R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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+ ,44
+ ,33
+ ,32
+ ,16
+ ,9
+ ,13
+ ,13
+ ,72
+ ,45
+ ,37
+ ,33
+ ,12
+ ,10
+ ,12
+ ,17
+ ,68
+ ,44
+ ,34
+ ,33
+ ,14
+ ,11
+ ,12
+ ,15
+ ,67
+ ,43
+ ,35
+ ,37
+ ,16
+ ,12
+ ,9
+ ,21
+ ,75
+ ,43
+ ,31
+ ,32
+ ,14
+ ,8
+ ,9
+ ,18
+ ,62
+ ,40
+ ,37
+ ,34
+ ,13
+ ,11
+ ,15
+ ,15
+ ,67
+ ,41
+ ,35
+ ,30
+ ,4
+ ,3
+ ,10
+ ,8
+ ,83
+ ,52
+ ,27
+ ,30
+ ,15
+ ,11
+ ,14
+ ,12
+ ,64
+ ,38
+ ,34
+ ,38
+ ,11
+ ,12
+ ,15
+ ,12
+ ,68
+ ,41
+ ,40
+ ,36
+ ,11
+ ,7
+ ,7
+ ,22
+ ,62
+ ,39
+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:264))
> 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. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Connected Separate Learning Software Happiness Depression Belonging
1 41 38 13 12 14 12.0 53
2 39 32 16 11 18 11.0 83
3 30 35 19 15 11 14.0 66
4 31 33 15 6 12 12.0 67
5 34 37 14 13 16 21.0 76
6 35 29 13 10 18 12.0 78
7 39 31 19 12 14 22.0 53
8 34 36 15 14 14 11.0 80
9 36 35 14 12 15 10.0 74
10 37 38 15 9 15 13.0 76
11 38 31 16 10 17 10.0 79
12 36 34 16 12 19 8.0 54
13 38 35 16 12 10 15.0 67
14 39 38 16 11 16 14.0 54
15 33 37 17 15 18 10.0 87
16 32 33 15 12 14 14.0 58
17 36 32 15 10 14 14.0 75
18 38 38 20 12 17 11.0 88
19 39 38 18 11 14 10.0 64
20 32 32 16 12 16 13.0 57
21 32 33 16 11 18 9.5 66
22 31 31 16 12 11 14.0 68
23 39 38 19 13 14 12.0 54
24 37 39 16 11 12 14.0 56
25 39 32 17 12 17 11.0 86
26 41 32 17 13 9 9.0 80
27 36 35 16 10 16 11.0 76
28 33 37 15 14 14 15.0 69
29 33 33 16 12 15 14.0 78
30 34 33 14 10 11 13.0 67
31 31 31 15 12 16 9.0 80
32 27 32 12 8 13 15.0 54
33 37 31 14 10 17 10.0 71
34 34 37 16 12 15 11.0 84
35 34 30 14 12 14 13.0 74
36 32 33 10 7 16 8.0 71
37 29 31 10 9 9 20.0 63
38 36 33 14 12 15 12.0 71
39 29 31 16 10 17 10.0 76
40 35 33 16 10 13 10.0 69
41 37 32 16 10 15 9.0 74
42 34 33 14 12 16 14.0 75
43 38 32 20 15 16 8.0 54
44 35 33 14 10 12 14.0 52
45 38 28 14 10 15 11.0 69
46 37 35 11 12 11 13.0 68
47 38 39 14 13 15 9.0 65
48 33 34 15 11 15 11.0 75
49 36 38 16 11 17 15.0 74
50 38 32 14 12 13 11.0 75
51 32 38 16 14 16 10.0 72
52 32 30 14 10 14 14.0 67
53 32 33 12 12 11 18.0 63
54 34 38 16 13 12 14.0 62
55 32 32 9 5 12 11.0 63
56 37 35 14 6 15 14.5 76
57 39 34 16 12 16 13.0 74
58 29 34 16 12 15 9.0 67
59 37 36 15 11 12 10.0 73
60 35 34 16 10 12 15.0 70
61 30 28 12 7 8 20.0 53
62 38 34 16 12 13 12.0 77
63 34 35 16 14 11 12.0 80
64 31 35 14 11 14 14.0 52
65 34 31 16 12 15 13.0 54
66 35 37 17 13 10 11.0 80
67 36 35 18 14 11 17.0 66
68 30 27 18 11 12 12.0 73
69 39 40 12 12 15 13.0 63
70 35 37 16 12 15 14.0 69
71 38 36 10 8 14 13.0 67
72 31 38 14 11 16 15.0 54
73 34 39 18 14 15 13.0 81
74 38 41 18 14 15 10.0 69
75 34 27 16 12 13 11.0 84
76 39 30 17 9 12 19.0 80
77 37 37 16 13 17 13.0 70
78 34 31 16 11 13 17.0 69
79 28 31 13 12 15 13.0 77
80 37 27 16 12 13 9.0 54
81 33 36 16 12 15 11.0 79
82 35 37 16 12 15 9.0 71
83 37 33 15 12 16 12.0 73
84 32 34 15 11 15 12.0 72
85 33 31 16 10 14 13.0 77
86 38 39 14 9 15 13.0 75
87 33 34 16 12 14 12.0 69
88 29 32 16 12 13 15.0 54
89 33 33 15 12 7 22.0 70
90 31 36 12 9 17 13.0 73
91 36 32 17 15 13 15.0 54
92 35 41 16 12 15 13.0 77
93 32 28 15 12 14 15.0 82
94 29 30 13 12 13 12.5 80
95 39 36 16 10 16 11.0 80
96 37 35 16 13 12 16.0 69
97 35 31 16 9 14 11.0 78
98 37 34 16 12 17 11.0 81
99 32 36 14 10 15 10.0 76
100 38 36 16 14 17 10.0 76
101 37 35 16 11 12 16.0 73
102 36 37 20 15 16 12.0 85
103 32 28 15 11 11 11.0 66
104 33 39 16 11 15 16.0 79
105 40 32 13 12 9 19.0 68
106 38 35 17 12 16 11.0 76
107 41 39 16 12 15 16.0 71
108 36 35 16 11 10 15.0 54
109 43 42 12 7 10 24.0 46
110 30 34 16 12 15 14.0 85
111 31 33 16 14 11 15.0 74
112 32 41 17 11 13 11.0 88
113 32 33 13 11 14 15.0 38
114 37 34 12 10 18 12.0 76
115 37 32 18 13 16 10.0 86
116 33 40 14 13 14 14.0 54
117 34 40 14 8 14 13.0 67
118 33 35 13 11 14 9.0 69
119 38 36 16 12 14 15.0 90
120 33 37 13 11 12 15.0 54
121 31 27 16 13 14 14.0 76
122 38 39 13 12 15 11.0 89
123 37 38 16 14 15 8.0 76
124 36 31 15 13 15 11.0 73
125 31 33 16 15 13 11.0 79
126 39 32 15 10 17 8.0 90
127 44 39 17 11 17 10.0 74
128 33 36 15 9 19 11.0 81
129 35 33 12 11 15 13.0 72
130 32 33 16 10 13 11.0 71
131 28 32 10 11 9 20.0 66
132 40 37 16 8 15 10.0 77
133 27 30 12 11 15 15.0 65
134 37 38 14 12 15 12.0 74
135 32 29 15 12 16 14.0 85
136 28 22 13 9 11 23.0 54
137 34 35 15 11 14 14.0 63
138 30 35 11 10 11 16.0 54
139 35 34 12 8 15 11.0 64
140 31 35 11 9 13 12.0 69
141 32 34 16 8 15 10.0 54
142 30 37 15 9 16 14.0 84
143 30 35 17 15 14 12.0 86
144 31 23 16 11 15 12.0 77
145 40 31 10 8 16 11.0 89
146 32 27 18 13 16 12.0 76
147 36 36 13 12 11 13.0 60
148 32 31 16 12 12 11.0 75
149 35 32 13 9 9 19.0 73
150 38 39 10 7 16 12.0 85
151 42 37 15 13 13 17.0 79
152 34 38 16 9 16 9.0 71
153 35 39 16 6 12 12.0 72
154 38 34 14 8 9 19.0 69
155 33 31 10 8 13 18.0 78
156 36 32 17 15 13 15.0 54
157 32 37 13 6 14 14.0 69
158 33 36 15 9 19 11.0 81
159 34 32 16 11 13 9.0 84
160 32 38 12 8 12 18.0 84
161 34 36 13 8 13 16.0 69
162 27 26 13 10 10 24.0 66
163 31 26 12 8 14 14.0 81
164 38 33 17 14 16 20.0 82
165 34 39 15 10 10 18.0 72
166 24 30 10 8 11 23.0 54
167 30 33 14 11 14 12.0 78
168 26 25 11 12 12 14.0 74
169 34 38 13 12 9 16.0 82
170 27 37 16 12 9 18.0 73
171 37 31 12 5 11 20.0 55
172 36 37 16 12 16 12.0 72
173 41 35 12 10 9 12.0 78
174 29 25 9 7 13 17.0 59
175 36 28 12 12 16 13.0 72
176 32 35 15 11 13 9.0 78
177 37 33 12 8 9 16.0 68
178 30 30 12 9 12 18.0 69
179 31 31 14 10 16 10.0 67
180 38 37 12 9 11 14.0 74
181 36 36 16 12 14 11.0 54
182 35 30 11 6 13 9.0 67
183 31 36 19 15 15 11.0 70
184 38 32 15 12 14 10.0 80
185 22 28 8 12 16 11.0 89
186 32 36 16 12 13 19.0 76
187 36 34 17 11 14 14.0 74
188 39 31 12 7 15 12.0 87
189 28 28 11 7 13 14.0 54
190 32 36 11 5 11 21.0 61
191 32 36 14 12 11 13.0 38
192 38 40 16 12 14 10.0 75
193 32 33 12 3 15 15.0 69
194 35 37 16 11 11 16.0 62
195 32 32 13 10 15 14.0 72
196 37 38 15 12 12 12.0 70
197 34 31 16 9 14 19.0 79
198 33 37 16 12 14 15.0 87
199 33 33 14 9 8 19.0 62
200 26 32 16 12 13 13.0 77
201 30 30 16 12 9 17.0 69
202 24 30 14 10 15 12.0 69
203 34 31 11 9 17 11.0 75
204 34 32 12 12 13 14.0 54
205 33 34 15 8 15 11.0 72
206 34 36 15 11 15 13.0 74
207 35 37 16 11 14 12.0 85
208 35 36 16 12 16 15.0 52
209 36 33 11 10 13 14.0 70
210 34 33 15 10 16 12.0 84
211 34 33 12 12 9 17.0 64
212 41 44 12 12 16 11.0 84
213 32 39 15 11 11 18.0 87
214 30 32 15 8 10 13.0 79
215 35 35 16 12 11 17.0 67
216 28 25 14 10 15 13.0 65
217 33 35 17 11 17 11.0 85
218 39 34 14 10 14 12.0 83
219 36 35 13 8 8 22.0 61
220 36 39 15 12 15 14.0 82
221 35 33 13 12 11 12.0 76
222 38 36 14 10 16 12.0 58
223 33 32 15 12 10 17.0 72
224 31 32 12 9 15 9.0 72
225 34 36 13 9 9 21.0 38
226 32 36 8 6 16 10.0 78
227 31 32 14 10 19 11.0 54
228 33 34 14 9 12 12.0 63
229 34 33 11 9 8 23.0 66
230 34 35 12 9 11 13.0 70
231 34 30 13 6 14 12.0 71
232 33 38 10 10 9 16.0 67
233 32 34 16 6 15 9.0 58
234 41 33 18 14 13 17.0 72
235 34 32 13 10 16 9.0 72
236 36 31 11 10 11 14.0 70
237 37 30 4 6 12 17.0 76
238 36 27 13 12 13 13.0 50
239 29 31 16 12 10 11.0 72
240 37 30 10 7 11 12.0 72
241 27 32 12 8 12 10.0 88
242 35 35 12 11 8 19.0 53
243 28 28 10 3 12 16.0 58
244 35 33 13 6 12 16.0 66
245 37 31 15 10 15 14.0 82
246 29 35 12 8 11 20.0 69
247 32 35 14 9 13 15.0 68
248 36 32 10 9 14 23.0 44
249 19 21 12 8 10 20.0 56
250 21 20 12 9 12 16.0 53
251 31 34 11 7 15 14.0 70
252 33 32 10 7 13 17.0 78
253 36 34 12 6 13 11.0 71
254 33 32 16 9 13 13.0 72
255 37 33 12 10 12 17.0 68
256 34 33 14 11 12 15.0 67
257 35 37 16 12 9 21.0 75
258 31 32 14 8 9 18.0 62
259 37 34 13 11 15 15.0 67
260 35 30 4 3 10 8.0 83
261 27 30 15 11 14 12.0 64
262 34 38 11 12 15 12.0 68
263 40 36 11 7 7 22.0 62
264 29 32 14 9 14 12.0 72
Belonging_Final
1 32
2 51
3 42
4 41
5 46
6 47
7 37
8 49
9 45
10 47
11 49
12 33
13 42
14 33
15 53
16 36
17 45
18 54
19 41
20 36
21 41
22 44
23 33
24 37
25 52
26 47
27 43
28 44
29 45
30 44
31 49
32 33
33 43
34 54
35 42
36 44
37 37
38 43
39 46
40 42
41 45
42 44
43 33
44 31
45 42
46 40
47 43
48 46
49 42
50 45
51 44
52 40
53 37
54 46
55 36
56 47
57 45
58 42
59 43
60 43
61 32
62 45
63 48
64 31
65 33
66 49
67 42
68 41
69 38
70 42
71 44
72 33
73 48
74 40
75 50
76 49
77 43
78 44
79 47
80 33
81 46
82 45
83 43
84 44
85 47
86 45
87 42
88 33
89 43
90 46
91 33
92 46
93 48
94 47
95 47
96 43
97 46
98 48
99 46
100 45
101 45
102 52
103 42
104 47
105 41
106 47
107 43
108 33
109 30
110 52
111 44
112 55
113 11
114 47
115 53
116 33
117 44
118 42
119 55
120 33
121 46
122 54
123 47
124 45
125 47
126 55
127 44
128 53
129 44
130 42
131 40
132 46
133 40
134 46
135 53
136 33
137 42
138 35
139 40
140 41
141 33
142 51
143 53
144 46
145 55
146 47
147 38
148 46
149 46
150 53
151 47
152 41
153 44
154 43
155 51
156 33
157 43
158 53
159 51
160 50
161 46
162 43
163 47
164 50
165 43
166 33
167 48
168 44
169 50
170 41
171 34
172 44
173 47
174 35
175 44
176 44
177 43
178 41
179 41
180 42
181 33
182 41
183 44
184 48
185 55
186 44
187 43
188 52
189 30
190 39
191 11
192 44
193 42
194 41
195 44
196 44
197 48
198 53
199 37
200 44
201 44
202 40
203 42
204 35
205 43
206 45
207 55
208 31
209 44
210 50
211 40
212 53
213 54
214 49
215 40
216 41
217 52
218 52
219 36
220 52
221 46
222 31
223 44
224 44
225 11
226 46
227 33
228 34
229 42
230 43
231 43
232 44
233 36
234 46
235 44
236 43
237 50
238 33
239 43
240 44
241 53
242 34
243 35
244 40
245 53
246 42
247 43
248 29
249 36
250 30
251 42
252 47
253 44
254 45
255 44
256 43
257 43
258 40
259 41
260 52
261 38
262 41
263 39
264 43
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Separate Learning Software
17.08173 0.43245 0.15046 -0.03607
Happiness Depression Belonging Belonging_Final
0.03566 -0.06009 -0.07275 0.14175
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.1922 -2.4611 0.0473 2.4871 7.5128
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.08173 3.29021 5.192 4.25e-07 ***
Separate 0.43245 0.05801 7.455 1.39e-12 ***
Learning 0.15046 0.11150 1.349 0.178
Software -0.03607 0.11502 -0.314 0.754
Happiness 0.03566 0.10433 0.342 0.733
Depression -0.06009 0.07622 -0.788 0.431
Belonging -0.07275 0.06762 -1.076 0.283
Belonging_Final 0.14175 0.10069 1.408 0.160
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.374 on 256 degrees of freedom
Multiple R-squared: 0.2313, Adjusted R-squared: 0.2103
F-statistic: 11.01 on 7 and 256 DF, p-value: 3.752e-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,] 0.04628218 0.09256437 0.9537178
[2,] 0.38804836 0.77609672 0.6119516
[3,] 0.56163765 0.87672470 0.4383624
[4,] 0.55819529 0.88360942 0.4418047
[5,] 0.45818564 0.91637127 0.5418144
[6,] 0.49355192 0.98710383 0.5064481
[7,] 0.56189273 0.87621453 0.4381073
[8,] 0.56298753 0.87402495 0.4370125
[9,] 0.47493331 0.94986661 0.5250667
[10,] 0.50644983 0.98710034 0.4935502
[11,] 0.56906135 0.86187730 0.4309387
[12,] 0.53846056 0.92307887 0.4615394
[13,] 0.52675622 0.94648755 0.4732438
[14,] 0.46033086 0.92066171 0.5396691
[15,] 0.51453271 0.97093458 0.4854673
[16,] 0.68923679 0.62152642 0.3107632
[17,] 0.66167533 0.67664934 0.3383247
[18,] 0.61549529 0.76900943 0.3845047
[19,] 0.60601461 0.78797079 0.3939854
[20,] 0.54332414 0.91335173 0.4566759
[21,] 0.53389815 0.93220370 0.4661019
[22,] 0.67986700 0.64026599 0.3201330
[23,] 0.66915085 0.66169829 0.3308491
[24,] 0.62801174 0.74397651 0.3719883
[25,] 0.57418317 0.85163366 0.4258168
[26,] 0.51957804 0.96084391 0.4804220
[27,] 0.47620881 0.95241761 0.5237912
[28,] 0.44194100 0.88388199 0.5580590
[29,] 0.55786809 0.88426381 0.4421319
[30,] 0.50469876 0.99060248 0.4953012
[31,] 0.47135391 0.94270782 0.5286461
[32,] 0.41897451 0.83794902 0.5810255
[33,] 0.38066779 0.76133558 0.6193322
[34,] 0.33653498 0.67306997 0.6634650
[35,] 0.40847487 0.81694975 0.5915251
[36,] 0.41823130 0.83646260 0.5817687
[37,] 0.38950811 0.77901622 0.6104919
[38,] 0.35966753 0.71933506 0.6403325
[39,] 0.31585725 0.63171449 0.6841428
[40,] 0.32823110 0.65646219 0.6717689
[41,] 0.37243721 0.74487442 0.6275628
[42,] 0.33750653 0.67501306 0.6624935
[43,] 0.29838116 0.59676233 0.7016188
[44,] 0.26493910 0.52987819 0.7350609
[45,] 0.22925094 0.45850188 0.7707491
[46,] 0.21657395 0.43314790 0.7834261
[47,] 0.23066725 0.46133450 0.7693327
[48,] 0.33708313 0.67416626 0.6629169
[49,] 0.30250672 0.60501345 0.6974933
[50,] 0.26500342 0.53000684 0.7349966
[51,] 0.23614504 0.47229007 0.7638550
[52,] 0.22347081 0.44694161 0.7765292
[53,] 0.19850936 0.39701871 0.8014906
[54,] 0.20266185 0.40532371 0.7973381
[55,] 0.17445479 0.34890959 0.8255452
[56,] 0.15270086 0.30540171 0.8472991
[57,] 0.12982781 0.25965561 0.8701722
[58,] 0.14707643 0.29415287 0.8529236
[59,] 0.14594282 0.29188564 0.8540572
[60,] 0.12499539 0.24999077 0.8750046
[61,] 0.13403765 0.26807529 0.8659624
[62,] 0.15639681 0.31279362 0.8436032
[63,] 0.15242115 0.30484230 0.8475789
[64,] 0.12944757 0.25889515 0.8705524
[65,] 0.11356709 0.22713417 0.8864329
[66,] 0.15244060 0.30488119 0.8475594
[67,] 0.13216061 0.26432122 0.8678394
[68,] 0.11221193 0.22442386 0.8877881
[69,] 0.14359899 0.28719798 0.8564010
[70,] 0.16767821 0.33535643 0.8323218
[71,] 0.15922477 0.31844955 0.8407752
[72,] 0.13893071 0.27786142 0.8610693
[73,] 0.13260197 0.26520395 0.8673980
[74,] 0.12636947 0.25273893 0.8736305
[75,] 0.11037363 0.22074727 0.8896264
[76,] 0.09818491 0.19636982 0.9018151
[77,] 0.08712398 0.17424797 0.9128760
[78,] 0.10394880 0.20789760 0.8960512
[79,] 0.08735756 0.17471513 0.9126424
[80,] 0.09293603 0.18587206 0.9070640
[81,] 0.08610820 0.17221639 0.9138918
[82,] 0.07728667 0.15457334 0.9227133
[83,] 0.06553311 0.13106622 0.9344669
[84,] 0.06720472 0.13440944 0.9327953
[85,] 0.06685490 0.13370980 0.9331451
[86,] 0.06149587 0.12299174 0.9385041
[87,] 0.05298890 0.10597780 0.9470111
[88,] 0.04737209 0.09474417 0.9526279
[89,] 0.04739097 0.09478194 0.9526090
[90,] 0.04387234 0.08774468 0.9561277
[91,] 0.03926932 0.07853865 0.9607307
[92,] 0.03271517 0.06543034 0.9672848
[93,] 0.02697966 0.05395932 0.9730203
[94,] 0.02712045 0.05424091 0.9728795
[95,] 0.06294853 0.12589706 0.9370515
[96,] 0.05886399 0.11772798 0.9411360
[97,] 0.07184311 0.14368622 0.9281569
[98,] 0.06221890 0.12443779 0.9377811
[99,] 0.09942827 0.19885655 0.9005717
[100,] 0.11617165 0.23234330 0.8838284
[101,] 0.11168476 0.22336951 0.8883152
[102,] 0.14753612 0.29507223 0.8524639
[103,] 0.13788069 0.27576137 0.8621193
[104,] 0.13293635 0.26587269 0.8670637
[105,] 0.12664703 0.25329406 0.8733530
[106,] 0.12441046 0.24882093 0.8755895
[107,] 0.12168825 0.24337650 0.8783117
[108,] 0.10721859 0.21443719 0.8927814
[109,] 0.10143351 0.20286702 0.8985665
[110,] 0.09091013 0.18182025 0.9090899
[111,] 0.08011004 0.16022007 0.9198900
[112,] 0.07294698 0.14589396 0.9270530
[113,] 0.06218404 0.12436808 0.9378160
[114,] 0.05940876 0.11881752 0.9405912
[115,] 0.05613094 0.11226187 0.9438691
[116,] 0.06932557 0.13865113 0.9306744
[117,] 0.12525905 0.25051810 0.8747410
[118,] 0.12309956 0.24619913 0.8769004
[119,] 0.10952539 0.21905078 0.8904746
[120,] 0.10135848 0.20271696 0.8986415
[121,] 0.10962687 0.21925374 0.8903731
[122,] 0.11771214 0.23542429 0.8822879
[123,] 0.14190412 0.28380824 0.8580959
[124,] 0.12528190 0.25056379 0.8747181
[125,] 0.10861693 0.21723385 0.8913831
[126,] 0.09520134 0.19040268 0.9047987
[127,] 0.08183847 0.16367694 0.9181615
[128,] 0.08892454 0.17784909 0.9110755
[129,] 0.07602042 0.15204084 0.9239796
[130,] 0.07427006 0.14854011 0.9257299
[131,] 0.07240154 0.14480308 0.9275985
[132,] 0.10054183 0.20108366 0.8994582
[133,] 0.11987797 0.23975594 0.8801220
[134,] 0.11370995 0.22741991 0.8862900
[135,] 0.19602192 0.39204383 0.8039781
[136,] 0.18065063 0.36130125 0.8193494
[137,] 0.16195645 0.32391290 0.8380436
[138,] 0.14365419 0.28730838 0.8563458
[139,] 0.13160794 0.26321588 0.8683921
[140,] 0.11706912 0.23413825 0.8829309
[141,] 0.18738241 0.37476481 0.8126176
[142,] 0.17528304 0.35056609 0.8247170
[143,] 0.16072204 0.32144409 0.8392780
[144,] 0.17180878 0.34361756 0.8281912
[145,] 0.14972424 0.29944847 0.8502758
[146,] 0.14642031 0.29284063 0.8535797
[147,] 0.15271575 0.30543150 0.8472843
[148,] 0.14868917 0.29737834 0.8513108
[149,] 0.13159387 0.26318774 0.8684061
[150,] 0.13402022 0.26804043 0.8659798
[151,] 0.12057202 0.24114404 0.8794280
[152,] 0.11582266 0.23164533 0.8841773
[153,] 0.10675632 0.21351265 0.8932437
[154,] 0.13564580 0.27129159 0.8643542
[155,] 0.12378047 0.24756095 0.8762195
[156,] 0.21117093 0.42234185 0.7888291
[157,] 0.21330836 0.42661671 0.7866916
[158,] 0.20383253 0.40766506 0.7961675
[159,] 0.18485696 0.36971392 0.8151430
[160,] 0.30200104 0.60400208 0.6979990
[161,] 0.33474475 0.66948950 0.6652553
[162,] 0.30153847 0.60307694 0.6984615
[163,] 0.40510001 0.81020002 0.5949000
[164,] 0.36941684 0.73883369 0.6305832
[165,] 0.43507105 0.87014210 0.5649290
[166,] 0.41163640 0.82327281 0.5883636
[167,] 0.41211141 0.82422282 0.5878886
[168,] 0.38053963 0.76107925 0.6194604
[169,] 0.35338600 0.70677200 0.6466140
[170,] 0.34804411 0.69608821 0.6519559
[171,] 0.31426099 0.62852198 0.6857390
[172,] 0.30750804 0.61501608 0.6924920
[173,] 0.33141787 0.66283575 0.6685821
[174,] 0.39870733 0.79741465 0.6012927
[175,] 0.64288552 0.71422896 0.3571145
[176,] 0.62649117 0.74701766 0.3735088
[177,] 0.62178065 0.75643869 0.3782193
[178,] 0.76062095 0.47875810 0.2393790
[179,] 0.74164261 0.51671478 0.2583574
[180,] 0.74744272 0.50511455 0.2525573
[181,] 0.71542355 0.56915289 0.2845764
[182,] 0.68630160 0.62739679 0.3136984
[183,] 0.65552401 0.68895198 0.3444760
[184,] 0.62308690 0.75382619 0.3769131
[185,] 0.58543983 0.82912034 0.4145602
[186,] 0.54578861 0.90842277 0.4542114
[187,] 0.53847192 0.92305616 0.4615281
[188,] 0.51233323 0.97533353 0.4876668
[189,] 0.47069268 0.94138536 0.5293073
[190,] 0.55045367 0.89909266 0.4495463
[191,] 0.51911716 0.96176567 0.4808828
[192,] 0.67455226 0.65089549 0.3254477
[193,] 0.65398436 0.69203128 0.3460156
[194,] 0.61613758 0.76772484 0.3838624
[195,] 0.57669894 0.84660211 0.4233011
[196,] 0.53674889 0.92650222 0.4632511
[197,] 0.49756003 0.99512006 0.5024400
[198,] 0.45341358 0.90682716 0.5465864
[199,] 0.42340525 0.84681050 0.5765948
[200,] 0.38972415 0.77944830 0.6102759
[201,] 0.34933278 0.69866557 0.6506672
[202,] 0.31826703 0.63653406 0.6817330
[203,] 0.37231091 0.74462181 0.6276891
[204,] 0.35171788 0.70343576 0.6482821
[205,] 0.30920479 0.61840957 0.6907952
[206,] 0.27377025 0.54754051 0.7262297
[207,] 0.24401016 0.48802032 0.7559898
[208,] 0.27484575 0.54969150 0.7251542
[209,] 0.24711247 0.49422494 0.7528875
[210,] 0.22492951 0.44985903 0.7750705
[211,] 0.19435391 0.38870783 0.8056461
[212,] 0.20740749 0.41481499 0.7925925
[213,] 0.17249907 0.34499813 0.8275009
[214,] 0.14881073 0.29762145 0.8511893
[215,] 0.21619344 0.43238688 0.7838066
[216,] 0.19903938 0.39807876 0.8009606
[217,] 0.17672520 0.35345041 0.8232748
[218,] 0.18657074 0.37314149 0.8134293
[219,] 0.15300166 0.30600331 0.8469983
[220,] 0.12227090 0.24454181 0.8777291
[221,] 0.12177772 0.24355544 0.8782223
[222,] 0.26810147 0.53620295 0.7318985
[223,] 0.22692228 0.45384456 0.7730777
[224,] 0.34749544 0.69499087 0.6525046
[225,] 0.29761666 0.59523331 0.7023833
[226,] 0.30225312 0.60450624 0.6977469
[227,] 0.26390523 0.52781046 0.7360948
[228,] 0.35450516 0.70901032 0.6454948
[229,] 0.30044835 0.60089670 0.6995517
[230,] 0.47433309 0.94866617 0.5256669
[231,] 0.54815029 0.90369941 0.4518497
[232,] 0.47059322 0.94118643 0.5294068
[233,] 0.39572647 0.79145294 0.6042735
[234,] 0.38408815 0.76817631 0.6159118
[235,] 0.41076723 0.82153446 0.5892328
[236,] 0.63361583 0.73276834 0.3663842
[237,] 0.63249795 0.73500411 0.3675021
[238,] 0.57038930 0.85922139 0.4296107
[239,] 0.77923886 0.44152228 0.2207611
[240,] 0.72806354 0.54387293 0.2719365
[241,] 0.70726931 0.58546138 0.2927307
[242,] 0.81460810 0.37078380 0.1853919
[243,] 0.72338067 0.55323866 0.2766193
> postscript(file="/var/fisher/rcomp/tmp/1ekgn1351941589.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2t5nu1351941589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qgw51351941589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4tkdq1351941589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5u2ds1351941589.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 = 264
Frequency = 1
1 2 3 4 5 6
5.50366927 4.89741706 -5.23810507 -3.03734960 -1.02002077 2.87341102
7 8 9 10 11 12
5.52020998 -1.36579916 1.17971113 0.66597030 4.26186607 1.29439677
13 14 15 16 17 18
3.27356843 2.99607580 -3.32359202 -1.71807531 2.60323768 0.71112662
19 20 21 22 23 24
2.11961841 -1.64025742 -2.44442246 -2.30314647 2.56798409 0.28478045
25 26 27 28 29 30
4.89519427 7.36864586 1.26008489 -2.64938575 -0.72493983 -0.07210794
31 32 33 34 35 36
-2.46721036 -5.74852498 3.83128461 -2.47426461 1.98314586 -1.76625665
37 38 39 40 41 42
-2.44821920 2.23004044 -4.53113271 0.80437586 3.04390513 0.46381247
43 44 45 46 47 48
3.77266004 1.70382251 6.25629851 3.22627118 1.05463983 -1.58327898
49 50 51 52 53 54
0.19976207 4.68122690 -4.38582615 -0.25465808 -0.69733460 -3.04988777
55 56 57 58 59 60
-0.38055504 2.09570054 4.45585159 -5.83285397 1.87847794 0.63904946
61 62 63 64 65 66
-0.50702304 3.72100600 -0.77497439 -3.19633198 1.03485622 -0.99257929
67 68 69 70 71 72
1.05655860 -1.27717937 2.69066154 -0.68423830 3.05325090 -4.64291423
73 74 75 76 77 78
-2.81549965 0.40032782 2.48855212 6.29968133 1.15141163 0.84248076
79 80 81 82 83 84
-4.82501170 5.59561105 -2.27155970 -1.26444869 3.18941935 -2.45793769
85 86 87 88 89 90
-0.31286571 1.59473323 -1.47141267 -4.20608039 -0.10694026 -4.16558598
91 92 93 94 95 96
2.75167142 -2.45911984 0.54926614 -3.13302747 3.55163503 2.30215271
97 98 99 100 101 102
1.74538343 2.38400752 -3.32112702 2.59265910 2.23751351 -0.58720870
103 104 105 106 107 108
0.06631302 -3.44626819 7.51282393 2.61476017 4.57479922 1.56749722
109 110 111 112 113 114
6.38198511 -4.64039091 -2.59930379 -6.17000557 0.69563026 2.71612685
115 116 117 118 119 120
2.61462646 -3.42443321 -3.27837869 -1.66882848 2.52896952 -1.91735005
121 122 123 124 125 126
-0.34576985 1.47597222 0.39540475 2.78245560 -2.93642722 4.80944707
127 128 129 130 131 132
7.03289355 -3.21872685 1.48597968 -1.99003025 -4.01552798 3.94611688
133 134 135 136 137 138
-5.03874426 0.86079815 -0.50510640 0.01362954 -1.10579566 -3.97535288
139 140 141 142 143 144
0.81013433 -3.08236022 -2.58704456 -5.86215115 -5.26860674 1.22878608
145 146 147 148 149 150
7.06523841 0.02004863 1.19439858 -1.29333060 2.05961089 1.62217251
151 152 153 154 155 156
6.77264808 -2.27376590 -1.84399702 4.14243504 0.35961589 2.75167142
157 158 159 160 161 162
-3.55537009 -3.21872685 0.02830596 -3.35451739 -1.32018846 -3.12883899
163 164 165 166 167 168
0.73016759 4.10387390 -1.97562694 -7.03064895 -3.96986546 -3.55531863
169 170 171 172 173 174
-1.51938815 -7.79713059 4.87849825 0.09466462 6.75015868 -0.10564445
175 176 177 178 179 180
4.64862688 -2.56282549 3.62277566 -1.67436450 -2.14055266 3.31579992
181 182 183 184 185 186
0.78809614 2.64589276 -4.98598342 4.37351297 -9.19221726 -2.65424878
187 188 189 190 191 192
1.68424518 6.10375788 -2.53918023 -2.34544294 -0.72929386 0.96671857
193 194 195 196 197 198
-1.61715009 -0.82497507 -1.20801002 0.80983009 1.01536432 -2.83822902
199 200 201 202 203 204
-0.01213091 -7.21225442 -2.54634391 -8.26500321 1.73943954 1.05216164
205 206 207 208 209 210
-1.48448874 -1.25899179 -1.48357954 0.09513499 2.58628624 -0.07471962
211 212 213 214 215 216
0.96139868 2.20652198 -4.44322074 -3.66233944 0.64159055 -2.47542664
217 218 219 220 221 222
-2.51097184 4.35836550 2.48663674 -0.87042621 1.46165746 3.58014471
223 224 225 226 227 228
-0.07819193 -2.39407920 1.86501557 -2.45281516 -2.43165428 -0.50987136
229 230 231 232 233 234
1.26185988 -0.31215136 1.49708648 -2.38090907 -2.85352692 6.71962728
235 236 237 238 239 240
0.45586776 3.66426261 5.59450040 4.99635046 -4.01500098 5.02252653
241 242 243 244 245 246
-6.37481104 1.26652608 -2.79490815 1.57293559 3.37526729 -4.85858005
247 248 249 250 251 252
-2.70971719 3.87302716 -8.86389638 -6.07481677 -2.74219826 0.39801194
253 254 255 256 257 258
1.75157384 -0.82597267 3.50626379 0.19023295 0.24514340 -2.13676348
259 260 261 262 263 264
3.08475444 3.24254823 -5.42398047 -1.41557340 6.00219725 -4.33730700
> postscript(file="/var/fisher/rcomp/tmp/6ygte1351941589.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 = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 5.50366927 NA
1 4.89741706 5.50366927
2 -5.23810507 4.89741706
3 -3.03734960 -5.23810507
4 -1.02002077 -3.03734960
5 2.87341102 -1.02002077
6 5.52020998 2.87341102
7 -1.36579916 5.52020998
8 1.17971113 -1.36579916
9 0.66597030 1.17971113
10 4.26186607 0.66597030
11 1.29439677 4.26186607
12 3.27356843 1.29439677
13 2.99607580 3.27356843
14 -3.32359202 2.99607580
15 -1.71807531 -3.32359202
16 2.60323768 -1.71807531
17 0.71112662 2.60323768
18 2.11961841 0.71112662
19 -1.64025742 2.11961841
20 -2.44442246 -1.64025742
21 -2.30314647 -2.44442246
22 2.56798409 -2.30314647
23 0.28478045 2.56798409
24 4.89519427 0.28478045
25 7.36864586 4.89519427
26 1.26008489 7.36864586
27 -2.64938575 1.26008489
28 -0.72493983 -2.64938575
29 -0.07210794 -0.72493983
30 -2.46721036 -0.07210794
31 -5.74852498 -2.46721036
32 3.83128461 -5.74852498
33 -2.47426461 3.83128461
34 1.98314586 -2.47426461
35 -1.76625665 1.98314586
36 -2.44821920 -1.76625665
37 2.23004044 -2.44821920
38 -4.53113271 2.23004044
39 0.80437586 -4.53113271
40 3.04390513 0.80437586
41 0.46381247 3.04390513
42 3.77266004 0.46381247
43 1.70382251 3.77266004
44 6.25629851 1.70382251
45 3.22627118 6.25629851
46 1.05463983 3.22627118
47 -1.58327898 1.05463983
48 0.19976207 -1.58327898
49 4.68122690 0.19976207
50 -4.38582615 4.68122690
51 -0.25465808 -4.38582615
52 -0.69733460 -0.25465808
53 -3.04988777 -0.69733460
54 -0.38055504 -3.04988777
55 2.09570054 -0.38055504
56 4.45585159 2.09570054
57 -5.83285397 4.45585159
58 1.87847794 -5.83285397
59 0.63904946 1.87847794
60 -0.50702304 0.63904946
61 3.72100600 -0.50702304
62 -0.77497439 3.72100600
63 -3.19633198 -0.77497439
64 1.03485622 -3.19633198
65 -0.99257929 1.03485622
66 1.05655860 -0.99257929
67 -1.27717937 1.05655860
68 2.69066154 -1.27717937
69 -0.68423830 2.69066154
70 3.05325090 -0.68423830
71 -4.64291423 3.05325090
72 -2.81549965 -4.64291423
73 0.40032782 -2.81549965
74 2.48855212 0.40032782
75 6.29968133 2.48855212
76 1.15141163 6.29968133
77 0.84248076 1.15141163
78 -4.82501170 0.84248076
79 5.59561105 -4.82501170
80 -2.27155970 5.59561105
81 -1.26444869 -2.27155970
82 3.18941935 -1.26444869
83 -2.45793769 3.18941935
84 -0.31286571 -2.45793769
85 1.59473323 -0.31286571
86 -1.47141267 1.59473323
87 -4.20608039 -1.47141267
88 -0.10694026 -4.20608039
89 -4.16558598 -0.10694026
90 2.75167142 -4.16558598
91 -2.45911984 2.75167142
92 0.54926614 -2.45911984
93 -3.13302747 0.54926614
94 3.55163503 -3.13302747
95 2.30215271 3.55163503
96 1.74538343 2.30215271
97 2.38400752 1.74538343
98 -3.32112702 2.38400752
99 2.59265910 -3.32112702
100 2.23751351 2.59265910
101 -0.58720870 2.23751351
102 0.06631302 -0.58720870
103 -3.44626819 0.06631302
104 7.51282393 -3.44626819
105 2.61476017 7.51282393
106 4.57479922 2.61476017
107 1.56749722 4.57479922
108 6.38198511 1.56749722
109 -4.64039091 6.38198511
110 -2.59930379 -4.64039091
111 -6.17000557 -2.59930379
112 0.69563026 -6.17000557
113 2.71612685 0.69563026
114 2.61462646 2.71612685
115 -3.42443321 2.61462646
116 -3.27837869 -3.42443321
117 -1.66882848 -3.27837869
118 2.52896952 -1.66882848
119 -1.91735005 2.52896952
120 -0.34576985 -1.91735005
121 1.47597222 -0.34576985
122 0.39540475 1.47597222
123 2.78245560 0.39540475
124 -2.93642722 2.78245560
125 4.80944707 -2.93642722
126 7.03289355 4.80944707
127 -3.21872685 7.03289355
128 1.48597968 -3.21872685
129 -1.99003025 1.48597968
130 -4.01552798 -1.99003025
131 3.94611688 -4.01552798
132 -5.03874426 3.94611688
133 0.86079815 -5.03874426
134 -0.50510640 0.86079815
135 0.01362954 -0.50510640
136 -1.10579566 0.01362954
137 -3.97535288 -1.10579566
138 0.81013433 -3.97535288
139 -3.08236022 0.81013433
140 -2.58704456 -3.08236022
141 -5.86215115 -2.58704456
142 -5.26860674 -5.86215115
143 1.22878608 -5.26860674
144 7.06523841 1.22878608
145 0.02004863 7.06523841
146 1.19439858 0.02004863
147 -1.29333060 1.19439858
148 2.05961089 -1.29333060
149 1.62217251 2.05961089
150 6.77264808 1.62217251
151 -2.27376590 6.77264808
152 -1.84399702 -2.27376590
153 4.14243504 -1.84399702
154 0.35961589 4.14243504
155 2.75167142 0.35961589
156 -3.55537009 2.75167142
157 -3.21872685 -3.55537009
158 0.02830596 -3.21872685
159 -3.35451739 0.02830596
160 -1.32018846 -3.35451739
161 -3.12883899 -1.32018846
162 0.73016759 -3.12883899
163 4.10387390 0.73016759
164 -1.97562694 4.10387390
165 -7.03064895 -1.97562694
166 -3.96986546 -7.03064895
167 -3.55531863 -3.96986546
168 -1.51938815 -3.55531863
169 -7.79713059 -1.51938815
170 4.87849825 -7.79713059
171 0.09466462 4.87849825
172 6.75015868 0.09466462
173 -0.10564445 6.75015868
174 4.64862688 -0.10564445
175 -2.56282549 4.64862688
176 3.62277566 -2.56282549
177 -1.67436450 3.62277566
178 -2.14055266 -1.67436450
179 3.31579992 -2.14055266
180 0.78809614 3.31579992
181 2.64589276 0.78809614
182 -4.98598342 2.64589276
183 4.37351297 -4.98598342
184 -9.19221726 4.37351297
185 -2.65424878 -9.19221726
186 1.68424518 -2.65424878
187 6.10375788 1.68424518
188 -2.53918023 6.10375788
189 -2.34544294 -2.53918023
190 -0.72929386 -2.34544294
191 0.96671857 -0.72929386
192 -1.61715009 0.96671857
193 -0.82497507 -1.61715009
194 -1.20801002 -0.82497507
195 0.80983009 -1.20801002
196 1.01536432 0.80983009
197 -2.83822902 1.01536432
198 -0.01213091 -2.83822902
199 -7.21225442 -0.01213091
200 -2.54634391 -7.21225442
201 -8.26500321 -2.54634391
202 1.73943954 -8.26500321
203 1.05216164 1.73943954
204 -1.48448874 1.05216164
205 -1.25899179 -1.48448874
206 -1.48357954 -1.25899179
207 0.09513499 -1.48357954
208 2.58628624 0.09513499
209 -0.07471962 2.58628624
210 0.96139868 -0.07471962
211 2.20652198 0.96139868
212 -4.44322074 2.20652198
213 -3.66233944 -4.44322074
214 0.64159055 -3.66233944
215 -2.47542664 0.64159055
216 -2.51097184 -2.47542664
217 4.35836550 -2.51097184
218 2.48663674 4.35836550
219 -0.87042621 2.48663674
220 1.46165746 -0.87042621
221 3.58014471 1.46165746
222 -0.07819193 3.58014471
223 -2.39407920 -0.07819193
224 1.86501557 -2.39407920
225 -2.45281516 1.86501557
226 -2.43165428 -2.45281516
227 -0.50987136 -2.43165428
228 1.26185988 -0.50987136
229 -0.31215136 1.26185988
230 1.49708648 -0.31215136
231 -2.38090907 1.49708648
232 -2.85352692 -2.38090907
233 6.71962728 -2.85352692
234 0.45586776 6.71962728
235 3.66426261 0.45586776
236 5.59450040 3.66426261
237 4.99635046 5.59450040
238 -4.01500098 4.99635046
239 5.02252653 -4.01500098
240 -6.37481104 5.02252653
241 1.26652608 -6.37481104
242 -2.79490815 1.26652608
243 1.57293559 -2.79490815
244 3.37526729 1.57293559
245 -4.85858005 3.37526729
246 -2.70971719 -4.85858005
247 3.87302716 -2.70971719
248 -8.86389638 3.87302716
249 -6.07481677 -8.86389638
250 -2.74219826 -6.07481677
251 0.39801194 -2.74219826
252 1.75157384 0.39801194
253 -0.82597267 1.75157384
254 3.50626379 -0.82597267
255 0.19023295 3.50626379
256 0.24514340 0.19023295
257 -2.13676348 0.24514340
258 3.08475444 -2.13676348
259 3.24254823 3.08475444
260 -5.42398047 3.24254823
261 -1.41557340 -5.42398047
262 6.00219725 -1.41557340
263 -4.33730700 6.00219725
264 NA -4.33730700
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.89741706 5.50366927
[2,] -5.23810507 4.89741706
[3,] -3.03734960 -5.23810507
[4,] -1.02002077 -3.03734960
[5,] 2.87341102 -1.02002077
[6,] 5.52020998 2.87341102
[7,] -1.36579916 5.52020998
[8,] 1.17971113 -1.36579916
[9,] 0.66597030 1.17971113
[10,] 4.26186607 0.66597030
[11,] 1.29439677 4.26186607
[12,] 3.27356843 1.29439677
[13,] 2.99607580 3.27356843
[14,] -3.32359202 2.99607580
[15,] -1.71807531 -3.32359202
[16,] 2.60323768 -1.71807531
[17,] 0.71112662 2.60323768
[18,] 2.11961841 0.71112662
[19,] -1.64025742 2.11961841
[20,] -2.44442246 -1.64025742
[21,] -2.30314647 -2.44442246
[22,] 2.56798409 -2.30314647
[23,] 0.28478045 2.56798409
[24,] 4.89519427 0.28478045
[25,] 7.36864586 4.89519427
[26,] 1.26008489 7.36864586
[27,] -2.64938575 1.26008489
[28,] -0.72493983 -2.64938575
[29,] -0.07210794 -0.72493983
[30,] -2.46721036 -0.07210794
[31,] -5.74852498 -2.46721036
[32,] 3.83128461 -5.74852498
[33,] -2.47426461 3.83128461
[34,] 1.98314586 -2.47426461
[35,] -1.76625665 1.98314586
[36,] -2.44821920 -1.76625665
[37,] 2.23004044 -2.44821920
[38,] -4.53113271 2.23004044
[39,] 0.80437586 -4.53113271
[40,] 3.04390513 0.80437586
[41,] 0.46381247 3.04390513
[42,] 3.77266004 0.46381247
[43,] 1.70382251 3.77266004
[44,] 6.25629851 1.70382251
[45,] 3.22627118 6.25629851
[46,] 1.05463983 3.22627118
[47,] -1.58327898 1.05463983
[48,] 0.19976207 -1.58327898
[49,] 4.68122690 0.19976207
[50,] -4.38582615 4.68122690
[51,] -0.25465808 -4.38582615
[52,] -0.69733460 -0.25465808
[53,] -3.04988777 -0.69733460
[54,] -0.38055504 -3.04988777
[55,] 2.09570054 -0.38055504
[56,] 4.45585159 2.09570054
[57,] -5.83285397 4.45585159
[58,] 1.87847794 -5.83285397
[59,] 0.63904946 1.87847794
[60,] -0.50702304 0.63904946
[61,] 3.72100600 -0.50702304
[62,] -0.77497439 3.72100600
[63,] -3.19633198 -0.77497439
[64,] 1.03485622 -3.19633198
[65,] -0.99257929 1.03485622
[66,] 1.05655860 -0.99257929
[67,] -1.27717937 1.05655860
[68,] 2.69066154 -1.27717937
[69,] -0.68423830 2.69066154
[70,] 3.05325090 -0.68423830
[71,] -4.64291423 3.05325090
[72,] -2.81549965 -4.64291423
[73,] 0.40032782 -2.81549965
[74,] 2.48855212 0.40032782
[75,] 6.29968133 2.48855212
[76,] 1.15141163 6.29968133
[77,] 0.84248076 1.15141163
[78,] -4.82501170 0.84248076
[79,] 5.59561105 -4.82501170
[80,] -2.27155970 5.59561105
[81,] -1.26444869 -2.27155970
[82,] 3.18941935 -1.26444869
[83,] -2.45793769 3.18941935
[84,] -0.31286571 -2.45793769
[85,] 1.59473323 -0.31286571
[86,] -1.47141267 1.59473323
[87,] -4.20608039 -1.47141267
[88,] -0.10694026 -4.20608039
[89,] -4.16558598 -0.10694026
[90,] 2.75167142 -4.16558598
[91,] -2.45911984 2.75167142
[92,] 0.54926614 -2.45911984
[93,] -3.13302747 0.54926614
[94,] 3.55163503 -3.13302747
[95,] 2.30215271 3.55163503
[96,] 1.74538343 2.30215271
[97,] 2.38400752 1.74538343
[98,] -3.32112702 2.38400752
[99,] 2.59265910 -3.32112702
[100,] 2.23751351 2.59265910
[101,] -0.58720870 2.23751351
[102,] 0.06631302 -0.58720870
[103,] -3.44626819 0.06631302
[104,] 7.51282393 -3.44626819
[105,] 2.61476017 7.51282393
[106,] 4.57479922 2.61476017
[107,] 1.56749722 4.57479922
[108,] 6.38198511 1.56749722
[109,] -4.64039091 6.38198511
[110,] -2.59930379 -4.64039091
[111,] -6.17000557 -2.59930379
[112,] 0.69563026 -6.17000557
[113,] 2.71612685 0.69563026
[114,] 2.61462646 2.71612685
[115,] -3.42443321 2.61462646
[116,] -3.27837869 -3.42443321
[117,] -1.66882848 -3.27837869
[118,] 2.52896952 -1.66882848
[119,] -1.91735005 2.52896952
[120,] -0.34576985 -1.91735005
[121,] 1.47597222 -0.34576985
[122,] 0.39540475 1.47597222
[123,] 2.78245560 0.39540475
[124,] -2.93642722 2.78245560
[125,] 4.80944707 -2.93642722
[126,] 7.03289355 4.80944707
[127,] -3.21872685 7.03289355
[128,] 1.48597968 -3.21872685
[129,] -1.99003025 1.48597968
[130,] -4.01552798 -1.99003025
[131,] 3.94611688 -4.01552798
[132,] -5.03874426 3.94611688
[133,] 0.86079815 -5.03874426
[134,] -0.50510640 0.86079815
[135,] 0.01362954 -0.50510640
[136,] -1.10579566 0.01362954
[137,] -3.97535288 -1.10579566
[138,] 0.81013433 -3.97535288
[139,] -3.08236022 0.81013433
[140,] -2.58704456 -3.08236022
[141,] -5.86215115 -2.58704456
[142,] -5.26860674 -5.86215115
[143,] 1.22878608 -5.26860674
[144,] 7.06523841 1.22878608
[145,] 0.02004863 7.06523841
[146,] 1.19439858 0.02004863
[147,] -1.29333060 1.19439858
[148,] 2.05961089 -1.29333060
[149,] 1.62217251 2.05961089
[150,] 6.77264808 1.62217251
[151,] -2.27376590 6.77264808
[152,] -1.84399702 -2.27376590
[153,] 4.14243504 -1.84399702
[154,] 0.35961589 4.14243504
[155,] 2.75167142 0.35961589
[156,] -3.55537009 2.75167142
[157,] -3.21872685 -3.55537009
[158,] 0.02830596 -3.21872685
[159,] -3.35451739 0.02830596
[160,] -1.32018846 -3.35451739
[161,] -3.12883899 -1.32018846
[162,] 0.73016759 -3.12883899
[163,] 4.10387390 0.73016759
[164,] -1.97562694 4.10387390
[165,] -7.03064895 -1.97562694
[166,] -3.96986546 -7.03064895
[167,] -3.55531863 -3.96986546
[168,] -1.51938815 -3.55531863
[169,] -7.79713059 -1.51938815
[170,] 4.87849825 -7.79713059
[171,] 0.09466462 4.87849825
[172,] 6.75015868 0.09466462
[173,] -0.10564445 6.75015868
[174,] 4.64862688 -0.10564445
[175,] -2.56282549 4.64862688
[176,] 3.62277566 -2.56282549
[177,] -1.67436450 3.62277566
[178,] -2.14055266 -1.67436450
[179,] 3.31579992 -2.14055266
[180,] 0.78809614 3.31579992
[181,] 2.64589276 0.78809614
[182,] -4.98598342 2.64589276
[183,] 4.37351297 -4.98598342
[184,] -9.19221726 4.37351297
[185,] -2.65424878 -9.19221726
[186,] 1.68424518 -2.65424878
[187,] 6.10375788 1.68424518
[188,] -2.53918023 6.10375788
[189,] -2.34544294 -2.53918023
[190,] -0.72929386 -2.34544294
[191,] 0.96671857 -0.72929386
[192,] -1.61715009 0.96671857
[193,] -0.82497507 -1.61715009
[194,] -1.20801002 -0.82497507
[195,] 0.80983009 -1.20801002
[196,] 1.01536432 0.80983009
[197,] -2.83822902 1.01536432
[198,] -0.01213091 -2.83822902
[199,] -7.21225442 -0.01213091
[200,] -2.54634391 -7.21225442
[201,] -8.26500321 -2.54634391
[202,] 1.73943954 -8.26500321
[203,] 1.05216164 1.73943954
[204,] -1.48448874 1.05216164
[205,] -1.25899179 -1.48448874
[206,] -1.48357954 -1.25899179
[207,] 0.09513499 -1.48357954
[208,] 2.58628624 0.09513499
[209,] -0.07471962 2.58628624
[210,] 0.96139868 -0.07471962
[211,] 2.20652198 0.96139868
[212,] -4.44322074 2.20652198
[213,] -3.66233944 -4.44322074
[214,] 0.64159055 -3.66233944
[215,] -2.47542664 0.64159055
[216,] -2.51097184 -2.47542664
[217,] 4.35836550 -2.51097184
[218,] 2.48663674 4.35836550
[219,] -0.87042621 2.48663674
[220,] 1.46165746 -0.87042621
[221,] 3.58014471 1.46165746
[222,] -0.07819193 3.58014471
[223,] -2.39407920 -0.07819193
[224,] 1.86501557 -2.39407920
[225,] -2.45281516 1.86501557
[226,] -2.43165428 -2.45281516
[227,] -0.50987136 -2.43165428
[228,] 1.26185988 -0.50987136
[229,] -0.31215136 1.26185988
[230,] 1.49708648 -0.31215136
[231,] -2.38090907 1.49708648
[232,] -2.85352692 -2.38090907
[233,] 6.71962728 -2.85352692
[234,] 0.45586776 6.71962728
[235,] 3.66426261 0.45586776
[236,] 5.59450040 3.66426261
[237,] 4.99635046 5.59450040
[238,] -4.01500098 4.99635046
[239,] 5.02252653 -4.01500098
[240,] -6.37481104 5.02252653
[241,] 1.26652608 -6.37481104
[242,] -2.79490815 1.26652608
[243,] 1.57293559 -2.79490815
[244,] 3.37526729 1.57293559
[245,] -4.85858005 3.37526729
[246,] -2.70971719 -4.85858005
[247,] 3.87302716 -2.70971719
[248,] -8.86389638 3.87302716
[249,] -6.07481677 -8.86389638
[250,] -2.74219826 -6.07481677
[251,] 0.39801194 -2.74219826
[252,] 1.75157384 0.39801194
[253,] -0.82597267 1.75157384
[254,] 3.50626379 -0.82597267
[255,] 0.19023295 3.50626379
[256,] 0.24514340 0.19023295
[257,] -2.13676348 0.24514340
[258,] 3.08475444 -2.13676348
[259,] 3.24254823 3.08475444
[260,] -5.42398047 3.24254823
[261,] -1.41557340 -5.42398047
[262,] 6.00219725 -1.41557340
[263,] -4.33730700 6.00219725
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.89741706 5.50366927
2 -5.23810507 4.89741706
3 -3.03734960 -5.23810507
4 -1.02002077 -3.03734960
5 2.87341102 -1.02002077
6 5.52020998 2.87341102
7 -1.36579916 5.52020998
8 1.17971113 -1.36579916
9 0.66597030 1.17971113
10 4.26186607 0.66597030
11 1.29439677 4.26186607
12 3.27356843 1.29439677
13 2.99607580 3.27356843
14 -3.32359202 2.99607580
15 -1.71807531 -3.32359202
16 2.60323768 -1.71807531
17 0.71112662 2.60323768
18 2.11961841 0.71112662
19 -1.64025742 2.11961841
20 -2.44442246 -1.64025742
21 -2.30314647 -2.44442246
22 2.56798409 -2.30314647
23 0.28478045 2.56798409
24 4.89519427 0.28478045
25 7.36864586 4.89519427
26 1.26008489 7.36864586
27 -2.64938575 1.26008489
28 -0.72493983 -2.64938575
29 -0.07210794 -0.72493983
30 -2.46721036 -0.07210794
31 -5.74852498 -2.46721036
32 3.83128461 -5.74852498
33 -2.47426461 3.83128461
34 1.98314586 -2.47426461
35 -1.76625665 1.98314586
36 -2.44821920 -1.76625665
37 2.23004044 -2.44821920
38 -4.53113271 2.23004044
39 0.80437586 -4.53113271
40 3.04390513 0.80437586
41 0.46381247 3.04390513
42 3.77266004 0.46381247
43 1.70382251 3.77266004
44 6.25629851 1.70382251
45 3.22627118 6.25629851
46 1.05463983 3.22627118
47 -1.58327898 1.05463983
48 0.19976207 -1.58327898
49 4.68122690 0.19976207
50 -4.38582615 4.68122690
51 -0.25465808 -4.38582615
52 -0.69733460 -0.25465808
53 -3.04988777 -0.69733460
54 -0.38055504 -3.04988777
55 2.09570054 -0.38055504
56 4.45585159 2.09570054
57 -5.83285397 4.45585159
58 1.87847794 -5.83285397
59 0.63904946 1.87847794
60 -0.50702304 0.63904946
61 3.72100600 -0.50702304
62 -0.77497439 3.72100600
63 -3.19633198 -0.77497439
64 1.03485622 -3.19633198
65 -0.99257929 1.03485622
66 1.05655860 -0.99257929
67 -1.27717937 1.05655860
68 2.69066154 -1.27717937
69 -0.68423830 2.69066154
70 3.05325090 -0.68423830
71 -4.64291423 3.05325090
72 -2.81549965 -4.64291423
73 0.40032782 -2.81549965
74 2.48855212 0.40032782
75 6.29968133 2.48855212
76 1.15141163 6.29968133
77 0.84248076 1.15141163
78 -4.82501170 0.84248076
79 5.59561105 -4.82501170
80 -2.27155970 5.59561105
81 -1.26444869 -2.27155970
82 3.18941935 -1.26444869
83 -2.45793769 3.18941935
84 -0.31286571 -2.45793769
85 1.59473323 -0.31286571
86 -1.47141267 1.59473323
87 -4.20608039 -1.47141267
88 -0.10694026 -4.20608039
89 -4.16558598 -0.10694026
90 2.75167142 -4.16558598
91 -2.45911984 2.75167142
92 0.54926614 -2.45911984
93 -3.13302747 0.54926614
94 3.55163503 -3.13302747
95 2.30215271 3.55163503
96 1.74538343 2.30215271
97 2.38400752 1.74538343
98 -3.32112702 2.38400752
99 2.59265910 -3.32112702
100 2.23751351 2.59265910
101 -0.58720870 2.23751351
102 0.06631302 -0.58720870
103 -3.44626819 0.06631302
104 7.51282393 -3.44626819
105 2.61476017 7.51282393
106 4.57479922 2.61476017
107 1.56749722 4.57479922
108 6.38198511 1.56749722
109 -4.64039091 6.38198511
110 -2.59930379 -4.64039091
111 -6.17000557 -2.59930379
112 0.69563026 -6.17000557
113 2.71612685 0.69563026
114 2.61462646 2.71612685
115 -3.42443321 2.61462646
116 -3.27837869 -3.42443321
117 -1.66882848 -3.27837869
118 2.52896952 -1.66882848
119 -1.91735005 2.52896952
120 -0.34576985 -1.91735005
121 1.47597222 -0.34576985
122 0.39540475 1.47597222
123 2.78245560 0.39540475
124 -2.93642722 2.78245560
125 4.80944707 -2.93642722
126 7.03289355 4.80944707
127 -3.21872685 7.03289355
128 1.48597968 -3.21872685
129 -1.99003025 1.48597968
130 -4.01552798 -1.99003025
131 3.94611688 -4.01552798
132 -5.03874426 3.94611688
133 0.86079815 -5.03874426
134 -0.50510640 0.86079815
135 0.01362954 -0.50510640
136 -1.10579566 0.01362954
137 -3.97535288 -1.10579566
138 0.81013433 -3.97535288
139 -3.08236022 0.81013433
140 -2.58704456 -3.08236022
141 -5.86215115 -2.58704456
142 -5.26860674 -5.86215115
143 1.22878608 -5.26860674
144 7.06523841 1.22878608
145 0.02004863 7.06523841
146 1.19439858 0.02004863
147 -1.29333060 1.19439858
148 2.05961089 -1.29333060
149 1.62217251 2.05961089
150 6.77264808 1.62217251
151 -2.27376590 6.77264808
152 -1.84399702 -2.27376590
153 4.14243504 -1.84399702
154 0.35961589 4.14243504
155 2.75167142 0.35961589
156 -3.55537009 2.75167142
157 -3.21872685 -3.55537009
158 0.02830596 -3.21872685
159 -3.35451739 0.02830596
160 -1.32018846 -3.35451739
161 -3.12883899 -1.32018846
162 0.73016759 -3.12883899
163 4.10387390 0.73016759
164 -1.97562694 4.10387390
165 -7.03064895 -1.97562694
166 -3.96986546 -7.03064895
167 -3.55531863 -3.96986546
168 -1.51938815 -3.55531863
169 -7.79713059 -1.51938815
170 4.87849825 -7.79713059
171 0.09466462 4.87849825
172 6.75015868 0.09466462
173 -0.10564445 6.75015868
174 4.64862688 -0.10564445
175 -2.56282549 4.64862688
176 3.62277566 -2.56282549
177 -1.67436450 3.62277566
178 -2.14055266 -1.67436450
179 3.31579992 -2.14055266
180 0.78809614 3.31579992
181 2.64589276 0.78809614
182 -4.98598342 2.64589276
183 4.37351297 -4.98598342
184 -9.19221726 4.37351297
185 -2.65424878 -9.19221726
186 1.68424518 -2.65424878
187 6.10375788 1.68424518
188 -2.53918023 6.10375788
189 -2.34544294 -2.53918023
190 -0.72929386 -2.34544294
191 0.96671857 -0.72929386
192 -1.61715009 0.96671857
193 -0.82497507 -1.61715009
194 -1.20801002 -0.82497507
195 0.80983009 -1.20801002
196 1.01536432 0.80983009
197 -2.83822902 1.01536432
198 -0.01213091 -2.83822902
199 -7.21225442 -0.01213091
200 -2.54634391 -7.21225442
201 -8.26500321 -2.54634391
202 1.73943954 -8.26500321
203 1.05216164 1.73943954
204 -1.48448874 1.05216164
205 -1.25899179 -1.48448874
206 -1.48357954 -1.25899179
207 0.09513499 -1.48357954
208 2.58628624 0.09513499
209 -0.07471962 2.58628624
210 0.96139868 -0.07471962
211 2.20652198 0.96139868
212 -4.44322074 2.20652198
213 -3.66233944 -4.44322074
214 0.64159055 -3.66233944
215 -2.47542664 0.64159055
216 -2.51097184 -2.47542664
217 4.35836550 -2.51097184
218 2.48663674 4.35836550
219 -0.87042621 2.48663674
220 1.46165746 -0.87042621
221 3.58014471 1.46165746
222 -0.07819193 3.58014471
223 -2.39407920 -0.07819193
224 1.86501557 -2.39407920
225 -2.45281516 1.86501557
226 -2.43165428 -2.45281516
227 -0.50987136 -2.43165428
228 1.26185988 -0.50987136
229 -0.31215136 1.26185988
230 1.49708648 -0.31215136
231 -2.38090907 1.49708648
232 -2.85352692 -2.38090907
233 6.71962728 -2.85352692
234 0.45586776 6.71962728
235 3.66426261 0.45586776
236 5.59450040 3.66426261
237 4.99635046 5.59450040
238 -4.01500098 4.99635046
239 5.02252653 -4.01500098
240 -6.37481104 5.02252653
241 1.26652608 -6.37481104
242 -2.79490815 1.26652608
243 1.57293559 -2.79490815
244 3.37526729 1.57293559
245 -4.85858005 3.37526729
246 -2.70971719 -4.85858005
247 3.87302716 -2.70971719
248 -8.86389638 3.87302716
249 -6.07481677 -8.86389638
250 -2.74219826 -6.07481677
251 0.39801194 -2.74219826
252 1.75157384 0.39801194
253 -0.82597267 1.75157384
254 3.50626379 -0.82597267
255 0.19023295 3.50626379
256 0.24514340 0.19023295
257 -2.13676348 0.24514340
258 3.08475444 -2.13676348
259 3.24254823 3.08475444
260 -5.42398047 3.24254823
261 -1.41557340 -5.42398047
262 6.00219725 -1.41557340
263 -4.33730700 6.00219725
> 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/78tr21351941589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/86fp81351941589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/996iq1351941589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/100ztu1351941589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11xyql1351941589.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12yypc1351941589.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13wjo41351941589.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14cbru1351941589.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/158las1351941589.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16sgft1351941589.tab")
+ }
>
> try(system("convert tmp/1ekgn1351941589.ps tmp/1ekgn1351941589.png",intern=TRUE))
character(0)
> try(system("convert tmp/2t5nu1351941589.ps tmp/2t5nu1351941589.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qgw51351941589.ps tmp/3qgw51351941589.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tkdq1351941589.ps tmp/4tkdq1351941589.png",intern=TRUE))
character(0)
> try(system("convert tmp/5u2ds1351941589.ps tmp/5u2ds1351941589.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ygte1351941589.ps tmp/6ygte1351941589.png",intern=TRUE))
character(0)
> try(system("convert tmp/78tr21351941589.ps tmp/78tr21351941589.png",intern=TRUE))
character(0)
> try(system("convert tmp/86fp81351941589.ps tmp/86fp81351941589.png",intern=TRUE))
character(0)
> try(system("convert tmp/996iq1351941589.ps tmp/996iq1351941589.png",intern=TRUE))
character(0)
> try(system("convert tmp/100ztu1351941589.ps tmp/100ztu1351941589.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.483 1.177 12.658