R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
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+ ,36
+ ,11
+ ,9
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+ ,0
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+ ,0
+ ,1
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+ ,35
+ ,15
+ ,10
+ ,14
+ ,0
+ ,0
+ ,27
+ ,29
+ ,12
+ ,9
+ ,13
+ ,0
+ ,1
+ ,16
+ ,22
+ ,6
+ ,6
+ ,12
+ ,0
+ ,0
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+ ,41
+ ,16
+ ,10
+ ,16
+ ,0
+ ,0
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+ ,36
+ ,10
+ ,9
+ ,16
+ ,0
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+ ,42
+ ,42
+ ,15
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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 = '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
Pop Gender Connected Separate Learning Software Happiness
1 1 1 41 38 13 12 14
2 1 1 39 32 16 11 18
3 1 1 30 35 19 15 11
4 1 0 31 33 15 6 12
5 1 1 34 37 14 13 16
6 1 1 35 29 13 10 18
7 1 1 39 31 19 12 14
8 1 1 34 36 15 14 14
9 1 1 36 35 14 12 15
10 1 1 37 38 15 9 15
11 1 0 38 31 16 10 17
12 1 1 36 34 16 12 19
13 1 0 38 35 16 12 10
14 1 1 39 38 16 11 16
15 1 1 33 37 17 15 18
16 1 0 32 33 15 12 14
17 1 0 36 32 15 10 14
18 1 1 38 38 20 12 17
19 1 0 39 38 18 11 14
20 1 1 32 32 16 12 16
21 1 0 32 33 16 11 18
22 1 1 31 31 16 12 11
23 1 1 39 38 19 13 14
24 1 1 37 39 16 11 12
25 1 0 39 32 17 12 17
26 1 1 41 32 17 13 9
27 1 0 36 35 16 10 16
28 1 1 33 37 15 14 14
29 1 1 33 33 16 12 15
30 1 0 34 33 14 10 11
31 1 1 31 31 15 12 16
32 1 0 27 32 12 8 13
33 1 1 37 31 14 10 17
34 1 1 34 37 16 12 15
35 1 0 34 30 14 12 14
36 1 0 32 33 10 7 16
37 1 0 29 31 10 9 9
38 1 0 36 33 14 12 15
39 1 1 29 31 16 10 17
40 1 0 35 33 16 10 13
41 1 0 37 32 16 10 15
42 1 1 34 33 14 12 16
43 1 0 38 32 20 15 16
44 1 0 35 33 14 10 12
45 1 1 38 28 14 10 15
46 1 1 37 35 11 12 11
47 1 1 38 39 14 13 15
48 1 1 33 34 15 11 15
49 1 1 36 38 16 11 17
50 1 0 38 32 14 12 13
51 1 1 32 38 16 14 16
52 1 0 32 30 14 10 14
53 1 0 32 33 12 12 11
54 1 1 34 38 16 13 12
55 1 0 32 32 9 5 12
56 1 1 37 35 14 6 15
57 1 1 39 34 16 12 16
58 1 1 29 34 16 12 15
59 1 0 37 36 15 11 12
60 1 1 35 34 16 10 12
61 1 0 30 28 12 7 8
62 1 0 38 34 16 12 13
63 1 1 34 35 16 14 11
64 1 1 31 35 14 11 14
65 1 1 34 31 16 12 15
66 1 0 35 37 17 13 10
67 1 1 36 35 18 14 11
68 1 0 30 27 18 11 12
69 1 1 39 40 12 12 15
70 1 0 35 37 16 12 15
71 1 0 38 36 10 8 14
72 1 1 31 38 14 11 16
73 1 1 34 39 18 14 15
74 1 0 38 41 18 14 15
75 1 0 34 27 16 12 13
76 1 1 39 30 17 9 12
77 1 1 37 37 16 13 17
78 1 1 34 31 16 11 13
79 1 0 28 31 13 12 15
80 1 0 37 27 16 12 13
81 1 0 33 36 16 12 15
82 1 1 35 37 16 12 15
83 1 0 37 33 15 12 16
84 1 1 32 34 15 11 15
85 1 1 33 31 16 10 14
86 1 0 38 39 14 9 15
87 1 1 33 34 16 12 14
88 1 1 29 32 16 12 13
89 1 1 33 33 15 12 7
90 1 1 31 36 12 9 17
91 1 1 36 32 17 15 13
92 1 1 35 41 16 12 15
93 1 1 32 28 15 12 14
94 1 1 29 30 13 12 13
95 1 1 39 36 16 10 16
96 1 1 37 35 16 13 12
97 1 1 35 31 16 9 14
98 1 0 37 34 16 12 17
99 1 0 32 36 14 10 15
100 1 1 38 36 16 14 17
101 1 0 37 35 16 11 12
102 1 1 36 37 20 15 16
103 1 0 32 28 15 11 11
104 1 1 33 39 16 11 15
105 1 0 40 32 13 12 9
106 1 1 38 35 17 12 16
107 1 0 41 39 16 12 15
108 1 0 36 35 16 11 10
109 1 1 43 42 12 7 10
110 1 1 30 34 16 12 15
111 1 1 31 33 16 14 11
112 1 1 32 41 17 11 13
113 1 1 37 34 12 10 18
114 1 0 37 32 18 13 16
115 1 1 33 40 14 13 14
116 1 1 34 40 14 8 14
117 1 1 33 35 13 11 14
118 1 1 38 36 16 12 14
119 1 0 33 37 13 11 12
120 1 1 31 27 16 13 14
121 1 1 38 39 13 12 15
122 1 1 37 38 16 14 15
123 1 1 36 31 15 13 15
124 1 1 31 33 16 15 13
125 1 0 39 32 15 10 17
126 1 1 44 39 17 11 17
127 1 1 33 36 15 9 19
128 1 1 35 33 12 11 15
129 1 0 32 33 16 10 13
130 1 0 28 32 10 11 9
131 1 1 40 37 16 8 15
132 1 0 27 30 12 11 15
133 1 0 37 38 14 12 15
134 1 1 32 29 15 12 16
135 1 0 28 22 13 9 11
136 1 0 34 35 15 11 14
137 1 1 30 35 11 10 11
138 1 1 35 34 12 8 15
139 1 0 31 35 11 9 13
140 1 1 32 34 16 8 15
141 1 0 30 37 15 9 16
142 1 1 30 35 17 15 14
143 1 0 31 23 16 11 15
144 1 1 40 31 10 8 16
145 1 1 32 27 18 13 16
146 1 0 36 36 13 12 11
147 1 0 32 31 16 12 12
148 1 0 35 32 13 9 9
149 1 1 38 39 10 7 16
150 1 1 42 37 15 13 13
151 1 0 34 38 16 9 16
152 1 1 35 39 16 6 12
153 1 1 38 34 14 8 9
154 1 1 33 31 10 8 13
155 1 1 32 37 13 6 14
156 1 1 33 36 15 9 19
157 1 1 34 32 16 11 13
158 1 1 32 38 12 8 12
159 0 0 27 26 13 10 10
160 0 0 31 26 12 8 14
161 0 0 38 33 17 14 16
162 0 1 34 39 15 10 10
163 0 0 24 30 10 8 11
164 0 0 30 33 14 11 14
165 0 1 26 25 11 12 12
166 0 1 34 38 13 12 9
167 0 0 27 37 16 12 9
168 0 0 37 31 12 5 11
169 0 1 36 37 16 12 16
170 0 0 41 35 12 10 9
171 0 1 29 25 9 7 13
172 0 1 36 28 12 12 16
173 0 0 32 35 15 11 13
174 0 1 37 33 12 8 9
175 0 0 30 30 12 9 12
176 0 1 31 31 14 10 16
177 0 1 38 37 12 9 11
178 0 1 36 36 16 12 14
179 0 0 35 30 11 6 13
180 0 0 31 36 19 15 15
181 0 0 38 32 15 12 14
182 0 1 22 28 8 12 16
183 0 1 32 36 16 12 13
184 0 0 36 34 17 11 14
185 0 1 39 31 12 7 15
186 0 0 28 28 11 7 13
187 0 0 32 36 11 5 11
188 0 1 32 36 14 12 11
189 0 1 38 40 16 12 14
190 0 1 32 33 12 3 15
191 0 1 35 37 16 11 11
192 0 1 32 32 13 10 15
193 0 0 37 38 15 12 12
194 0 1 34 31 16 9 14
195 0 1 33 37 16 12 14
196 0 0 33 33 14 9 8
197 0 0 30 30 16 12 9
198 0 0 24 30 14 10 15
199 0 0 34 31 11 9 17
200 0 0 34 32 12 12 13
201 0 1 33 34 15 8 15
202 0 1 34 36 15 11 15
203 0 1 35 37 16 11 14
204 0 0 35 36 16 12 16
205 0 0 36 33 11 10 13
206 0 0 34 33 15 10 16
207 0 1 34 33 12 12 9
208 0 0 41 44 12 12 16
209 0 0 32 39 15 11 11
210 0 0 30 32 15 8 10
211 0 1 35 35 16 12 11
212 0 0 28 25 14 10 15
213 0 1 33 35 17 11 17
214 0 1 39 34 14 10 14
215 0 0 36 35 13 8 8
216 0 1 36 39 15 12 15
217 0 0 35 33 13 12 11
218 0 0 38 36 14 10 16
219 0 1 33 32 15 12 10
220 0 0 31 32 12 9 15
221 0 1 32 36 8 6 16
222 0 0 31 32 14 10 19
223 0 0 33 34 14 9 12
224 0 0 34 33 11 9 8
225 0 0 34 35 12 9 11
226 0 1 34 30 13 6 14
227 0 0 33 38 10 10 9
228 0 0 32 34 16 6 15
229 0 1 41 33 18 14 13
230 0 1 34 32 13 10 16
231 0 0 36 31 11 10 11
232 0 0 37 30 4 6 12
233 0 0 36 27 13 12 13
234 0 1 29 31 16 12 10
235 0 0 37 30 10 7 11
236 0 0 27 32 12 8 12
237 0 0 35 35 12 11 8
238 0 0 28 28 10 3 12
239 0 0 35 33 13 6 12
240 0 0 29 35 12 8 11
241 0 0 32 35 14 9 13
242 0 1 36 32 10 9 14
243 0 1 19 21 12 8 10
244 0 1 21 20 12 9 12
245 0 0 31 34 11 7 15
246 0 0 33 32 10 7 13
247 0 1 36 34 12 6 13
248 0 1 33 32 16 9 13
249 0 0 37 33 12 10 12
250 0 0 34 33 14 11 12
251 0 0 35 37 16 12 9
252 0 1 31 32 14 8 9
253 0 1 37 34 13 11 15
254 0 1 35 30 4 3 10
255 0 1 27 30 15 11 14
256 0 0 34 38 11 12 15
257 0 0 40 36 11 7 7
258 0 0 29 32 14 9 14
259 0 0 38 34 15 12 8
260 0 1 34 33 14 8 10
261 0 0 21 27 13 11 13
262 0 0 36 32 11 8 13
263 0 1 38 34 15 10 13
264 0 0 30 29 11 8 8
265 0 0 35 35 13 7 12
266 0 1 30 27 13 8 13
267 0 1 36 33 16 10 12
268 0 0 34 38 13 8 10
269 0 1 35 36 16 12 13
270 0 0 34 33 16 14 12
271 0 0 32 39 12 7 9
272 0 1 33 29 7 6 15
273 0 0 33 32 16 11 13
274 0 1 26 34 5 4 13
275 0 0 35 38 16 9 13
276 0 0 21 17 4 5 15
277 0 0 38 35 12 9 15
278 0 0 35 32 15 11 14
279 0 1 33 34 14 12 15
280 0 0 37 36 11 9 11
281 0 0 38 31 16 12 15
282 0 1 34 35 15 10 14
283 0 0 27 29 12 9 13
284 0 1 16 22 6 6 12
285 0 0 40 41 16 10 16
286 0 0 36 36 10 9 16
287 0 1 42 42 15 13 9
288 0 1 30 33 14 12 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender Connected Separate Learning Software
-1.149950 0.099678 0.010933 -0.000912 0.036760 0.028991
Happiness
0.036099
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.9047 -0.4138 0.1288 0.3412 0.9203
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.149950 0.288831 -3.981 8.73e-05 ***
Gender 0.099678 0.055311 1.802 0.07260 .
Connected 0.010933 0.007834 1.396 0.16393
Separate -0.000912 0.008170 -0.112 0.91120
Learning 0.036760 0.013978 2.630 0.00901 **
Software 0.028991 0.014999 1.933 0.05426 .
Happiness 0.036099 0.011139 3.241 0.00133 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4504 on 281 degrees of freedom
Multiple R-squared: 0.2006, Adjusted R-squared: 0.1836
F-statistic: 11.75 on 6 and 281 DF, p-value: 9.325e-12
> 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,] 1.111101e-48 2.222202e-48 1
[2,] 1.069915e-67 2.139829e-67 1
[3,] 1.160785e-78 2.321570e-78 1
[4,] 3.759557e-106 7.519114e-106 1
[5,] 2.083791e-108 4.167581e-108 1
[6,] 7.223297e-124 1.444659e-123 1
[7,] 0.000000e+00 0.000000e+00 1
[8,] 1.960013e-166 3.920027e-166 1
[9,] 7.922458e-172 1.584492e-171 1
[10,] 3.393253e-186 6.786507e-186 1
[11,] 3.111220e-211 6.222439e-211 1
[12,] 3.818317e-245 7.636633e-245 1
[13,] 1.268108e-235 2.536216e-235 1
[14,] 2.810998e-247 5.621997e-247 1
[15,] 8.783044e-266 1.756609e-265 1
[16,] 7.359563e-285 1.471913e-284 1
[17,] 0.000000e+00 0.000000e+00 1
[18,] 8.427680e-315 1.685536e-314 1
[19,] 0.000000e+00 0.000000e+00 1
[20,] 0.000000e+00 0.000000e+00 1
[21,] 0.000000e+00 0.000000e+00 1
[22,] 0.000000e+00 0.000000e+00 1
[23,] 0.000000e+00 0.000000e+00 1
[24,] 0.000000e+00 0.000000e+00 1
[25,] 0.000000e+00 0.000000e+00 1
[26,] 0.000000e+00 0.000000e+00 1
[27,] 0.000000e+00 0.000000e+00 1
[28,] 0.000000e+00 0.000000e+00 1
[29,] 0.000000e+00 0.000000e+00 1
[30,] 0.000000e+00 0.000000e+00 1
[31,] 0.000000e+00 0.000000e+00 1
[32,] 0.000000e+00 0.000000e+00 1
[33,] 0.000000e+00 0.000000e+00 1
[34,] 0.000000e+00 0.000000e+00 1
[35,] 0.000000e+00 0.000000e+00 1
[36,] 0.000000e+00 0.000000e+00 1
[37,] 0.000000e+00 0.000000e+00 1
[38,] 0.000000e+00 0.000000e+00 1
[39,] 0.000000e+00 0.000000e+00 1
[40,] 0.000000e+00 0.000000e+00 1
[41,] 0.000000e+00 0.000000e+00 1
[42,] 0.000000e+00 0.000000e+00 1
[43,] 0.000000e+00 0.000000e+00 1
[44,] 0.000000e+00 0.000000e+00 1
[45,] 0.000000e+00 0.000000e+00 1
[46,] 0.000000e+00 0.000000e+00 1
[47,] 0.000000e+00 0.000000e+00 1
[48,] 0.000000e+00 0.000000e+00 1
[49,] 0.000000e+00 0.000000e+00 1
[50,] 0.000000e+00 0.000000e+00 1
[51,] 0.000000e+00 0.000000e+00 1
[52,] 0.000000e+00 0.000000e+00 1
[53,] 0.000000e+00 0.000000e+00 1
[54,] 0.000000e+00 0.000000e+00 1
[55,] 0.000000e+00 0.000000e+00 1
[56,] 0.000000e+00 0.000000e+00 1
[57,] 0.000000e+00 0.000000e+00 1
[58,] 0.000000e+00 0.000000e+00 1
[59,] 0.000000e+00 0.000000e+00 1
[60,] 0.000000e+00 0.000000e+00 1
[61,] 0.000000e+00 0.000000e+00 1
[62,] 0.000000e+00 0.000000e+00 1
[63,] 0.000000e+00 0.000000e+00 1
[64,] 0.000000e+00 0.000000e+00 1
[65,] 0.000000e+00 0.000000e+00 1
[66,] 0.000000e+00 0.000000e+00 1
[67,] 0.000000e+00 0.000000e+00 1
[68,] 0.000000e+00 0.000000e+00 1
[69,] 0.000000e+00 0.000000e+00 1
[70,] 0.000000e+00 0.000000e+00 1
[71,] 0.000000e+00 0.000000e+00 1
[72,] 0.000000e+00 0.000000e+00 1
[73,] 0.000000e+00 0.000000e+00 1
[74,] 0.000000e+00 0.000000e+00 1
[75,] 0.000000e+00 0.000000e+00 1
[76,] 0.000000e+00 0.000000e+00 1
[77,] 0.000000e+00 0.000000e+00 1
[78,] 0.000000e+00 0.000000e+00 1
[79,] 0.000000e+00 0.000000e+00 1
[80,] 0.000000e+00 0.000000e+00 1
[81,] 0.000000e+00 0.000000e+00 1
[82,] 0.000000e+00 0.000000e+00 1
[83,] 0.000000e+00 0.000000e+00 1
[84,] 0.000000e+00 0.000000e+00 1
[85,] 0.000000e+00 0.000000e+00 1
[86,] 0.000000e+00 0.000000e+00 1
[87,] 0.000000e+00 0.000000e+00 1
[88,] 0.000000e+00 0.000000e+00 1
[89,] 0.000000e+00 0.000000e+00 1
[90,] 0.000000e+00 0.000000e+00 1
[91,] 0.000000e+00 0.000000e+00 1
[92,] 0.000000e+00 0.000000e+00 1
[93,] 0.000000e+00 0.000000e+00 1
[94,] 0.000000e+00 0.000000e+00 1
[95,] 0.000000e+00 0.000000e+00 1
[96,] 0.000000e+00 0.000000e+00 1
[97,] 0.000000e+00 0.000000e+00 1
[98,] 0.000000e+00 0.000000e+00 1
[99,] 0.000000e+00 0.000000e+00 1
[100,] 0.000000e+00 0.000000e+00 1
[101,] 0.000000e+00 0.000000e+00 1
[102,] 0.000000e+00 0.000000e+00 1
[103,] 0.000000e+00 0.000000e+00 1
[104,] 0.000000e+00 0.000000e+00 1
[105,] 0.000000e+00 0.000000e+00 1
[106,] 0.000000e+00 0.000000e+00 1
[107,] 0.000000e+00 0.000000e+00 1
[108,] 0.000000e+00 0.000000e+00 1
[109,] 0.000000e+00 0.000000e+00 1
[110,] 0.000000e+00 0.000000e+00 1
[111,] 0.000000e+00 0.000000e+00 1
[112,] 0.000000e+00 0.000000e+00 1
[113,] 0.000000e+00 0.000000e+00 1
[114,] 0.000000e+00 0.000000e+00 1
[115,] 0.000000e+00 0.000000e+00 1
[116,] 0.000000e+00 0.000000e+00 1
[117,] 0.000000e+00 0.000000e+00 1
[118,] 0.000000e+00 0.000000e+00 1
[119,] 0.000000e+00 0.000000e+00 1
[120,] 0.000000e+00 0.000000e+00 1
[121,] 0.000000e+00 0.000000e+00 1
[122,] 0.000000e+00 0.000000e+00 1
[123,] 0.000000e+00 0.000000e+00 1
[124,] 0.000000e+00 0.000000e+00 1
[125,] 0.000000e+00 0.000000e+00 1
[126,] 0.000000e+00 0.000000e+00 1
[127,] 0.000000e+00 0.000000e+00 1
[128,] 0.000000e+00 0.000000e+00 1
[129,] 0.000000e+00 0.000000e+00 1
[130,] 0.000000e+00 0.000000e+00 1
[131,] 0.000000e+00 0.000000e+00 1
[132,] 0.000000e+00 0.000000e+00 1
[133,] 0.000000e+00 0.000000e+00 1
[134,] 0.000000e+00 0.000000e+00 1
[135,] 0.000000e+00 0.000000e+00 1
[136,] 0.000000e+00 0.000000e+00 1
[137,] 0.000000e+00 0.000000e+00 1
[138,] 0.000000e+00 0.000000e+00 1
[139,] 0.000000e+00 0.000000e+00 1
[140,] 0.000000e+00 0.000000e+00 1
[141,] 0.000000e+00 0.000000e+00 1
[142,] 0.000000e+00 0.000000e+00 1
[143,] 0.000000e+00 0.000000e+00 1
[144,] 0.000000e+00 0.000000e+00 1
[145,] 0.000000e+00 0.000000e+00 1
[146,] 0.000000e+00 0.000000e+00 1
[147,] 0.000000e+00 0.000000e+00 1
[148,] 0.000000e+00 0.000000e+00 1
[149,] 1.000000e+00 0.000000e+00 0
[150,] 1.000000e+00 0.000000e+00 0
[151,] 1.000000e+00 0.000000e+00 0
[152,] 1.000000e+00 0.000000e+00 0
[153,] 1.000000e+00 0.000000e+00 0
[154,] 1.000000e+00 0.000000e+00 0
[155,] 1.000000e+00 0.000000e+00 0
[156,] 1.000000e+00 0.000000e+00 0
[157,] 1.000000e+00 0.000000e+00 0
[158,] 1.000000e+00 0.000000e+00 0
[159,] 1.000000e+00 0.000000e+00 0
[160,] 1.000000e+00 0.000000e+00 0
[161,] 1.000000e+00 0.000000e+00 0
[162,] 1.000000e+00 0.000000e+00 0
[163,] 1.000000e+00 0.000000e+00 0
[164,] 1.000000e+00 0.000000e+00 0
[165,] 1.000000e+00 0.000000e+00 0
[166,] 1.000000e+00 0.000000e+00 0
[167,] 1.000000e+00 0.000000e+00 0
[168,] 1.000000e+00 0.000000e+00 0
[169,] 1.000000e+00 0.000000e+00 0
[170,] 1.000000e+00 0.000000e+00 0
[171,] 1.000000e+00 0.000000e+00 0
[172,] 1.000000e+00 0.000000e+00 0
[173,] 1.000000e+00 0.000000e+00 0
[174,] 1.000000e+00 0.000000e+00 0
[175,] 1.000000e+00 0.000000e+00 0
[176,] 1.000000e+00 0.000000e+00 0
[177,] 1.000000e+00 0.000000e+00 0
[178,] 1.000000e+00 0.000000e+00 0
[179,] 1.000000e+00 0.000000e+00 0
[180,] 1.000000e+00 0.000000e+00 0
[181,] 1.000000e+00 0.000000e+00 0
[182,] 1.000000e+00 0.000000e+00 0
[183,] 1.000000e+00 0.000000e+00 0
[184,] 1.000000e+00 0.000000e+00 0
[185,] 1.000000e+00 0.000000e+00 0
[186,] 1.000000e+00 0.000000e+00 0
[187,] 1.000000e+00 0.000000e+00 0
[188,] 1.000000e+00 0.000000e+00 0
[189,] 1.000000e+00 0.000000e+00 0
[190,] 1.000000e+00 0.000000e+00 0
[191,] 1.000000e+00 0.000000e+00 0
[192,] 1.000000e+00 0.000000e+00 0
[193,] 1.000000e+00 0.000000e+00 0
[194,] 1.000000e+00 0.000000e+00 0
[195,] 1.000000e+00 0.000000e+00 0
[196,] 1.000000e+00 0.000000e+00 0
[197,] 1.000000e+00 0.000000e+00 0
[198,] 1.000000e+00 0.000000e+00 0
[199,] 1.000000e+00 0.000000e+00 0
[200,] 1.000000e+00 0.000000e+00 0
[201,] 1.000000e+00 0.000000e+00 0
[202,] 1.000000e+00 0.000000e+00 0
[203,] 1.000000e+00 0.000000e+00 0
[204,] 1.000000e+00 0.000000e+00 0
[205,] 1.000000e+00 0.000000e+00 0
[206,] 1.000000e+00 0.000000e+00 0
[207,] 1.000000e+00 0.000000e+00 0
[208,] 1.000000e+00 0.000000e+00 0
[209,] 1.000000e+00 0.000000e+00 0
[210,] 1.000000e+00 0.000000e+00 0
[211,] 1.000000e+00 0.000000e+00 0
[212,] 1.000000e+00 0.000000e+00 0
[213,] 1.000000e+00 0.000000e+00 0
[214,] 1.000000e+00 0.000000e+00 0
[215,] 1.000000e+00 0.000000e+00 0
[216,] 1.000000e+00 0.000000e+00 0
[217,] 1.000000e+00 0.000000e+00 0
[218,] 1.000000e+00 0.000000e+00 0
[219,] 1.000000e+00 0.000000e+00 0
[220,] 1.000000e+00 0.000000e+00 0
[221,] 1.000000e+00 0.000000e+00 0
[222,] 1.000000e+00 0.000000e+00 0
[223,] 1.000000e+00 0.000000e+00 0
[224,] 1.000000e+00 0.000000e+00 0
[225,] 1.000000e+00 0.000000e+00 0
[226,] 1.000000e+00 0.000000e+00 0
[227,] 1.000000e+00 0.000000e+00 0
[228,] 1.000000e+00 0.000000e+00 0
[229,] 1.000000e+00 0.000000e+00 0
[230,] 1.000000e+00 0.000000e+00 0
[231,] 1.000000e+00 0.000000e+00 0
[232,] 1.000000e+00 0.000000e+00 0
[233,] 1.000000e+00 0.000000e+00 0
[234,] 1.000000e+00 0.000000e+00 0
[235,] 1.000000e+00 0.000000e+00 0
[236,] 1.000000e+00 0.000000e+00 0
[237,] 1.000000e+00 0.000000e+00 0
[238,] 1.000000e+00 0.000000e+00 0
[239,] 1.000000e+00 0.000000e+00 0
[240,] 1.000000e+00 0.000000e+00 0
[241,] 1.000000e+00 0.000000e+00 0
[242,] 1.000000e+00 0.000000e+00 0
[243,] 1.000000e+00 0.000000e+00 0
[244,] 1.000000e+00 0.000000e+00 0
[245,] 1.000000e+00 0.000000e+00 0
[246,] 1.000000e+00 0.000000e+00 0
[247,] 1.000000e+00 0.000000e+00 0
[248,] 1.000000e+00 0.000000e+00 0
[249,] 1.000000e+00 0.000000e+00 0
[250,] 1.000000e+00 0.000000e+00 0
[251,] 1.000000e+00 0.000000e+00 0
[252,] 1.000000e+00 0.000000e+00 0
[253,] 1.000000e+00 0.000000e+00 0
[254,] 1.000000e+00 0.000000e+00 0
[255,] 1.000000e+00 0.000000e+00 0
[256,] 1.000000e+00 0.000000e+00 0
[257,] 1.000000e+00 0.000000e+00 0
[258,] 1.000000e+00 0.000000e+00 0
[259,] 1.000000e+00 0.000000e+00 0
[260,] 1.000000e+00 0.000000e+00 0
[261,] 1.000000e+00 0.000000e+00 0
[262,] 1.000000e+00 0.000000e+00 0
[263,] 1.000000e+00 0.000000e+00 0
[264,] 1.000000e+00 0.000000e+00 0
[265,] 1.000000e+00 0.000000e+00 0
[266,] 1.000000e+00 0.000000e+00 0
[267,] 1.000000e+00 0.000000e+00 0
[268,] 1.000000e+00 0.000000e+00 0
[269,] 1.000000e+00 0.000000e+00 0
> postscript(file="/var/wessaorg/rcomp/tmp/1pacn1386608298.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2gx851386608298.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3owsb1386608298.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4c1m61386608298.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5yxuu1386608298.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
0.30551142 0.09622152 0.22380069 0.68258641 0.24318153 0.27648931
7 8 9 10 11 12
0.10043324 0.24871569 0.28458176 0.32659905 0.27101107 0.06575433
13 14 15 16 17 18
0.46936795 0.17389115 0.01365383 0.42550692 0.43884569 -0.02730630
19 20 21 22 23 24
0.27224692 0.21595901 0.27334301 0.40647421 0.07782574 0.34106456
25 26 27 28 29 30
0.16624705 0.30450195 0.33262396 0.26056072 0.24203679 0.60668032
31 32 33 34 35 36
0.26274010 0.74160497 0.25578600 0.23475173 0.43766491 0.68206683
37 38 39 40 41 42
0.90775086 0.38243598 0.26973023 0.45002951 0.35505377 0.26852502
43 44 45 46 47 48
0.01602438 0.55964844 0.31431465 0.52832420 0.23737219 0.30870030
49 50 51 52 53 54
0.17059144 0.43185557 0.16344804 0.51751392 0.64408357 0.31496876
55 56 57 58 59 60
0.92029314 0.44759751 0.14125171 0.28668095 0.47476683 0.38736213
61 62 63 64 65 66
0.91464350 0.36015946 0.31934014 0.40433726 0.22927976 0.43823954
67 68 69 70 71 72
0.22395396 0.43281002 0.32986271 0.32349690 0.66241077 0.33487558
73 74 75 76 77 78
0.10507269 0.16284272 0.39750766 0.33221341 0.10076348 0.33046889
79 80 81 82 83 84
0.50483636 0.36470854 0.34445098 0.22381869 0.29864405 0.31963334
85 86 87 88 89 90
0.33429456 0.45301626 0.27904762 0.35705462 0.56758750 0.42845578
91 92 93 94 95 96
0.15678890 0.22746667 0.32126874 0.46551079 0.20105863 0.27943365
97 98 99 100 101 102
0.34141995 0.22669717 0.48688705 0.05992698 0.43709479 -0.05722778
103 104 105 106 107 108
0.55823491 0.27650022 0.59114487 0.11633670 0.25972264 0.52022549
109 110 111 112 113 114
0.61340602 0.27574791 0.35031527 0.32469487 0.29594326 0.15846045
115 116 117 118 119 120
0.32904822 0.46307250 0.41923123 0.22620640 0.59293109 0.26553827
121 122 123 124 125 126
0.30312370 0.14488167 0.21518227 0.24912614 0.29775008 0.04727906
127 128 129 130 131 132
0.22411189 0.39620237 0.48282863 0.86161297 0.28511934 0.58060892
133 134 135 136 137 138
0.37606291 0.24998307 0.72799814 0.43445629 0.66283841 0.48408876
139 140 141 142 143 144
0.70837738 0.36984769 0.46579771 0.18902429 0.38345260 0.46410884
145 146 147 148 149 150
0.10888747 0.56632734 0.45912055 0.73278446 0.52226235 0.22725365
151 152 153 154 155 156
0.38621749 0.50788796 0.59436254 0.64893663 0.57694558 0.22411189
157 158 159 160 161 162
0.33138089 0.62883236 -0.25031348 -0.34369799 -0.84379202 -0.48818714
163 164 165 166 167 168
-0.08170213 -0.48687548 -0.39663113 -0.43746313 -0.37244579 -0.20946537
169 170 171 172 173 174
-0.82321318 -0.32230921 -0.24705166 -0.68438093 -0.50757879 -0.32209632
175 176 177 178 179 180
-0.28591077 -0.64251692 -0.43057051 -0.75192751 -0.25294036 -0.83093748
181 182 183 184 185 186
-0.64100332 -0.38427817 -0.67209652 -0.66184188 -0.53338792 -0.20722453
187 188 189 190 191 192
-0.11348015 -0.52637876 -0.77014562 -0.33906679 -0.60279452 -0.57967908
193 194 195 196 197 198
-0.55240064 -0.64764701 -0.71821640 -0.24509868 -0.41162887 -0.43112059
199 200 201 202 203 204
-0.47246505 -0.45089217 -0.60432531 -0.70040875 -0.71109101 -0.71351393
205 206 207 208 209 210
-0.37710328 -0.61057389 -0.40526305 -0.62477602 -0.43173315 -0.29317780
211 212 213 214 215 216
-0.63360997 -0.47941272 -0.83610547 -0.65504760 -0.21032230 -0.74853031
217 218 219 220 221 222
-0.42547560 -0.61481002 -0.54162099 -0.40331632 -0.31236383 -0.65022321
223 224 225 226 227 228
-0.38858202 -0.14575157 -0.28898413 -0.45130446 -0.15858881 -0.47249118
229 230 231 232 233 234
-0.90473290 -0.63764400 -0.30672960 0.01861274 -0.51407827 -0.53556087
235 236 237 238 239 240
-0.19484019 -0.22229620 -0.24960360 -0.01839979 -0.28762565 -0.20532746
241 242 243 244 245 246
-0.41283581 -0.44804080 -0.17234435 -0.29631156 -0.30674935 -0.22148171
247 248 249 250 251 252
-0.39666368 -0.59970314 -0.38869754 -0.45840998 -0.45991011 -0.33093017
253 254 255 256 257 258
-0.66151176 0.09997267 -0.59325060 -0.48085782 -0.11453207 -0.41887151
259 260 261 262 263 264
-0.42258633 -0.39891613 -0.32109120 -0.32003234 -0.64477577 -0.07667592
265 266 267 268 269 270
-0.31479312 -0.43219238 -0.62448290 -0.25791790 -0.70489564 -0.61890447
271 272 273 274 275 276
-0.13328948 -0.25682195 -0.55800787 0.02797000 -0.51641905 0.10238043
277 278 279 280 281 282
-0.47711162 -0.57921273 -0.68353111 -0.28411121 -0.71477419 -0.63623045
283 284 285 286 287 288
-0.29012248 0.06771233 -0.70563623 -0.41691228 -0.62379104 -0.61554515
> postscript(file="/var/wessaorg/rcomp/tmp/6wuka1386608298.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 0.30551142 NA
1 0.09622152 0.30551142
2 0.22380069 0.09622152
3 0.68258641 0.22380069
4 0.24318153 0.68258641
5 0.27648931 0.24318153
6 0.10043324 0.27648931
7 0.24871569 0.10043324
8 0.28458176 0.24871569
9 0.32659905 0.28458176
10 0.27101107 0.32659905
11 0.06575433 0.27101107
12 0.46936795 0.06575433
13 0.17389115 0.46936795
14 0.01365383 0.17389115
15 0.42550692 0.01365383
16 0.43884569 0.42550692
17 -0.02730630 0.43884569
18 0.27224692 -0.02730630
19 0.21595901 0.27224692
20 0.27334301 0.21595901
21 0.40647421 0.27334301
22 0.07782574 0.40647421
23 0.34106456 0.07782574
24 0.16624705 0.34106456
25 0.30450195 0.16624705
26 0.33262396 0.30450195
27 0.26056072 0.33262396
28 0.24203679 0.26056072
29 0.60668032 0.24203679
30 0.26274010 0.60668032
31 0.74160497 0.26274010
32 0.25578600 0.74160497
33 0.23475173 0.25578600
34 0.43766491 0.23475173
35 0.68206683 0.43766491
36 0.90775086 0.68206683
37 0.38243598 0.90775086
38 0.26973023 0.38243598
39 0.45002951 0.26973023
40 0.35505377 0.45002951
41 0.26852502 0.35505377
42 0.01602438 0.26852502
43 0.55964844 0.01602438
44 0.31431465 0.55964844
45 0.52832420 0.31431465
46 0.23737219 0.52832420
47 0.30870030 0.23737219
48 0.17059144 0.30870030
49 0.43185557 0.17059144
50 0.16344804 0.43185557
51 0.51751392 0.16344804
52 0.64408357 0.51751392
53 0.31496876 0.64408357
54 0.92029314 0.31496876
55 0.44759751 0.92029314
56 0.14125171 0.44759751
57 0.28668095 0.14125171
58 0.47476683 0.28668095
59 0.38736213 0.47476683
60 0.91464350 0.38736213
61 0.36015946 0.91464350
62 0.31934014 0.36015946
63 0.40433726 0.31934014
64 0.22927976 0.40433726
65 0.43823954 0.22927976
66 0.22395396 0.43823954
67 0.43281002 0.22395396
68 0.32986271 0.43281002
69 0.32349690 0.32986271
70 0.66241077 0.32349690
71 0.33487558 0.66241077
72 0.10507269 0.33487558
73 0.16284272 0.10507269
74 0.39750766 0.16284272
75 0.33221341 0.39750766
76 0.10076348 0.33221341
77 0.33046889 0.10076348
78 0.50483636 0.33046889
79 0.36470854 0.50483636
80 0.34445098 0.36470854
81 0.22381869 0.34445098
82 0.29864405 0.22381869
83 0.31963334 0.29864405
84 0.33429456 0.31963334
85 0.45301626 0.33429456
86 0.27904762 0.45301626
87 0.35705462 0.27904762
88 0.56758750 0.35705462
89 0.42845578 0.56758750
90 0.15678890 0.42845578
91 0.22746667 0.15678890
92 0.32126874 0.22746667
93 0.46551079 0.32126874
94 0.20105863 0.46551079
95 0.27943365 0.20105863
96 0.34141995 0.27943365
97 0.22669717 0.34141995
98 0.48688705 0.22669717
99 0.05992698 0.48688705
100 0.43709479 0.05992698
101 -0.05722778 0.43709479
102 0.55823491 -0.05722778
103 0.27650022 0.55823491
104 0.59114487 0.27650022
105 0.11633670 0.59114487
106 0.25972264 0.11633670
107 0.52022549 0.25972264
108 0.61340602 0.52022549
109 0.27574791 0.61340602
110 0.35031527 0.27574791
111 0.32469487 0.35031527
112 0.29594326 0.32469487
113 0.15846045 0.29594326
114 0.32904822 0.15846045
115 0.46307250 0.32904822
116 0.41923123 0.46307250
117 0.22620640 0.41923123
118 0.59293109 0.22620640
119 0.26553827 0.59293109
120 0.30312370 0.26553827
121 0.14488167 0.30312370
122 0.21518227 0.14488167
123 0.24912614 0.21518227
124 0.29775008 0.24912614
125 0.04727906 0.29775008
126 0.22411189 0.04727906
127 0.39620237 0.22411189
128 0.48282863 0.39620237
129 0.86161297 0.48282863
130 0.28511934 0.86161297
131 0.58060892 0.28511934
132 0.37606291 0.58060892
133 0.24998307 0.37606291
134 0.72799814 0.24998307
135 0.43445629 0.72799814
136 0.66283841 0.43445629
137 0.48408876 0.66283841
138 0.70837738 0.48408876
139 0.36984769 0.70837738
140 0.46579771 0.36984769
141 0.18902429 0.46579771
142 0.38345260 0.18902429
143 0.46410884 0.38345260
144 0.10888747 0.46410884
145 0.56632734 0.10888747
146 0.45912055 0.56632734
147 0.73278446 0.45912055
148 0.52226235 0.73278446
149 0.22725365 0.52226235
150 0.38621749 0.22725365
151 0.50788796 0.38621749
152 0.59436254 0.50788796
153 0.64893663 0.59436254
154 0.57694558 0.64893663
155 0.22411189 0.57694558
156 0.33138089 0.22411189
157 0.62883236 0.33138089
158 -0.25031348 0.62883236
159 -0.34369799 -0.25031348
160 -0.84379202 -0.34369799
161 -0.48818714 -0.84379202
162 -0.08170213 -0.48818714
163 -0.48687548 -0.08170213
164 -0.39663113 -0.48687548
165 -0.43746313 -0.39663113
166 -0.37244579 -0.43746313
167 -0.20946537 -0.37244579
168 -0.82321318 -0.20946537
169 -0.32230921 -0.82321318
170 -0.24705166 -0.32230921
171 -0.68438093 -0.24705166
172 -0.50757879 -0.68438093
173 -0.32209632 -0.50757879
174 -0.28591077 -0.32209632
175 -0.64251692 -0.28591077
176 -0.43057051 -0.64251692
177 -0.75192751 -0.43057051
178 -0.25294036 -0.75192751
179 -0.83093748 -0.25294036
180 -0.64100332 -0.83093748
181 -0.38427817 -0.64100332
182 -0.67209652 -0.38427817
183 -0.66184188 -0.67209652
184 -0.53338792 -0.66184188
185 -0.20722453 -0.53338792
186 -0.11348015 -0.20722453
187 -0.52637876 -0.11348015
188 -0.77014562 -0.52637876
189 -0.33906679 -0.77014562
190 -0.60279452 -0.33906679
191 -0.57967908 -0.60279452
192 -0.55240064 -0.57967908
193 -0.64764701 -0.55240064
194 -0.71821640 -0.64764701
195 -0.24509868 -0.71821640
196 -0.41162887 -0.24509868
197 -0.43112059 -0.41162887
198 -0.47246505 -0.43112059
199 -0.45089217 -0.47246505
200 -0.60432531 -0.45089217
201 -0.70040875 -0.60432531
202 -0.71109101 -0.70040875
203 -0.71351393 -0.71109101
204 -0.37710328 -0.71351393
205 -0.61057389 -0.37710328
206 -0.40526305 -0.61057389
207 -0.62477602 -0.40526305
208 -0.43173315 -0.62477602
209 -0.29317780 -0.43173315
210 -0.63360997 -0.29317780
211 -0.47941272 -0.63360997
212 -0.83610547 -0.47941272
213 -0.65504760 -0.83610547
214 -0.21032230 -0.65504760
215 -0.74853031 -0.21032230
216 -0.42547560 -0.74853031
217 -0.61481002 -0.42547560
218 -0.54162099 -0.61481002
219 -0.40331632 -0.54162099
220 -0.31236383 -0.40331632
221 -0.65022321 -0.31236383
222 -0.38858202 -0.65022321
223 -0.14575157 -0.38858202
224 -0.28898413 -0.14575157
225 -0.45130446 -0.28898413
226 -0.15858881 -0.45130446
227 -0.47249118 -0.15858881
228 -0.90473290 -0.47249118
229 -0.63764400 -0.90473290
230 -0.30672960 -0.63764400
231 0.01861274 -0.30672960
232 -0.51407827 0.01861274
233 -0.53556087 -0.51407827
234 -0.19484019 -0.53556087
235 -0.22229620 -0.19484019
236 -0.24960360 -0.22229620
237 -0.01839979 -0.24960360
238 -0.28762565 -0.01839979
239 -0.20532746 -0.28762565
240 -0.41283581 -0.20532746
241 -0.44804080 -0.41283581
242 -0.17234435 -0.44804080
243 -0.29631156 -0.17234435
244 -0.30674935 -0.29631156
245 -0.22148171 -0.30674935
246 -0.39666368 -0.22148171
247 -0.59970314 -0.39666368
248 -0.38869754 -0.59970314
249 -0.45840998 -0.38869754
250 -0.45991011 -0.45840998
251 -0.33093017 -0.45991011
252 -0.66151176 -0.33093017
253 0.09997267 -0.66151176
254 -0.59325060 0.09997267
255 -0.48085782 -0.59325060
256 -0.11453207 -0.48085782
257 -0.41887151 -0.11453207
258 -0.42258633 -0.41887151
259 -0.39891613 -0.42258633
260 -0.32109120 -0.39891613
261 -0.32003234 -0.32109120
262 -0.64477577 -0.32003234
263 -0.07667592 -0.64477577
264 -0.31479312 -0.07667592
265 -0.43219238 -0.31479312
266 -0.62448290 -0.43219238
267 -0.25791790 -0.62448290
268 -0.70489564 -0.25791790
269 -0.61890447 -0.70489564
270 -0.13328948 -0.61890447
271 -0.25682195 -0.13328948
272 -0.55800787 -0.25682195
273 0.02797000 -0.55800787
274 -0.51641905 0.02797000
275 0.10238043 -0.51641905
276 -0.47711162 0.10238043
277 -0.57921273 -0.47711162
278 -0.68353111 -0.57921273
279 -0.28411121 -0.68353111
280 -0.71477419 -0.28411121
281 -0.63623045 -0.71477419
282 -0.29012248 -0.63623045
283 0.06771233 -0.29012248
284 -0.70563623 0.06771233
285 -0.41691228 -0.70563623
286 -0.62379104 -0.41691228
287 -0.61554515 -0.62379104
288 NA -0.61554515
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.09622152 0.30551142
[2,] 0.22380069 0.09622152
[3,] 0.68258641 0.22380069
[4,] 0.24318153 0.68258641
[5,] 0.27648931 0.24318153
[6,] 0.10043324 0.27648931
[7,] 0.24871569 0.10043324
[8,] 0.28458176 0.24871569
[9,] 0.32659905 0.28458176
[10,] 0.27101107 0.32659905
[11,] 0.06575433 0.27101107
[12,] 0.46936795 0.06575433
[13,] 0.17389115 0.46936795
[14,] 0.01365383 0.17389115
[15,] 0.42550692 0.01365383
[16,] 0.43884569 0.42550692
[17,] -0.02730630 0.43884569
[18,] 0.27224692 -0.02730630
[19,] 0.21595901 0.27224692
[20,] 0.27334301 0.21595901
[21,] 0.40647421 0.27334301
[22,] 0.07782574 0.40647421
[23,] 0.34106456 0.07782574
[24,] 0.16624705 0.34106456
[25,] 0.30450195 0.16624705
[26,] 0.33262396 0.30450195
[27,] 0.26056072 0.33262396
[28,] 0.24203679 0.26056072
[29,] 0.60668032 0.24203679
[30,] 0.26274010 0.60668032
[31,] 0.74160497 0.26274010
[32,] 0.25578600 0.74160497
[33,] 0.23475173 0.25578600
[34,] 0.43766491 0.23475173
[35,] 0.68206683 0.43766491
[36,] 0.90775086 0.68206683
[37,] 0.38243598 0.90775086
[38,] 0.26973023 0.38243598
[39,] 0.45002951 0.26973023
[40,] 0.35505377 0.45002951
[41,] 0.26852502 0.35505377
[42,] 0.01602438 0.26852502
[43,] 0.55964844 0.01602438
[44,] 0.31431465 0.55964844
[45,] 0.52832420 0.31431465
[46,] 0.23737219 0.52832420
[47,] 0.30870030 0.23737219
[48,] 0.17059144 0.30870030
[49,] 0.43185557 0.17059144
[50,] 0.16344804 0.43185557
[51,] 0.51751392 0.16344804
[52,] 0.64408357 0.51751392
[53,] 0.31496876 0.64408357
[54,] 0.92029314 0.31496876
[55,] 0.44759751 0.92029314
[56,] 0.14125171 0.44759751
[57,] 0.28668095 0.14125171
[58,] 0.47476683 0.28668095
[59,] 0.38736213 0.47476683
[60,] 0.91464350 0.38736213
[61,] 0.36015946 0.91464350
[62,] 0.31934014 0.36015946
[63,] 0.40433726 0.31934014
[64,] 0.22927976 0.40433726
[65,] 0.43823954 0.22927976
[66,] 0.22395396 0.43823954
[67,] 0.43281002 0.22395396
[68,] 0.32986271 0.43281002
[69,] 0.32349690 0.32986271
[70,] 0.66241077 0.32349690
[71,] 0.33487558 0.66241077
[72,] 0.10507269 0.33487558
[73,] 0.16284272 0.10507269
[74,] 0.39750766 0.16284272
[75,] 0.33221341 0.39750766
[76,] 0.10076348 0.33221341
[77,] 0.33046889 0.10076348
[78,] 0.50483636 0.33046889
[79,] 0.36470854 0.50483636
[80,] 0.34445098 0.36470854
[81,] 0.22381869 0.34445098
[82,] 0.29864405 0.22381869
[83,] 0.31963334 0.29864405
[84,] 0.33429456 0.31963334
[85,] 0.45301626 0.33429456
[86,] 0.27904762 0.45301626
[87,] 0.35705462 0.27904762
[88,] 0.56758750 0.35705462
[89,] 0.42845578 0.56758750
[90,] 0.15678890 0.42845578
[91,] 0.22746667 0.15678890
[92,] 0.32126874 0.22746667
[93,] 0.46551079 0.32126874
[94,] 0.20105863 0.46551079
[95,] 0.27943365 0.20105863
[96,] 0.34141995 0.27943365
[97,] 0.22669717 0.34141995
[98,] 0.48688705 0.22669717
[99,] 0.05992698 0.48688705
[100,] 0.43709479 0.05992698
[101,] -0.05722778 0.43709479
[102,] 0.55823491 -0.05722778
[103,] 0.27650022 0.55823491
[104,] 0.59114487 0.27650022
[105,] 0.11633670 0.59114487
[106,] 0.25972264 0.11633670
[107,] 0.52022549 0.25972264
[108,] 0.61340602 0.52022549
[109,] 0.27574791 0.61340602
[110,] 0.35031527 0.27574791
[111,] 0.32469487 0.35031527
[112,] 0.29594326 0.32469487
[113,] 0.15846045 0.29594326
[114,] 0.32904822 0.15846045
[115,] 0.46307250 0.32904822
[116,] 0.41923123 0.46307250
[117,] 0.22620640 0.41923123
[118,] 0.59293109 0.22620640
[119,] 0.26553827 0.59293109
[120,] 0.30312370 0.26553827
[121,] 0.14488167 0.30312370
[122,] 0.21518227 0.14488167
[123,] 0.24912614 0.21518227
[124,] 0.29775008 0.24912614
[125,] 0.04727906 0.29775008
[126,] 0.22411189 0.04727906
[127,] 0.39620237 0.22411189
[128,] 0.48282863 0.39620237
[129,] 0.86161297 0.48282863
[130,] 0.28511934 0.86161297
[131,] 0.58060892 0.28511934
[132,] 0.37606291 0.58060892
[133,] 0.24998307 0.37606291
[134,] 0.72799814 0.24998307
[135,] 0.43445629 0.72799814
[136,] 0.66283841 0.43445629
[137,] 0.48408876 0.66283841
[138,] 0.70837738 0.48408876
[139,] 0.36984769 0.70837738
[140,] 0.46579771 0.36984769
[141,] 0.18902429 0.46579771
[142,] 0.38345260 0.18902429
[143,] 0.46410884 0.38345260
[144,] 0.10888747 0.46410884
[145,] 0.56632734 0.10888747
[146,] 0.45912055 0.56632734
[147,] 0.73278446 0.45912055
[148,] 0.52226235 0.73278446
[149,] 0.22725365 0.52226235
[150,] 0.38621749 0.22725365
[151,] 0.50788796 0.38621749
[152,] 0.59436254 0.50788796
[153,] 0.64893663 0.59436254
[154,] 0.57694558 0.64893663
[155,] 0.22411189 0.57694558
[156,] 0.33138089 0.22411189
[157,] 0.62883236 0.33138089
[158,] -0.25031348 0.62883236
[159,] -0.34369799 -0.25031348
[160,] -0.84379202 -0.34369799
[161,] -0.48818714 -0.84379202
[162,] -0.08170213 -0.48818714
[163,] -0.48687548 -0.08170213
[164,] -0.39663113 -0.48687548
[165,] -0.43746313 -0.39663113
[166,] -0.37244579 -0.43746313
[167,] -0.20946537 -0.37244579
[168,] -0.82321318 -0.20946537
[169,] -0.32230921 -0.82321318
[170,] -0.24705166 -0.32230921
[171,] -0.68438093 -0.24705166
[172,] -0.50757879 -0.68438093
[173,] -0.32209632 -0.50757879
[174,] -0.28591077 -0.32209632
[175,] -0.64251692 -0.28591077
[176,] -0.43057051 -0.64251692
[177,] -0.75192751 -0.43057051
[178,] -0.25294036 -0.75192751
[179,] -0.83093748 -0.25294036
[180,] -0.64100332 -0.83093748
[181,] -0.38427817 -0.64100332
[182,] -0.67209652 -0.38427817
[183,] -0.66184188 -0.67209652
[184,] -0.53338792 -0.66184188
[185,] -0.20722453 -0.53338792
[186,] -0.11348015 -0.20722453
[187,] -0.52637876 -0.11348015
[188,] -0.77014562 -0.52637876
[189,] -0.33906679 -0.77014562
[190,] -0.60279452 -0.33906679
[191,] -0.57967908 -0.60279452
[192,] -0.55240064 -0.57967908
[193,] -0.64764701 -0.55240064
[194,] -0.71821640 -0.64764701
[195,] -0.24509868 -0.71821640
[196,] -0.41162887 -0.24509868
[197,] -0.43112059 -0.41162887
[198,] -0.47246505 -0.43112059
[199,] -0.45089217 -0.47246505
[200,] -0.60432531 -0.45089217
[201,] -0.70040875 -0.60432531
[202,] -0.71109101 -0.70040875
[203,] -0.71351393 -0.71109101
[204,] -0.37710328 -0.71351393
[205,] -0.61057389 -0.37710328
[206,] -0.40526305 -0.61057389
[207,] -0.62477602 -0.40526305
[208,] -0.43173315 -0.62477602
[209,] -0.29317780 -0.43173315
[210,] -0.63360997 -0.29317780
[211,] -0.47941272 -0.63360997
[212,] -0.83610547 -0.47941272
[213,] -0.65504760 -0.83610547
[214,] -0.21032230 -0.65504760
[215,] -0.74853031 -0.21032230
[216,] -0.42547560 -0.74853031
[217,] -0.61481002 -0.42547560
[218,] -0.54162099 -0.61481002
[219,] -0.40331632 -0.54162099
[220,] -0.31236383 -0.40331632
[221,] -0.65022321 -0.31236383
[222,] -0.38858202 -0.65022321
[223,] -0.14575157 -0.38858202
[224,] -0.28898413 -0.14575157
[225,] -0.45130446 -0.28898413
[226,] -0.15858881 -0.45130446
[227,] -0.47249118 -0.15858881
[228,] -0.90473290 -0.47249118
[229,] -0.63764400 -0.90473290
[230,] -0.30672960 -0.63764400
[231,] 0.01861274 -0.30672960
[232,] -0.51407827 0.01861274
[233,] -0.53556087 -0.51407827
[234,] -0.19484019 -0.53556087
[235,] -0.22229620 -0.19484019
[236,] -0.24960360 -0.22229620
[237,] -0.01839979 -0.24960360
[238,] -0.28762565 -0.01839979
[239,] -0.20532746 -0.28762565
[240,] -0.41283581 -0.20532746
[241,] -0.44804080 -0.41283581
[242,] -0.17234435 -0.44804080
[243,] -0.29631156 -0.17234435
[244,] -0.30674935 -0.29631156
[245,] -0.22148171 -0.30674935
[246,] -0.39666368 -0.22148171
[247,] -0.59970314 -0.39666368
[248,] -0.38869754 -0.59970314
[249,] -0.45840998 -0.38869754
[250,] -0.45991011 -0.45840998
[251,] -0.33093017 -0.45991011
[252,] -0.66151176 -0.33093017
[253,] 0.09997267 -0.66151176
[254,] -0.59325060 0.09997267
[255,] -0.48085782 -0.59325060
[256,] -0.11453207 -0.48085782
[257,] -0.41887151 -0.11453207
[258,] -0.42258633 -0.41887151
[259,] -0.39891613 -0.42258633
[260,] -0.32109120 -0.39891613
[261,] -0.32003234 -0.32109120
[262,] -0.64477577 -0.32003234
[263,] -0.07667592 -0.64477577
[264,] -0.31479312 -0.07667592
[265,] -0.43219238 -0.31479312
[266,] -0.62448290 -0.43219238
[267,] -0.25791790 -0.62448290
[268,] -0.70489564 -0.25791790
[269,] -0.61890447 -0.70489564
[270,] -0.13328948 -0.61890447
[271,] -0.25682195 -0.13328948
[272,] -0.55800787 -0.25682195
[273,] 0.02797000 -0.55800787
[274,] -0.51641905 0.02797000
[275,] 0.10238043 -0.51641905
[276,] -0.47711162 0.10238043
[277,] -0.57921273 -0.47711162
[278,] -0.68353111 -0.57921273
[279,] -0.28411121 -0.68353111
[280,] -0.71477419 -0.28411121
[281,] -0.63623045 -0.71477419
[282,] -0.29012248 -0.63623045
[283,] 0.06771233 -0.29012248
[284,] -0.70563623 0.06771233
[285,] -0.41691228 -0.70563623
[286,] -0.62379104 -0.41691228
[287,] -0.61554515 -0.62379104
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.09622152 0.30551142
2 0.22380069 0.09622152
3 0.68258641 0.22380069
4 0.24318153 0.68258641
5 0.27648931 0.24318153
6 0.10043324 0.27648931
7 0.24871569 0.10043324
8 0.28458176 0.24871569
9 0.32659905 0.28458176
10 0.27101107 0.32659905
11 0.06575433 0.27101107
12 0.46936795 0.06575433
13 0.17389115 0.46936795
14 0.01365383 0.17389115
15 0.42550692 0.01365383
16 0.43884569 0.42550692
17 -0.02730630 0.43884569
18 0.27224692 -0.02730630
19 0.21595901 0.27224692
20 0.27334301 0.21595901
21 0.40647421 0.27334301
22 0.07782574 0.40647421
23 0.34106456 0.07782574
24 0.16624705 0.34106456
25 0.30450195 0.16624705
26 0.33262396 0.30450195
27 0.26056072 0.33262396
28 0.24203679 0.26056072
29 0.60668032 0.24203679
30 0.26274010 0.60668032
31 0.74160497 0.26274010
32 0.25578600 0.74160497
33 0.23475173 0.25578600
34 0.43766491 0.23475173
35 0.68206683 0.43766491
36 0.90775086 0.68206683
37 0.38243598 0.90775086
38 0.26973023 0.38243598
39 0.45002951 0.26973023
40 0.35505377 0.45002951
41 0.26852502 0.35505377
42 0.01602438 0.26852502
43 0.55964844 0.01602438
44 0.31431465 0.55964844
45 0.52832420 0.31431465
46 0.23737219 0.52832420
47 0.30870030 0.23737219
48 0.17059144 0.30870030
49 0.43185557 0.17059144
50 0.16344804 0.43185557
51 0.51751392 0.16344804
52 0.64408357 0.51751392
53 0.31496876 0.64408357
54 0.92029314 0.31496876
55 0.44759751 0.92029314
56 0.14125171 0.44759751
57 0.28668095 0.14125171
58 0.47476683 0.28668095
59 0.38736213 0.47476683
60 0.91464350 0.38736213
61 0.36015946 0.91464350
62 0.31934014 0.36015946
63 0.40433726 0.31934014
64 0.22927976 0.40433726
65 0.43823954 0.22927976
66 0.22395396 0.43823954
67 0.43281002 0.22395396
68 0.32986271 0.43281002
69 0.32349690 0.32986271
70 0.66241077 0.32349690
71 0.33487558 0.66241077
72 0.10507269 0.33487558
73 0.16284272 0.10507269
74 0.39750766 0.16284272
75 0.33221341 0.39750766
76 0.10076348 0.33221341
77 0.33046889 0.10076348
78 0.50483636 0.33046889
79 0.36470854 0.50483636
80 0.34445098 0.36470854
81 0.22381869 0.34445098
82 0.29864405 0.22381869
83 0.31963334 0.29864405
84 0.33429456 0.31963334
85 0.45301626 0.33429456
86 0.27904762 0.45301626
87 0.35705462 0.27904762
88 0.56758750 0.35705462
89 0.42845578 0.56758750
90 0.15678890 0.42845578
91 0.22746667 0.15678890
92 0.32126874 0.22746667
93 0.46551079 0.32126874
94 0.20105863 0.46551079
95 0.27943365 0.20105863
96 0.34141995 0.27943365
97 0.22669717 0.34141995
98 0.48688705 0.22669717
99 0.05992698 0.48688705
100 0.43709479 0.05992698
101 -0.05722778 0.43709479
102 0.55823491 -0.05722778
103 0.27650022 0.55823491
104 0.59114487 0.27650022
105 0.11633670 0.59114487
106 0.25972264 0.11633670
107 0.52022549 0.25972264
108 0.61340602 0.52022549
109 0.27574791 0.61340602
110 0.35031527 0.27574791
111 0.32469487 0.35031527
112 0.29594326 0.32469487
113 0.15846045 0.29594326
114 0.32904822 0.15846045
115 0.46307250 0.32904822
116 0.41923123 0.46307250
117 0.22620640 0.41923123
118 0.59293109 0.22620640
119 0.26553827 0.59293109
120 0.30312370 0.26553827
121 0.14488167 0.30312370
122 0.21518227 0.14488167
123 0.24912614 0.21518227
124 0.29775008 0.24912614
125 0.04727906 0.29775008
126 0.22411189 0.04727906
127 0.39620237 0.22411189
128 0.48282863 0.39620237
129 0.86161297 0.48282863
130 0.28511934 0.86161297
131 0.58060892 0.28511934
132 0.37606291 0.58060892
133 0.24998307 0.37606291
134 0.72799814 0.24998307
135 0.43445629 0.72799814
136 0.66283841 0.43445629
137 0.48408876 0.66283841
138 0.70837738 0.48408876
139 0.36984769 0.70837738
140 0.46579771 0.36984769
141 0.18902429 0.46579771
142 0.38345260 0.18902429
143 0.46410884 0.38345260
144 0.10888747 0.46410884
145 0.56632734 0.10888747
146 0.45912055 0.56632734
147 0.73278446 0.45912055
148 0.52226235 0.73278446
149 0.22725365 0.52226235
150 0.38621749 0.22725365
151 0.50788796 0.38621749
152 0.59436254 0.50788796
153 0.64893663 0.59436254
154 0.57694558 0.64893663
155 0.22411189 0.57694558
156 0.33138089 0.22411189
157 0.62883236 0.33138089
158 -0.25031348 0.62883236
159 -0.34369799 -0.25031348
160 -0.84379202 -0.34369799
161 -0.48818714 -0.84379202
162 -0.08170213 -0.48818714
163 -0.48687548 -0.08170213
164 -0.39663113 -0.48687548
165 -0.43746313 -0.39663113
166 -0.37244579 -0.43746313
167 -0.20946537 -0.37244579
168 -0.82321318 -0.20946537
169 -0.32230921 -0.82321318
170 -0.24705166 -0.32230921
171 -0.68438093 -0.24705166
172 -0.50757879 -0.68438093
173 -0.32209632 -0.50757879
174 -0.28591077 -0.32209632
175 -0.64251692 -0.28591077
176 -0.43057051 -0.64251692
177 -0.75192751 -0.43057051
178 -0.25294036 -0.75192751
179 -0.83093748 -0.25294036
180 -0.64100332 -0.83093748
181 -0.38427817 -0.64100332
182 -0.67209652 -0.38427817
183 -0.66184188 -0.67209652
184 -0.53338792 -0.66184188
185 -0.20722453 -0.53338792
186 -0.11348015 -0.20722453
187 -0.52637876 -0.11348015
188 -0.77014562 -0.52637876
189 -0.33906679 -0.77014562
190 -0.60279452 -0.33906679
191 -0.57967908 -0.60279452
192 -0.55240064 -0.57967908
193 -0.64764701 -0.55240064
194 -0.71821640 -0.64764701
195 -0.24509868 -0.71821640
196 -0.41162887 -0.24509868
197 -0.43112059 -0.41162887
198 -0.47246505 -0.43112059
199 -0.45089217 -0.47246505
200 -0.60432531 -0.45089217
201 -0.70040875 -0.60432531
202 -0.71109101 -0.70040875
203 -0.71351393 -0.71109101
204 -0.37710328 -0.71351393
205 -0.61057389 -0.37710328
206 -0.40526305 -0.61057389
207 -0.62477602 -0.40526305
208 -0.43173315 -0.62477602
209 -0.29317780 -0.43173315
210 -0.63360997 -0.29317780
211 -0.47941272 -0.63360997
212 -0.83610547 -0.47941272
213 -0.65504760 -0.83610547
214 -0.21032230 -0.65504760
215 -0.74853031 -0.21032230
216 -0.42547560 -0.74853031
217 -0.61481002 -0.42547560
218 -0.54162099 -0.61481002
219 -0.40331632 -0.54162099
220 -0.31236383 -0.40331632
221 -0.65022321 -0.31236383
222 -0.38858202 -0.65022321
223 -0.14575157 -0.38858202
224 -0.28898413 -0.14575157
225 -0.45130446 -0.28898413
226 -0.15858881 -0.45130446
227 -0.47249118 -0.15858881
228 -0.90473290 -0.47249118
229 -0.63764400 -0.90473290
230 -0.30672960 -0.63764400
231 0.01861274 -0.30672960
232 -0.51407827 0.01861274
233 -0.53556087 -0.51407827
234 -0.19484019 -0.53556087
235 -0.22229620 -0.19484019
236 -0.24960360 -0.22229620
237 -0.01839979 -0.24960360
238 -0.28762565 -0.01839979
239 -0.20532746 -0.28762565
240 -0.41283581 -0.20532746
241 -0.44804080 -0.41283581
242 -0.17234435 -0.44804080
243 -0.29631156 -0.17234435
244 -0.30674935 -0.29631156
245 -0.22148171 -0.30674935
246 -0.39666368 -0.22148171
247 -0.59970314 -0.39666368
248 -0.38869754 -0.59970314
249 -0.45840998 -0.38869754
250 -0.45991011 -0.45840998
251 -0.33093017 -0.45991011
252 -0.66151176 -0.33093017
253 0.09997267 -0.66151176
254 -0.59325060 0.09997267
255 -0.48085782 -0.59325060
256 -0.11453207 -0.48085782
257 -0.41887151 -0.11453207
258 -0.42258633 -0.41887151
259 -0.39891613 -0.42258633
260 -0.32109120 -0.39891613
261 -0.32003234 -0.32109120
262 -0.64477577 -0.32003234
263 -0.07667592 -0.64477577
264 -0.31479312 -0.07667592
265 -0.43219238 -0.31479312
266 -0.62448290 -0.43219238
267 -0.25791790 -0.62448290
268 -0.70489564 -0.25791790
269 -0.61890447 -0.70489564
270 -0.13328948 -0.61890447
271 -0.25682195 -0.13328948
272 -0.55800787 -0.25682195
273 0.02797000 -0.55800787
274 -0.51641905 0.02797000
275 0.10238043 -0.51641905
276 -0.47711162 0.10238043
277 -0.57921273 -0.47711162
278 -0.68353111 -0.57921273
279 -0.28411121 -0.68353111
280 -0.71477419 -0.28411121
281 -0.63623045 -0.71477419
282 -0.29012248 -0.63623045
283 0.06771233 -0.29012248
284 -0.70563623 0.06771233
285 -0.41691228 -0.70563623
286 -0.62379104 -0.41691228
287 -0.61554515 -0.62379104
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7tnpq1386608298.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8b0cr1386608298.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9iodr1386608298.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10lfk71386608298.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11gojx1386608298.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12c2l41386608298.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13e0ho1386608298.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14ocla1386608298.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15ae021386608298.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16fg2q1386608298.tab")
+ }
>
> try(system("convert tmp/1pacn1386608298.ps tmp/1pacn1386608298.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gx851386608298.ps tmp/2gx851386608298.png",intern=TRUE))
character(0)
> try(system("convert tmp/3owsb1386608298.ps tmp/3owsb1386608298.png",intern=TRUE))
character(0)
> try(system("convert tmp/4c1m61386608298.ps tmp/4c1m61386608298.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yxuu1386608298.ps tmp/5yxuu1386608298.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wuka1386608298.ps tmp/6wuka1386608298.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tnpq1386608298.ps tmp/7tnpq1386608298.png",intern=TRUE))
character(0)
> try(system("convert tmp/8b0cr1386608298.ps tmp/8b0cr1386608298.png",intern=TRUE))
character(0)
> try(system("convert tmp/9iodr1386608298.ps tmp/9iodr1386608298.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lfk71386608298.ps tmp/10lfk71386608298.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.732 2.734 19.554