0.02462258
239 -1.09738591 -0.44432974
240 0.59890557 -1.09738591
241 1.12714906 0.59890557
242 -2.83862469 1.12714906
243 -0.89312955 -2.83862469
244 2.93486622 -0.89312955
245 0.96721947 2.93486622
246 -0.12704311 0.96721947
247 -0.50462616 -0.12704311
248 -0.31560761 -0.50462616
249 -0.45549818 -0.31560761
250 -3.64062446 -0.45549818
251 -4.22822063 -3.64062446
252 1.80718247 -4.22822063
253 -2.26042300 1.80718247
254 0.77834141 -2.26042300
255 2.99740944 0.77834141
256 -5.27887259 2.99740944
257 1.63283235 -5.27887259
258 -4.65156827 1.63283235
259 -3.29413554 -4.65156827
260 0.92242371 -3.29413554
261 0.78245831 0.92242371
262 -0.47411044 0.78245831
263 -4.10170593 -0.47411044
264 -0.39624642 -4.10170593
265 -0.16194059 -0.39624642
266 -1.55444589 -0.16194059
267 -2.30486605 -1.55444589
268 -0.44697032 -2.30486605
269 -0.64510735 -0.44697032
270 -3.13141381 -0.64510735
271 2.46963238 -3.13141381
272 0.31791602 2.46963238
273 0.94642802 0.31791602
274 0.32708405 0.94642802
275 3.73643823 0.32708405
276 2.67298234 3.73643823
277 1.38225740 2.67298234
278 1.81354227 1.38225740
279 -1.16477324 1.81354227
280 2.18150217 -1.16477324
281 0.65109560 2.18150217
282 0.87735087 0.65109560
283 -0.05922782 0.87735087
284 3.25464646 -0.05922782
285 3.98147712 3.25464646
286 -4.40708433 3.98147712
287 0.87945718 -4.40708433
> 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/7o4b11386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8xlf61386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9bnfu1386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10l9341386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/113lz41386543953.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12dqoc1386543953.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13p9an1386543954.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14okib1386543954.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/1516bw1386543954.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16oeke1386543954.tab")
+ }
>
> try(system("convert tmp/1z58r1386543953.ps tmp/1z58r1386543953.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yb0o1386543953.ps tmp/2yb0o1386543953.png",intern=TRUE))
character(0)
> try(system("convert tmp/3x6fu1386543953.ps tmp/3x6fu1386543953.png",intern=TRUE))
character(0)
> try(system("convert tmp/4d51d1386543953.ps tmp/4d51d1386543953.png",intern=TRUE))
character(0)
> try(system("convert tmp/53bbl1386543953.ps tmp/53bbl1386543953.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zvmt1386543953.ps tmp/6zvmt1386543953.png",intern=TRUE))
character(0)
> try(system("convert tmp/7o4b11386543953.ps tmp/7o4b11386543953.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xlf61386543953.ps tmp/8xlf61386543953.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bnfu1386543953.ps tmp/9bnfu1386543953.png",intern=TRUE))
character(0)
> try(system("convert tmp/10l9341386543953.ps tmp/10l9341386543953.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
22.407 3.387 25.815