R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
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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'
+ ,'Seperate'
+ ,'Learning'
+ ,'Software'
+ ,'Hapiness'
+ ,'Depression'
+ ,'belonging'
+ ,'Belonging_Fin')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Connected','Seperate','Learning','Software','Hapiness','Depression','belonging','Belonging_Fin'),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 = '3'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'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
Learning Connected Seperate Software Hapiness Depression belonging
1 13 1 38 12 14 12.0 53
2 16 39 32 11 18 11.0 83
3 19 30 35 15 11 14.0 66
4 15 31 33 6 12 12.0 67
5 14 34 37 13 16 21.0 76
6 13 35 29 10 18 12.0 78
7 19 39 31 12 14 22.0 53
8 15 34 36 14 14 11.0 80
9 14 36 35 12 15 10.0 74
10 15 37 38 9 15 13.0 76
11 16 38 31 10 17 10.0 79
12 16 36 34 12 19 8.0 54
13 16 38 35 12 10 15.0 67
14 16 39 38 11 16 14.0 54
15 17 33 37 15 18 10.0 87
16 15 32 33 12 14 14.0 58
17 15 36 32 10 14 14.0 75
18 20 38 38 12 17 11.0 88
19 18 39 38 11 14 10.0 64
20 16 32 32 12 16 13.0 57
21 16 32 33 11 18 9.5 66
22 16 31 31 12 11 14.0 68
23 19 39 38 13 14 12.0 54
24 16 37 39 11 12 14.0 56
25 17 39 32 12 17 11.0 86
26 17 41 32 13 9 9.0 80
27 16 36 35 10 16 11.0 76
28 15 33 37 14 14 15.0 69
29 16 33 33 12 15 14.0 78
30 14 34 33 10 11 13.0 67
31 15 31 31 12 16 9.0 80
32 12 27 32 8 13 15.0 54
33 14 37 31 10 17 10.0 71
34 16 34 37 12 15 11.0 84
35 14 34 30 12 14 13.0 74
36 10 32 33 7 16 8.0 71
37 10 29 31 9 9 20.0 63
38 14 36 33 12 15 12.0 71
39 16 29 31 10 17 10.0 76
40 16 35 33 10 13 10.0 69
41 16 37 32 10 15 9.0 74
42 14 34 33 12 16 14.0 75
43 20 38 32 15 16 8.0 54
44 14 35 33 10 12 14.0 52
45 14 38 28 10 15 11.0 69
46 11 37 35 12 11 13.0 68
47 14 38 39 13 15 9.0 65
48 15 33 34 11 15 11.0 75
49 16 36 38 11 17 15.0 74
50 14 38 32 12 13 11.0 75
51 16 32 38 14 16 10.0 72
52 14 32 30 10 14 14.0 67
53 12 32 33 12 11 18.0 63
54 16 34 38 13 12 14.0 62
55 9 32 32 5 12 11.0 63
56 14 37 35 6 15 14.5 76
57 16 39 34 12 16 13.0 74
58 16 29 34 12 15 9.0 67
59 15 37 36 11 12 10.0 73
60 16 35 34 10 12 15.0 70
61 12 30 28 7 8 20.0 53
62 16 38 34 12 13 12.0 77
63 16 34 35 14 11 12.0 80
64 14 31 35 11 14 14.0 52
65 16 34 31 12 15 13.0 54
66 17 35 37 13 10 11.0 80
67 18 36 35 14 11 17.0 66
68 18 30 27 11 12 12.0 73
69 12 39 40 12 15 13.0 63
70 16 35 37 12 15 14.0 69
71 10 38 36 8 14 13.0 67
72 14 31 38 11 16 15.0 54
73 18 34 39 14 15 13.0 81
74 18 38 41 14 15 10.0 69
75 16 34 27 12 13 11.0 84
76 17 39 30 9 12 19.0 80
77 16 37 37 13 17 13.0 70
78 16 34 31 11 13 17.0 69
79 13 28 31 12 15 13.0 77
80 16 37 27 12 13 9.0 54
81 16 33 36 12 15 11.0 79
82 16 35 37 12 15 9.0 71
83 15 37 33 12 16 12.0 73
84 15 32 34 11 15 12.0 72
85 16 33 31 10 14 13.0 77
86 14 38 39 9 15 13.0 75
87 16 33 34 12 14 12.0 69
88 16 29 32 12 13 15.0 54
89 15 33 33 12 7 22.0 70
90 12 31 36 9 17 13.0 73
91 17 36 32 15 13 15.0 54
92 16 35 41 12 15 13.0 77
93 15 32 28 12 14 15.0 82
94 13 29 30 12 13 12.5 80
95 16 39 36 10 16 11.0 80
96 16 37 35 13 12 16.0 69
97 16 35 31 9 14 11.0 78
98 16 37 34 12 17 11.0 81
99 14 32 36 10 15 10.0 76
100 16 38 36 14 17 10.0 76
101 16 37 35 11 12 16.0 73
102 20 36 37 15 16 12.0 85
103 15 32 28 11 11 11.0 66
104 16 33 39 11 15 16.0 79
105 13 40 32 12 9 19.0 68
106 17 38 35 12 16 11.0 76
107 16 41 39 12 15 16.0 71
108 16 36 35 11 10 15.0 54
109 12 43 42 7 10 24.0 46
110 16 30 34 12 15 14.0 85
111 16 31 33 14 11 15.0 74
112 17 32 41 11 13 11.0 88
113 13 32 33 11 14 15.0 38
114 12 37 34 10 18 12.0 76
115 18 37 32 13 16 10.0 86
116 14 33 40 13 14 14.0 54
117 14 34 40 8 14 13.0 67
118 13 33 35 11 14 9.0 69
119 16 38 36 12 14 15.0 90
120 13 33 37 11 12 15.0 54
121 16 31 27 13 14 14.0 76
122 13 38 39 12 15 11.0 89
123 16 37 38 14 15 8.0 76
124 15 36 31 13 15 11.0 73
125 16 31 33 15 13 11.0 79
126 15 39 32 10 17 8.0 90
127 17 44 39 11 17 10.0 74
128 15 33 36 9 19 11.0 81
129 12 35 33 11 15 13.0 72
130 16 32 33 10 13 11.0 71
131 10 28 32 11 9 20.0 66
132 16 40 37 8 15 10.0 77
133 12 27 30 11 15 15.0 65
134 14 37 38 12 15 12.0 74
135 15 32 29 12 16 14.0 85
136 13 28 22 9 11 23.0 54
137 15 34 35 11 14 14.0 63
138 11 30 35 10 11 16.0 54
139 12 35 34 8 15 11.0 64
140 11 31 35 9 13 12.0 69
141 16 32 34 8 15 10.0 54
142 15 30 37 9 16 14.0 84
143 17 30 35 15 14 12.0 86
144 16 31 23 11 15 12.0 77
145 10 40 31 8 16 11.0 89
146 18 32 27 13 16 12.0 76
147 13 36 36 12 11 13.0 60
148 16 32 31 12 12 11.0 75
149 13 35 32 9 9 19.0 73
150 10 38 39 7 16 12.0 85
151 15 42 37 13 13 17.0 79
152 16 34 38 9 16 9.0 71
153 16 35 39 6 12 12.0 72
154 14 38 34 8 9 19.0 69
155 10 33 31 8 13 18.0 78
156 17 36 32 15 13 15.0 54
157 13 32 37 6 14 14.0 69
158 15 33 36 9 19 11.0 81
159 16 34 32 11 13 9.0 84
160 12 32 38 8 12 18.0 84
161 13 34 36 8 13 16.0 69
162 13 27 26 10 10 24.0 66
163 12 31 26 8 14 14.0 81
164 17 38 33 14 16 20.0 82
165 15 34 39 10 10 18.0 72
166 10 24 30 8 11 23.0 54
167 14 30 33 11 14 12.0 78
168 11 26 25 12 12 14.0 74
169 13 34 38 12 9 16.0 82
170 16 27 37 12 9 18.0 73
171 12 37 31 5 11 20.0 55
172 16 36 37 12 16 12.0 72
173 12 41 35 10 9 12.0 78
174 9 29 25 7 13 17.0 59
175 12 36 28 12 16 13.0 72
176 15 32 35 11 13 9.0 78
177 12 37 33 8 9 16.0 68
178 12 30 30 9 12 18.0 69
179 14 31 31 10 16 10.0 67
180 12 38 37 9 11 14.0 74
181 16 36 36 12 14 11.0 54
182 11 35 30 6 13 9.0 67
183 19 31 36 15 15 11.0 70
184 15 38 32 12 14 10.0 80
185 8 22 28 12 16 11.0 89
186 16 32 36 12 13 19.0 76
187 17 36 34 11 14 14.0 74
188 12 39 31 7 15 12.0 87
189 11 28 28 7 13 14.0 54
190 11 32 36 5 11 21.0 61
191 14 32 36 12 11 13.0 38
192 16 38 40 12 14 10.0 75
193 12 32 33 3 15 15.0 69
194 16 35 37 11 11 16.0 62
195 13 32 32 10 15 14.0 72
196 15 37 38 12 12 12.0 70
197 16 34 31 9 14 19.0 79
198 16 33 37 12 14 15.0 87
199 14 33 33 9 8 19.0 62
200 16 26 32 12 13 13.0 77
201 16 30 30 12 9 17.0 69
202 14 24 30 10 15 12.0 69
203 11 34 31 9 17 11.0 75
204 12 34 32 12 13 14.0 54
205 15 33 34 8 15 11.0 72
206 15 34 36 11 15 13.0 74
207 16 35 37 11 14 12.0 85
208 16 35 36 12 16 15.0 52
209 11 36 33 10 13 14.0 70
210 15 34 33 10 16 12.0 84
211 12 34 33 12 9 17.0 64
212 12 41 44 12 16 11.0 84
213 15 32 39 11 11 18.0 87
214 15 30 32 8 10 13.0 79
215 16 35 35 12 11 17.0 67
216 14 28 25 10 15 13.0 65
217 17 33 35 11 17 11.0 85
218 14 39 34 10 14 12.0 83
219 13 36 35 8 8 22.0 61
220 15 36 39 12 15 14.0 82
221 13 35 33 12 11 12.0 76
222 14 38 36 10 16 12.0 58
223 15 33 32 12 10 17.0 72
224 12 31 32 9 15 9.0 72
225 13 34 36 9 9 21.0 38
226 8 32 36 6 16 10.0 78
227 14 31 32 10 19 11.0 54
228 14 33 34 9 12 12.0 63
229 11 34 33 9 8 23.0 66
230 12 34 35 9 11 13.0 70
231 13 34 30 6 14 12.0 71
232 10 33 38 10 9 16.0 67
233 16 32 34 6 15 9.0 58
234 18 41 33 14 13 17.0 72
235 13 34 32 10 16 9.0 72
236 11 36 31 10 11 14.0 70
237 4 37 30 6 12 17.0 76
238 13 36 27 12 13 13.0 50
239 16 29 31 12 10 11.0 72
240 10 37 30 7 11 12.0 72
241 12 27 32 8 12 10.0 88
242 12 35 35 11 8 19.0 53
243 10 28 28 3 12 16.0 58
244 13 35 33 6 12 16.0 66
245 15 37 31 10 15 14.0 82
246 12 29 35 8 11 20.0 69
247 14 32 35 9 13 15.0 68
248 10 36 32 9 14 23.0 44
249 12 19 21 8 10 20.0 56
250 12 21 20 9 12 16.0 53
251 11 31 34 7 15 14.0 70
252 10 33 32 7 13 17.0 78
253 12 36 34 6 13 11.0 71
254 16 33 32 9 13 13.0 72
255 12 37 33 10 12 17.0 68
256 14 34 33 11 12 15.0 67
257 16 35 37 12 9 21.0 75
258 14 31 32 8 9 18.0 62
259 13 37 34 11 15 15.0 67
260 4 35 30 3 10 8.0 83
261 15 27 30 11 14 12.0 64
262 11 34 38 12 15 12.0 68
263 11 40 36 7 7 22.0 62
264 14 29 32 9 14 12.0 72
Belonging_Fin
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) Connected Seperate Software Hapiness
3.63882 0.05800 0.03994 0.60865 0.09825
Depression belonging Belonging_Fin
-0.04110 0.01452 -0.02053
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.1929 -1.2611 0.2691 1.1836 4.2931
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.63882 1.87419 1.942 0.0533 .
Connected 0.05800 0.02937 1.975 0.0494 *
Seperate 0.03994 0.03405 1.173 0.2419
Software 0.60865 0.05161 11.794 <2e-16 ***
Hapiness 0.09825 0.05772 1.702 0.0899 .
Depression -0.04110 0.04233 -0.971 0.3325
belonging 0.01452 0.03762 0.386 0.6998
Belonging_Fin -0.02053 0.05614 -0.366 0.7149
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.877 on 256 degrees of freedom
Multiple R-squared: 0.4315, Adjusted R-squared: 0.416
F-statistic: 27.76 on 7 and 256 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.170144339 0.340288679 0.8298557
[2,] 0.234194045 0.468388090 0.7658060
[3,] 0.159791946 0.319583892 0.8402081
[4,] 0.098197267 0.196394535 0.9018027
[5,] 0.090836866 0.181673731 0.9091631
[6,] 0.052214121 0.104428243 0.9477859
[7,] 0.049350361 0.098700721 0.9506496
[8,] 0.212786552 0.425573103 0.7872134
[9,] 0.165921612 0.331843225 0.8340784
[10,] 0.114312558 0.228625115 0.8856874
[11,] 0.076128543 0.152257085 0.9238715
[12,] 0.062483203 0.124966406 0.9375168
[13,] 0.101465471 0.202930942 0.8985345
[14,] 0.167688790 0.335377580 0.8323112
[15,] 0.149548326 0.299096651 0.8504517
[16,] 0.124823409 0.249646819 0.8751766
[17,] 0.143402743 0.286805485 0.8565973
[18,] 0.164230813 0.328461625 0.8357692
[19,] 0.146582235 0.293164469 0.8534178
[20,] 0.165141752 0.330283503 0.8348582
[21,] 0.129435106 0.258870212 0.8705649
[22,] 0.121675987 0.243351974 0.8783240
[23,] 0.106906091 0.213812181 0.8930939
[24,] 0.084025561 0.168051122 0.9159744
[25,] 0.066302572 0.132605145 0.9336974
[26,] 0.151585309 0.303170617 0.8484147
[27,] 0.191995010 0.383990021 0.8080050
[28,] 0.191315624 0.382631249 0.8086844
[29,] 0.213321165 0.426642330 0.7866788
[30,] 0.193085510 0.386171021 0.8069145
[31,] 0.167550290 0.335100579 0.8324497
[32,] 0.145671780 0.291343559 0.8543282
[33,] 0.142397114 0.284794228 0.8576029
[34,] 0.118036318 0.236072636 0.8819637
[35,] 0.098602243 0.197204486 0.9013978
[36,] 0.264360347 0.528720694 0.7356397
[37,] 0.446208955 0.892417911 0.5537910
[38,] 0.397819116 0.795638233 0.6021809
[39,] 0.372060931 0.744121862 0.6279391
[40,] 0.355987133 0.711974265 0.6440129
[41,] 0.319859549 0.639719098 0.6801405
[42,] 0.278578942 0.557157885 0.7214211
[43,] 0.296357590 0.592715180 0.7036424
[44,] 0.281249741 0.562499481 0.7187503
[45,] 0.284406364 0.568812728 0.7155936
[46,] 0.270064032 0.540128063 0.7299360
[47,] 0.233932244 0.467864488 0.7660678
[48,] 0.204118806 0.408237613 0.7958812
[49,] 0.173900835 0.347801669 0.8260992
[50,] 0.180060676 0.360121352 0.8199393
[51,] 0.160189897 0.320379794 0.8398101
[52,] 0.138547729 0.277095458 0.8614523
[53,] 0.115864873 0.231729747 0.8841351
[54,] 0.097186025 0.194372051 0.9028140
[55,] 0.081656751 0.163313502 0.9183432
[56,] 0.074276865 0.148553729 0.9257231
[57,] 0.071443901 0.142887802 0.9285561
[58,] 0.169652645 0.339305291 0.8303474
[59,] 0.268844951 0.537689902 0.7311550
[60,] 0.238606535 0.477213071 0.7613935
[61,] 0.347039233 0.694078466 0.6529608
[62,] 0.311575685 0.623151370 0.6884243
[63,] 0.301057026 0.602114052 0.6989430
[64,] 0.280592226 0.561184452 0.7194078
[65,] 0.251035829 0.502071657 0.7489642
[66,] 0.325639366 0.651278732 0.6743606
[67,] 0.292181165 0.584362329 0.7078188
[68,] 0.273518818 0.547037636 0.7264812
[69,] 0.289448148 0.578896296 0.7105519
[70,] 0.263291496 0.526582993 0.7367085
[71,] 0.235402730 0.470805461 0.7645973
[72,] 0.206576274 0.413152549 0.7934237
[73,] 0.184091491 0.368182983 0.8159085
[74,] 0.159217200 0.318434401 0.8407828
[75,] 0.157337705 0.314675410 0.8426623
[76,] 0.134799633 0.269599265 0.8652004
[77,] 0.117125246 0.234250492 0.8828748
[78,] 0.106371606 0.212743211 0.8936284
[79,] 0.091074327 0.182148655 0.9089257
[80,] 0.089918816 0.179837631 0.9100812
[81,] 0.076516310 0.153032621 0.9234837
[82,] 0.064886492 0.129772984 0.9351135
[83,] 0.054284099 0.108568198 0.9457159
[84,] 0.056526100 0.113052200 0.9434739
[85,] 0.051222742 0.102445483 0.9487773
[86,] 0.042487671 0.084975341 0.9575123
[87,] 0.047873066 0.095746133 0.9521269
[88,] 0.039284629 0.078569257 0.9607154
[89,] 0.032029382 0.064058764 0.9679706
[90,] 0.027936856 0.055873713 0.9720631
[91,] 0.024824464 0.049648929 0.9751755
[92,] 0.029751295 0.059502590 0.9702487
[93,] 0.025372562 0.050745124 0.9746274
[94,] 0.022484647 0.044969294 0.9775154
[95,] 0.025913572 0.051827145 0.9740864
[96,] 0.022712205 0.045424410 0.9772878
[97,] 0.018269815 0.036539630 0.9817302
[98,] 0.017783960 0.035567920 0.9822160
[99,] 0.014328028 0.028656055 0.9856720
[100,] 0.011630005 0.023260010 0.9883700
[101,] 0.009132992 0.018265983 0.9908670
[102,] 0.009398604 0.018797208 0.9906014
[103,] 0.008140130 0.016280260 0.9918599
[104,] 0.011870064 0.023740127 0.9881299
[105,] 0.011407638 0.022815277 0.9885924
[106,] 0.011289568 0.022579135 0.9887104
[107,] 0.009204579 0.018409158 0.9907954
[108,] 0.009426623 0.018853245 0.9905734
[109,] 0.007575940 0.015151880 0.9924241
[110,] 0.006792993 0.013585987 0.9932070
[111,] 0.005483287 0.010966574 0.9945167
[112,] 0.008563398 0.017126795 0.9914366
[113,] 0.007240993 0.014481986 0.9927590
[114,] 0.006397700 0.012795401 0.9936023
[115,] 0.005233171 0.010466343 0.9947668
[116,] 0.004205430 0.008410860 0.9957946
[117,] 0.003785730 0.007571460 0.9962143
[118,] 0.003027913 0.006055826 0.9969721
[119,] 0.004558690 0.009117380 0.9954413
[120,] 0.004908098 0.009816195 0.9950919
[121,] 0.010691290 0.021382579 0.9893087
[122,] 0.013071050 0.026142099 0.9869290
[123,] 0.014497555 0.028995110 0.9855024
[124,] 0.014099708 0.028199415 0.9859003
[125,] 0.011401956 0.022803912 0.9885980
[126,] 0.009607025 0.019214049 0.9903930
[127,] 0.007647105 0.015294211 0.9923529
[128,] 0.009800336 0.019600672 0.9901997
[129,] 0.008573449 0.017146897 0.9914266
[130,] 0.010083791 0.020167582 0.9899162
[131,] 0.015026572 0.030053145 0.9849734
[132,] 0.013483922 0.026967844 0.9865161
[133,] 0.010666843 0.021333686 0.9893332
[134,] 0.011414487 0.022828974 0.9885855
[135,] 0.020801296 0.041602591 0.9791987
[136,] 0.025806799 0.051613598 0.9741932
[137,] 0.027506096 0.055012191 0.9724939
[138,] 0.024740139 0.049480278 0.9752599
[139,] 0.020464415 0.040928831 0.9795356
[140,] 0.028576841 0.057153682 0.9714232
[141,] 0.024414890 0.048829779 0.9755851
[142,] 0.025473257 0.050946513 0.9745267
[143,] 0.053415388 0.106830776 0.9465846
[144,] 0.052618370 0.105236739 0.9473816
[145,] 0.060276857 0.120553713 0.9397231
[146,] 0.051223872 0.102447744 0.9487761
[147,] 0.045487516 0.090975031 0.9545125
[148,] 0.039621047 0.079242094 0.9603790
[149,] 0.038784114 0.077568229 0.9612159
[150,] 0.033042342 0.066084684 0.9669577
[151,] 0.027073533 0.054147065 0.9729265
[152,] 0.022122829 0.044245658 0.9778772
[153,] 0.018237985 0.036475970 0.9817620
[154,] 0.015337398 0.030674796 0.9846626
[155,] 0.013381293 0.026762585 0.9866187
[156,] 0.013646649 0.027293298 0.9863534
[157,] 0.010898679 0.021797358 0.9891013
[158,] 0.016384307 0.032768614 0.9836157
[159,] 0.016269772 0.032539545 0.9837302
[160,] 0.015018329 0.030036659 0.9849817
[161,] 0.014455178 0.028910357 0.9855448
[162,] 0.011626253 0.023252507 0.9883737
[163,] 0.011639719 0.023279438 0.9883603
[164,] 0.013046824 0.026093647 0.9869532
[165,] 0.017644146 0.035288293 0.9823559
[166,] 0.014085332 0.028170663 0.9859147
[167,] 0.011401198 0.022802396 0.9885988
[168,] 0.009246829 0.018493658 0.9907532
[169,] 0.007189209 0.014378418 0.9928108
[170,] 0.006258217 0.012516433 0.9937418
[171,] 0.005120990 0.010241981 0.9948790
[172,] 0.004126804 0.008253608 0.9958732
[173,] 0.004322704 0.008645407 0.9956773
[174,] 0.003398714 0.006797428 0.9966013
[175,] 0.080318366 0.160636732 0.9196816
[176,] 0.069336790 0.138673579 0.9306632
[177,] 0.079385748 0.158771497 0.9206143
[178,] 0.067945431 0.135890862 0.9320546
[179,] 0.057263957 0.114527913 0.9427360
[180,] 0.047148644 0.094297288 0.9528514
[181,] 0.041562086 0.083124173 0.9584379
[182,] 0.034891096 0.069782192 0.9651089
[183,] 0.040835165 0.081670329 0.9591648
[184,] 0.043872322 0.087744644 0.9561277
[185,] 0.037060643 0.074121286 0.9629394
[186,] 0.030794239 0.061588478 0.9692058
[187,] 0.041010393 0.082020786 0.9589896
[188,] 0.033416409 0.066832817 0.9665836
[189,] 0.030928109 0.061856218 0.9690719
[190,] 0.025993629 0.051987259 0.9740064
[191,] 0.024470766 0.048941532 0.9755292
[192,] 0.020289303 0.040578607 0.9797107
[193,] 0.026326646 0.052653292 0.9736734
[194,] 0.030494041 0.060988082 0.9695060
[195,] 0.033354332 0.066708665 0.9666457
[196,] 0.026310424 0.052620848 0.9736896
[197,] 0.025875456 0.051750911 0.9741245
[198,] 0.021127592 0.042255184 0.9788724
[199,] 0.023781297 0.047562595 0.9762187
[200,] 0.019155178 0.038310357 0.9808448
[201,] 0.021390355 0.042780709 0.9786096
[202,] 0.035062790 0.070125579 0.9649372
[203,] 0.027581124 0.055162247 0.9724189
[204,] 0.039074209 0.078148419 0.9609258
[205,] 0.033563971 0.067127941 0.9664360
[206,] 0.026107270 0.052214539 0.9738927
[207,] 0.028381664 0.056763328 0.9716183
[208,] 0.024197067 0.048394135 0.9758029
[209,] 0.021712914 0.043425829 0.9782871
[210,] 0.016563496 0.033126993 0.9834365
[211,] 0.014754569 0.029509138 0.9852454
[212,] 0.010804220 0.021608440 0.9891958
[213,] 0.008063123 0.016126245 0.9919369
[214,] 0.006484403 0.012968806 0.9935156
[215,] 0.005643196 0.011286393 0.9943568
[216,] 0.013208208 0.026416416 0.9867918
[217,] 0.010129800 0.020259600 0.9898702
[218,] 0.007288330 0.014576661 0.9927117
[219,] 0.005295989 0.010591979 0.9947040
[220,] 0.003819611 0.007639223 0.9961804
[221,] 0.003816145 0.007632290 0.9961839
[222,] 0.007214545 0.014429090 0.9927855
[223,] 0.034347231 0.068694461 0.9656528
[224,] 0.049847429 0.099694858 0.9501526
[225,] 0.037054048 0.074108097 0.9629460
[226,] 0.033035240 0.066070480 0.9669648
[227,] 0.235521128 0.471042255 0.7644789
[228,] 0.191466174 0.382932349 0.8085338
[229,] 0.171564976 0.343129952 0.8284350
[230,] 0.135054109 0.270108218 0.8649459
[231,] 0.107701908 0.215403816 0.8922981
[232,] 0.089502799 0.179005597 0.9104972
[233,] 0.083299658 0.166599316 0.9167003
[234,] 0.147134125 0.294268251 0.8528659
[235,] 0.131848104 0.263696207 0.8681519
[236,] 0.105282948 0.210565896 0.8947171
[237,] 0.074117287 0.148234574 0.9258827
[238,] 0.063719329 0.127438658 0.9362807
[239,] 0.059890794 0.119781587 0.9401092
[240,] 0.074553331 0.149106662 0.9254467
[241,] 0.044218093 0.088436186 0.9557819
[242,] 0.109195309 0.218390617 0.8908047
[243,] 0.231065314 0.462130628 0.7689347
> postscript(file="/var/wessaorg/rcomp/tmp/1qyao1356120742.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2zgah1356120742.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3p1ob1356120742.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4y9t61356120742.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ajxm1356120742.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
-0.51367519 0.65074066 2.49153369 3.77580138 -1.86974434 -1.35716285
7 8 9 10 11 12
4.07545559 -1.64945643 -1.64252779 1.14091209 1.43153210 -0.03363940
13 14 15 16 17 18
0.97830247 0.78258699 -0.69371017 -0.02035265 0.94272777 3.94765262
19 20 21 22 23 24
2.83367608 0.79651274 0.99679928 1.43126869 2.67958259 1.30471868
25 26 27 28 29 30
1.11729237 1.08094771 1.44752288 -1.40987316 0.71766048 0.36811218
31 32 33 34 35 36
-0.33713190 -0.11991942 -0.51744030 0.47420372 -1.16685224 -2.44476718
37 38 39 40 41 42
-2.25474517 -1.47793012 1.93554199 1.92020545 1.59550447 -1.41554790
43 44 45 46 47 48
2.39902917 0.20395517 -0.20949640 -4.19972513 -2.47838914 0.22717824
49 50 51 52 53 54
0.79370867 -1.41563129 -0.83738211 0.26817441 -2.61329821 0.39896812
55 56 57 58 59 60
-1.71924824 2.14834823 0.24845233 0.80241793 0.13640281 2.19001307
61 62 63 64 65 66
1.16523946 0.51653529 -0.29419148 -0.44907465 0.80068865 1.25425749
67 68 69 70 71 72
1.87547008 3.94303578 -3.87687665 0.51102385 -3.06116684 -0.71229478
73 74 75 76 77 78
1.17961245 0.75448897 0.98800871 3.86874579 -0.44523205 1.77818599
79 80 81 82 83 84
-1.89796162 0.81855447 0.48054275 0.33806579 -0.66323241 0.32879610
85 86 87 88 89 90
2.12758574 0.01643560 0.76290691 1.32945871 0.90759794 -1.60464694
91 92 93 94 95 96
0.09747899 0.27607621 0.11820853 -1.78364332 1.25758978 0.26372712
97 98 99 100 101 102
2.50298451 0.14392248 -0.24167179 -1.24133365 1.46399462 2.41950273
103 104 105 106 107 108
0.96644101 1.19539480 -1.79028375 1.19632323 0.15680323 1.70702381
109 110 111 112 113 114
-0.11946746 0.89375377 0.08802889 2.19802854 -1.59332080 -2.64382715
115 116 117 118 119 120
1.70232343 -1.97008850 1.01107611 -1.79167716 0.47816382 -1.39535417
121 122 123 124 125 126
0.61248945 -2.91031165 -1.10785362 -1.03579198 -0.89255984 0.21479165
127 128 129 130 131 132
1.12531009 1.02815182 -2.76417060 2.10626265 -3.43598107 2.45715123
133 134 135 136 137 138
-2.07857088 -1.71762907 -0.10026518 1.13813364 0.44296163 -2.35240262
139 140 141 142 143 144
-1.02626523 -2.25734464 3.10818884 1.49563660 0.04990644 1.79459152
145 146 147 148 149 150
-3.34988852 2.29631581 -2.10652551 1.09110427 0.35571092 -2.88662297
151 152 153 154 155 156
-1.22645136 2.00172466 4.29310456 1.70698715 -2.28377116 0.09747899
157 158 159 160 161 162
1.45573603 1.02815182 1.33528523 -0.51478479 0.40439833 0.59805555
163 164 165 166 167 168
-0.35639148 0.40322647 1.33905668 -1.34073587 -0.42203955 -3.22446687
169 170 171 172 173 174
-1.82380268 1.65031807 1.57396110 0.27005618 -2.06056898 -2.29704595
175 176 177 178 179 180
-3.32936982 0.27491285 -0.30384093 -0.65478959 -0.05413383 -1.51663920
181 182 183 184 185 186
0.50102858 -0.55793084 1.86024817 -0.56601817 -6.62063697 1.06635465
187 188 189 190 191 192
2.22765776 -0.57641836 -0.51218501 0.72085900 -1.10924505 0.10495701
193 194 195 196 197 198
2.36378505 1.67602248 -0.90046852 -0.40583523 2.91631036 0.73075078
199 200 201 202 203 204
1.50503832 1.31301814 1.83448685 0.52269613 -2.77230620 -2.96059542
205 206 207 208 209 210
2.03512915 0.16548627 1.17020935 0.51491673 -2.94686906 0.71201320
211 212 213 214 215 216
-2.52683924 -4.33018434 0.75610199 2.88397152 1.09519970 0.61012003
217 218 219 220 221 222
1.96866056 -0.36585737 0.97709465 -0.61039575 -2.03796100 -0.65222545
223 224 225 226 227 228
0.43877189 -1.43930625 0.12600582 -3.93435462 -0.32313878 0.70829097
229 230 231 232 233 234
-1.34402431 -1.16721875 1.50807626 -3.45379528 4.28788589 1.46380636
235 236 237 238 239 240
-1.32021870 -2.69101224 -7.19285806 -1.90094972 1.44360153 -1.97382847
241 242 243 244 245 246
-0.31042625 -1.83902347 1.14742499 1.70218327 0.88897222 0.01313581
247 248 249 250 251 252
0.86353090 -2.95692437 1.31624199 0.19103002 -1.10839460 -1.83827116
253 254 255 256 257 258
0.30998519 2.82611475 -1.75427496 -0.27712369 1.32160328 2.19187456
259 260 261 262 263 264
-1.82688230 -5.48489291 0.86985104 -4.55911713 -0.54089070 0.87771775
> postscript(file="/var/wessaorg/rcomp/tmp/6drh91356120742.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 -0.51367519 NA
1 0.65074066 -0.51367519
2 2.49153369 0.65074066
3 3.77580138 2.49153369
4 -1.86974434 3.77580138
5 -1.35716285 -1.86974434
6 4.07545559 -1.35716285
7 -1.64945643 4.07545559
8 -1.64252779 -1.64945643
9 1.14091209 -1.64252779
10 1.43153210 1.14091209
11 -0.03363940 1.43153210
12 0.97830247 -0.03363940
13 0.78258699 0.97830247
14 -0.69371017 0.78258699
15 -0.02035265 -0.69371017
16 0.94272777 -0.02035265
17 3.94765262 0.94272777
18 2.83367608 3.94765262
19 0.79651274 2.83367608
20 0.99679928 0.79651274
21 1.43126869 0.99679928
22 2.67958259 1.43126869
23 1.30471868 2.67958259
24 1.11729237 1.30471868
25 1.08094771 1.11729237
26 1.44752288 1.08094771
27 -1.40987316 1.44752288
28 0.71766048 -1.40987316
29 0.36811218 0.71766048
30 -0.33713190 0.36811218
31 -0.11991942 -0.33713190
32 -0.51744030 -0.11991942
33 0.47420372 -0.51744030
34 -1.16685224 0.47420372
35 -2.44476718 -1.16685224
36 -2.25474517 -2.44476718
37 -1.47793012 -2.25474517
38 1.93554199 -1.47793012
39 1.92020545 1.93554199
40 1.59550447 1.92020545
41 -1.41554790 1.59550447
42 2.39902917 -1.41554790
43 0.20395517 2.39902917
44 -0.20949640 0.20395517
45 -4.19972513 -0.20949640
46 -2.47838914 -4.19972513
47 0.22717824 -2.47838914
48 0.79370867 0.22717824
49 -1.41563129 0.79370867
50 -0.83738211 -1.41563129
51 0.26817441 -0.83738211
52 -2.61329821 0.26817441
53 0.39896812 -2.61329821
54 -1.71924824 0.39896812
55 2.14834823 -1.71924824
56 0.24845233 2.14834823
57 0.80241793 0.24845233
58 0.13640281 0.80241793
59 2.19001307 0.13640281
60 1.16523946 2.19001307
61 0.51653529 1.16523946
62 -0.29419148 0.51653529
63 -0.44907465 -0.29419148
64 0.80068865 -0.44907465
65 1.25425749 0.80068865
66 1.87547008 1.25425749
67 3.94303578 1.87547008
68 -3.87687665 3.94303578
69 0.51102385 -3.87687665
70 -3.06116684 0.51102385
71 -0.71229478 -3.06116684
72 1.17961245 -0.71229478
73 0.75448897 1.17961245
74 0.98800871 0.75448897
75 3.86874579 0.98800871
76 -0.44523205 3.86874579
77 1.77818599 -0.44523205
78 -1.89796162 1.77818599
79 0.81855447 -1.89796162
80 0.48054275 0.81855447
81 0.33806579 0.48054275
82 -0.66323241 0.33806579
83 0.32879610 -0.66323241
84 2.12758574 0.32879610
85 0.01643560 2.12758574
86 0.76290691 0.01643560
87 1.32945871 0.76290691
88 0.90759794 1.32945871
89 -1.60464694 0.90759794
90 0.09747899 -1.60464694
91 0.27607621 0.09747899
92 0.11820853 0.27607621
93 -1.78364332 0.11820853
94 1.25758978 -1.78364332
95 0.26372712 1.25758978
96 2.50298451 0.26372712
97 0.14392248 2.50298451
98 -0.24167179 0.14392248
99 -1.24133365 -0.24167179
100 1.46399462 -1.24133365
101 2.41950273 1.46399462
102 0.96644101 2.41950273
103 1.19539480 0.96644101
104 -1.79028375 1.19539480
105 1.19632323 -1.79028375
106 0.15680323 1.19632323
107 1.70702381 0.15680323
108 -0.11946746 1.70702381
109 0.89375377 -0.11946746
110 0.08802889 0.89375377
111 2.19802854 0.08802889
112 -1.59332080 2.19802854
113 -2.64382715 -1.59332080
114 1.70232343 -2.64382715
115 -1.97008850 1.70232343
116 1.01107611 -1.97008850
117 -1.79167716 1.01107611
118 0.47816382 -1.79167716
119 -1.39535417 0.47816382
120 0.61248945 -1.39535417
121 -2.91031165 0.61248945
122 -1.10785362 -2.91031165
123 -1.03579198 -1.10785362
124 -0.89255984 -1.03579198
125 0.21479165 -0.89255984
126 1.12531009 0.21479165
127 1.02815182 1.12531009
128 -2.76417060 1.02815182
129 2.10626265 -2.76417060
130 -3.43598107 2.10626265
131 2.45715123 -3.43598107
132 -2.07857088 2.45715123
133 -1.71762907 -2.07857088
134 -0.10026518 -1.71762907
135 1.13813364 -0.10026518
136 0.44296163 1.13813364
137 -2.35240262 0.44296163
138 -1.02626523 -2.35240262
139 -2.25734464 -1.02626523
140 3.10818884 -2.25734464
141 1.49563660 3.10818884
142 0.04990644 1.49563660
143 1.79459152 0.04990644
144 -3.34988852 1.79459152
145 2.29631581 -3.34988852
146 -2.10652551 2.29631581
147 1.09110427 -2.10652551
148 0.35571092 1.09110427
149 -2.88662297 0.35571092
150 -1.22645136 -2.88662297
151 2.00172466 -1.22645136
152 4.29310456 2.00172466
153 1.70698715 4.29310456
154 -2.28377116 1.70698715
155 0.09747899 -2.28377116
156 1.45573603 0.09747899
157 1.02815182 1.45573603
158 1.33528523 1.02815182
159 -0.51478479 1.33528523
160 0.40439833 -0.51478479
161 0.59805555 0.40439833
162 -0.35639148 0.59805555
163 0.40322647 -0.35639148
164 1.33905668 0.40322647
165 -1.34073587 1.33905668
166 -0.42203955 -1.34073587
167 -3.22446687 -0.42203955
168 -1.82380268 -3.22446687
169 1.65031807 -1.82380268
170 1.57396110 1.65031807
171 0.27005618 1.57396110
172 -2.06056898 0.27005618
173 -2.29704595 -2.06056898
174 -3.32936982 -2.29704595
175 0.27491285 -3.32936982
176 -0.30384093 0.27491285
177 -0.65478959 -0.30384093
178 -0.05413383 -0.65478959
179 -1.51663920 -0.05413383
180 0.50102858 -1.51663920
181 -0.55793084 0.50102858
182 1.86024817 -0.55793084
183 -0.56601817 1.86024817
184 -6.62063697 -0.56601817
185 1.06635465 -6.62063697
186 2.22765776 1.06635465
187 -0.57641836 2.22765776
188 -0.51218501 -0.57641836
189 0.72085900 -0.51218501
190 -1.10924505 0.72085900
191 0.10495701 -1.10924505
192 2.36378505 0.10495701
193 1.67602248 2.36378505
194 -0.90046852 1.67602248
195 -0.40583523 -0.90046852
196 2.91631036 -0.40583523
197 0.73075078 2.91631036
198 1.50503832 0.73075078
199 1.31301814 1.50503832
200 1.83448685 1.31301814
201 0.52269613 1.83448685
202 -2.77230620 0.52269613
203 -2.96059542 -2.77230620
204 2.03512915 -2.96059542
205 0.16548627 2.03512915
206 1.17020935 0.16548627
207 0.51491673 1.17020935
208 -2.94686906 0.51491673
209 0.71201320 -2.94686906
210 -2.52683924 0.71201320
211 -4.33018434 -2.52683924
212 0.75610199 -4.33018434
213 2.88397152 0.75610199
214 1.09519970 2.88397152
215 0.61012003 1.09519970
216 1.96866056 0.61012003
217 -0.36585737 1.96866056
218 0.97709465 -0.36585737
219 -0.61039575 0.97709465
220 -2.03796100 -0.61039575
221 -0.65222545 -2.03796100
222 0.43877189 -0.65222545
223 -1.43930625 0.43877189
224 0.12600582 -1.43930625
225 -3.93435462 0.12600582
226 -0.32313878 -3.93435462
227 0.70829097 -0.32313878
228 -1.34402431 0.70829097
229 -1.16721875 -1.34402431
230 1.50807626 -1.16721875
231 -3.45379528 1.50807626
232 4.28788589 -3.45379528
233 1.46380636 4.28788589
234 -1.32021870 1.46380636
235 -2.69101224 -1.32021870
236 -7.19285806 -2.69101224
237 -1.90094972 -7.19285806
238 1.44360153 -1.90094972
239 -1.97382847 1.44360153
240 -0.31042625 -1.97382847
241 -1.83902347 -0.31042625
242 1.14742499 -1.83902347
243 1.70218327 1.14742499
244 0.88897222 1.70218327
245 0.01313581 0.88897222
246 0.86353090 0.01313581
247 -2.95692437 0.86353090
248 1.31624199 -2.95692437
249 0.19103002 1.31624199
250 -1.10839460 0.19103002
251 -1.83827116 -1.10839460
252 0.30998519 -1.83827116
253 2.82611475 0.30998519
254 -1.75427496 2.82611475
255 -0.27712369 -1.75427496
256 1.32160328 -0.27712369
257 2.19187456 1.32160328
258 -1.82688230 2.19187456
259 -5.48489291 -1.82688230
260 0.86985104 -5.48489291
261 -4.55911713 0.86985104
262 -0.54089070 -4.55911713
263 0.87771775 -0.54089070
264 NA 0.87771775
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.65074066 -0.51367519
[2,] 2.49153369 0.65074066
[3,] 3.77580138 2.49153369
[4,] -1.86974434 3.77580138
[5,] -1.35716285 -1.86974434
[6,] 4.07545559 -1.35716285
[7,] -1.64945643 4.07545559
[8,] -1.64252779 -1.64945643
[9,] 1.14091209 -1.64252779
[10,] 1.43153210 1.14091209
[11,] -0.03363940 1.43153210
[12,] 0.97830247 -0.03363940
[13,] 0.78258699 0.97830247
[14,] -0.69371017 0.78258699
[15,] -0.02035265 -0.69371017
[16,] 0.94272777 -0.02035265
[17,] 3.94765262 0.94272777
[18,] 2.83367608 3.94765262
[19,] 0.79651274 2.83367608
[20,] 0.99679928 0.79651274
[21,] 1.43126869 0.99679928
[22,] 2.67958259 1.43126869
[23,] 1.30471868 2.67958259
[24,] 1.11729237 1.30471868
[25,] 1.08094771 1.11729237
[26,] 1.44752288 1.08094771
[27,] -1.40987316 1.44752288
[28,] 0.71766048 -1.40987316
[29,] 0.36811218 0.71766048
[30,] -0.33713190 0.36811218
[31,] -0.11991942 -0.33713190
[32,] -0.51744030 -0.11991942
[33,] 0.47420372 -0.51744030
[34,] -1.16685224 0.47420372
[35,] -2.44476718 -1.16685224
[36,] -2.25474517 -2.44476718
[37,] -1.47793012 -2.25474517
[38,] 1.93554199 -1.47793012
[39,] 1.92020545 1.93554199
[40,] 1.59550447 1.92020545
[41,] -1.41554790 1.59550447
[42,] 2.39902917 -1.41554790
[43,] 0.20395517 2.39902917
[44,] -0.20949640 0.20395517
[45,] -4.19972513 -0.20949640
[46,] -2.47838914 -4.19972513
[47,] 0.22717824 -2.47838914
[48,] 0.79370867 0.22717824
[49,] -1.41563129 0.79370867
[50,] -0.83738211 -1.41563129
[51,] 0.26817441 -0.83738211
[52,] -2.61329821 0.26817441
[53,] 0.39896812 -2.61329821
[54,] -1.71924824 0.39896812
[55,] 2.14834823 -1.71924824
[56,] 0.24845233 2.14834823
[57,] 0.80241793 0.24845233
[58,] 0.13640281 0.80241793
[59,] 2.19001307 0.13640281
[60,] 1.16523946 2.19001307
[61,] 0.51653529 1.16523946
[62,] -0.29419148 0.51653529
[63,] -0.44907465 -0.29419148
[64,] 0.80068865 -0.44907465
[65,] 1.25425749 0.80068865
[66,] 1.87547008 1.25425749
[67,] 3.94303578 1.87547008
[68,] -3.87687665 3.94303578
[69,] 0.51102385 -3.87687665
[70,] -3.06116684 0.51102385
[71,] -0.71229478 -3.06116684
[72,] 1.17961245 -0.71229478
[73,] 0.75448897 1.17961245
[74,] 0.98800871 0.75448897
[75,] 3.86874579 0.98800871
[76,] -0.44523205 3.86874579
[77,] 1.77818599 -0.44523205
[78,] -1.89796162 1.77818599
[79,] 0.81855447 -1.89796162
[80,] 0.48054275 0.81855447
[81,] 0.33806579 0.48054275
[82,] -0.66323241 0.33806579
[83,] 0.32879610 -0.66323241
[84,] 2.12758574 0.32879610
[85,] 0.01643560 2.12758574
[86,] 0.76290691 0.01643560
[87,] 1.32945871 0.76290691
[88,] 0.90759794 1.32945871
[89,] -1.60464694 0.90759794
[90,] 0.09747899 -1.60464694
[91,] 0.27607621 0.09747899
[92,] 0.11820853 0.27607621
[93,] -1.78364332 0.11820853
[94,] 1.25758978 -1.78364332
[95,] 0.26372712 1.25758978
[96,] 2.50298451 0.26372712
[97,] 0.14392248 2.50298451
[98,] -0.24167179 0.14392248
[99,] -1.24133365 -0.24167179
[100,] 1.46399462 -1.24133365
[101,] 2.41950273 1.46399462
[102,] 0.96644101 2.41950273
[103,] 1.19539480 0.96644101
[104,] -1.79028375 1.19539480
[105,] 1.19632323 -1.79028375
[106,] 0.15680323 1.19632323
[107,] 1.70702381 0.15680323
[108,] -0.11946746 1.70702381
[109,] 0.89375377 -0.11946746
[110,] 0.08802889 0.89375377
[111,] 2.19802854 0.08802889
[112,] -1.59332080 2.19802854
[113,] -2.64382715 -1.59332080
[114,] 1.70232343 -2.64382715
[115,] -1.97008850 1.70232343
[116,] 1.01107611 -1.97008850
[117,] -1.79167716 1.01107611
[118,] 0.47816382 -1.79167716
[119,] -1.39535417 0.47816382
[120,] 0.61248945 -1.39535417
[121,] -2.91031165 0.61248945
[122,] -1.10785362 -2.91031165
[123,] -1.03579198 -1.10785362
[124,] -0.89255984 -1.03579198
[125,] 0.21479165 -0.89255984
[126,] 1.12531009 0.21479165
[127,] 1.02815182 1.12531009
[128,] -2.76417060 1.02815182
[129,] 2.10626265 -2.76417060
[130,] -3.43598107 2.10626265
[131,] 2.45715123 -3.43598107
[132,] -2.07857088 2.45715123
[133,] -1.71762907 -2.07857088
[134,] -0.10026518 -1.71762907
[135,] 1.13813364 -0.10026518
[136,] 0.44296163 1.13813364
[137,] -2.35240262 0.44296163
[138,] -1.02626523 -2.35240262
[139,] -2.25734464 -1.02626523
[140,] 3.10818884 -2.25734464
[141,] 1.49563660 3.10818884
[142,] 0.04990644 1.49563660
[143,] 1.79459152 0.04990644
[144,] -3.34988852 1.79459152
[145,] 2.29631581 -3.34988852
[146,] -2.10652551 2.29631581
[147,] 1.09110427 -2.10652551
[148,] 0.35571092 1.09110427
[149,] -2.88662297 0.35571092
[150,] -1.22645136 -2.88662297
[151,] 2.00172466 -1.22645136
[152,] 4.29310456 2.00172466
[153,] 1.70698715 4.29310456
[154,] -2.28377116 1.70698715
[155,] 0.09747899 -2.28377116
[156,] 1.45573603 0.09747899
[157,] 1.02815182 1.45573603
[158,] 1.33528523 1.02815182
[159,] -0.51478479 1.33528523
[160,] 0.40439833 -0.51478479
[161,] 0.59805555 0.40439833
[162,] -0.35639148 0.59805555
[163,] 0.40322647 -0.35639148
[164,] 1.33905668 0.40322647
[165,] -1.34073587 1.33905668
[166,] -0.42203955 -1.34073587
[167,] -3.22446687 -0.42203955
[168,] -1.82380268 -3.22446687
[169,] 1.65031807 -1.82380268
[170,] 1.57396110 1.65031807
[171,] 0.27005618 1.57396110
[172,] -2.06056898 0.27005618
[173,] -2.29704595 -2.06056898
[174,] -3.32936982 -2.29704595
[175,] 0.27491285 -3.32936982
[176,] -0.30384093 0.27491285
[177,] -0.65478959 -0.30384093
[178,] -0.05413383 -0.65478959
[179,] -1.51663920 -0.05413383
[180,] 0.50102858 -1.51663920
[181,] -0.55793084 0.50102858
[182,] 1.86024817 -0.55793084
[183,] -0.56601817 1.86024817
[184,] -6.62063697 -0.56601817
[185,] 1.06635465 -6.62063697
[186,] 2.22765776 1.06635465
[187,] -0.57641836 2.22765776
[188,] -0.51218501 -0.57641836
[189,] 0.72085900 -0.51218501
[190,] -1.10924505 0.72085900
[191,] 0.10495701 -1.10924505
[192,] 2.36378505 0.10495701
[193,] 1.67602248 2.36378505
[194,] -0.90046852 1.67602248
[195,] -0.40583523 -0.90046852
[196,] 2.91631036 -0.40583523
[197,] 0.73075078 2.91631036
[198,] 1.50503832 0.73075078
[199,] 1.31301814 1.50503832
[200,] 1.83448685 1.31301814
[201,] 0.52269613 1.83448685
[202,] -2.77230620 0.52269613
[203,] -2.96059542 -2.77230620
[204,] 2.03512915 -2.96059542
[205,] 0.16548627 2.03512915
[206,] 1.17020935 0.16548627
[207,] 0.51491673 1.17020935
[208,] -2.94686906 0.51491673
[209,] 0.71201320 -2.94686906
[210,] -2.52683924 0.71201320
[211,] -4.33018434 -2.52683924
[212,] 0.75610199 -4.33018434
[213,] 2.88397152 0.75610199
[214,] 1.09519970 2.88397152
[215,] 0.61012003 1.09519970
[216,] 1.96866056 0.61012003
[217,] -0.36585737 1.96866056
[218,] 0.97709465 -0.36585737
[219,] -0.61039575 0.97709465
[220,] -2.03796100 -0.61039575
[221,] -0.65222545 -2.03796100
[222,] 0.43877189 -0.65222545
[223,] -1.43930625 0.43877189
[224,] 0.12600582 -1.43930625
[225,] -3.93435462 0.12600582
[226,] -0.32313878 -3.93435462
[227,] 0.70829097 -0.32313878
[228,] -1.34402431 0.70829097
[229,] -1.16721875 -1.34402431
[230,] 1.50807626 -1.16721875
[231,] -3.45379528 1.50807626
[232,] 4.28788589 -3.45379528
[233,] 1.46380636 4.28788589
[234,] -1.32021870 1.46380636
[235,] -2.69101224 -1.32021870
[236,] -7.19285806 -2.69101224
[237,] -1.90094972 -7.19285806
[238,] 1.44360153 -1.90094972
[239,] -1.97382847 1.44360153
[240,] -0.31042625 -1.97382847
[241,] -1.83902347 -0.31042625
[242,] 1.14742499 -1.83902347
[243,] 1.70218327 1.14742499
[244,] 0.88897222 1.70218327
[245,] 0.01313581 0.88897222
[246,] 0.86353090 0.01313581
[247,] -2.95692437 0.86353090
[248,] 1.31624199 -2.95692437
[249,] 0.19103002 1.31624199
[250,] -1.10839460 0.19103002
[251,] -1.83827116 -1.10839460
[252,] 0.30998519 -1.83827116
[253,] 2.82611475 0.30998519
[254,] -1.75427496 2.82611475
[255,] -0.27712369 -1.75427496
[256,] 1.32160328 -0.27712369
[257,] 2.19187456 1.32160328
[258,] -1.82688230 2.19187456
[259,] -5.48489291 -1.82688230
[260,] 0.86985104 -5.48489291
[261,] -4.55911713 0.86985104
[262,] -0.54089070 -4.55911713
[263,] 0.87771775 -0.54089070
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.65074066 -0.51367519
2 2.49153369 0.65074066
3 3.77580138 2.49153369
4 -1.86974434 3.77580138
5 -1.35716285 -1.86974434
6 4.07545559 -1.35716285
7 -1.64945643 4.07545559
8 -1.64252779 -1.64945643
9 1.14091209 -1.64252779
10 1.43153210 1.14091209
11 -0.03363940 1.43153210
12 0.97830247 -0.03363940
13 0.78258699 0.97830247
14 -0.69371017 0.78258699
15 -0.02035265 -0.69371017
16 0.94272777 -0.02035265
17 3.94765262 0.94272777
18 2.83367608 3.94765262
19 0.79651274 2.83367608
20 0.99679928 0.79651274
21 1.43126869 0.99679928
22 2.67958259 1.43126869
23 1.30471868 2.67958259
24 1.11729237 1.30471868
25 1.08094771 1.11729237
26 1.44752288 1.08094771
27 -1.40987316 1.44752288
28 0.71766048 -1.40987316
29 0.36811218 0.71766048
30 -0.33713190 0.36811218
31 -0.11991942 -0.33713190
32 -0.51744030 -0.11991942
33 0.47420372 -0.51744030
34 -1.16685224 0.47420372
35 -2.44476718 -1.16685224
36 -2.25474517 -2.44476718
37 -1.47793012 -2.25474517
38 1.93554199 -1.47793012
39 1.92020545 1.93554199
40 1.59550447 1.92020545
41 -1.41554790 1.59550447
42 2.39902917 -1.41554790
43 0.20395517 2.39902917
44 -0.20949640 0.20395517
45 -4.19972513 -0.20949640
46 -2.47838914 -4.19972513
47 0.22717824 -2.47838914
48 0.79370867 0.22717824
49 -1.41563129 0.79370867
50 -0.83738211 -1.41563129
51 0.26817441 -0.83738211
52 -2.61329821 0.26817441
53 0.39896812 -2.61329821
54 -1.71924824 0.39896812
55 2.14834823 -1.71924824
56 0.24845233 2.14834823
57 0.80241793 0.24845233
58 0.13640281 0.80241793
59 2.19001307 0.13640281
60 1.16523946 2.19001307
61 0.51653529 1.16523946
62 -0.29419148 0.51653529
63 -0.44907465 -0.29419148
64 0.80068865 -0.44907465
65 1.25425749 0.80068865
66 1.87547008 1.25425749
67 3.94303578 1.87547008
68 -3.87687665 3.94303578
69 0.51102385 -3.87687665
70 -3.06116684 0.51102385
71 -0.71229478 -3.06116684
72 1.17961245 -0.71229478
73 0.75448897 1.17961245
74 0.98800871 0.75448897
75 3.86874579 0.98800871
76 -0.44523205 3.86874579
77 1.77818599 -0.44523205
78 -1.89796162 1.77818599
79 0.81855447 -1.89796162
80 0.48054275 0.81855447
81 0.33806579 0.48054275
82 -0.66323241 0.33806579
83 0.32879610 -0.66323241
84 2.12758574 0.32879610
85 0.01643560 2.12758574
86 0.76290691 0.01643560
87 1.32945871 0.76290691
88 0.90759794 1.32945871
89 -1.60464694 0.90759794
90 0.09747899 -1.60464694
91 0.27607621 0.09747899
92 0.11820853 0.27607621
93 -1.78364332 0.11820853
94 1.25758978 -1.78364332
95 0.26372712 1.25758978
96 2.50298451 0.26372712
97 0.14392248 2.50298451
98 -0.24167179 0.14392248
99 -1.24133365 -0.24167179
100 1.46399462 -1.24133365
101 2.41950273 1.46399462
102 0.96644101 2.41950273
103 1.19539480 0.96644101
104 -1.79028375 1.19539480
105 1.19632323 -1.79028375
106 0.15680323 1.19632323
107 1.70702381 0.15680323
108 -0.11946746 1.70702381
109 0.89375377 -0.11946746
110 0.08802889 0.89375377
111 2.19802854 0.08802889
112 -1.59332080 2.19802854
113 -2.64382715 -1.59332080
114 1.70232343 -2.64382715
115 -1.97008850 1.70232343
116 1.01107611 -1.97008850
117 -1.79167716 1.01107611
118 0.47816382 -1.79167716
119 -1.39535417 0.47816382
120 0.61248945 -1.39535417
121 -2.91031165 0.61248945
122 -1.10785362 -2.91031165
123 -1.03579198 -1.10785362
124 -0.89255984 -1.03579198
125 0.21479165 -0.89255984
126 1.12531009 0.21479165
127 1.02815182 1.12531009
128 -2.76417060 1.02815182
129 2.10626265 -2.76417060
130 -3.43598107 2.10626265
131 2.45715123 -3.43598107
132 -2.07857088 2.45715123
133 -1.71762907 -2.07857088
134 -0.10026518 -1.71762907
135 1.13813364 -0.10026518
136 0.44296163 1.13813364
137 -2.35240262 0.44296163
138 -1.02626523 -2.35240262
139 -2.25734464 -1.02626523
140 3.10818884 -2.25734464
141 1.49563660 3.10818884
142 0.04990644 1.49563660
143 1.79459152 0.04990644
144 -3.34988852 1.79459152
145 2.29631581 -3.34988852
146 -2.10652551 2.29631581
147 1.09110427 -2.10652551
148 0.35571092 1.09110427
149 -2.88662297 0.35571092
150 -1.22645136 -2.88662297
151 2.00172466 -1.22645136
152 4.29310456 2.00172466
153 1.70698715 4.29310456
154 -2.28377116 1.70698715
155 0.09747899 -2.28377116
156 1.45573603 0.09747899
157 1.02815182 1.45573603
158 1.33528523 1.02815182
159 -0.51478479 1.33528523
160 0.40439833 -0.51478479
161 0.59805555 0.40439833
162 -0.35639148 0.59805555
163 0.40322647 -0.35639148
164 1.33905668 0.40322647
165 -1.34073587 1.33905668
166 -0.42203955 -1.34073587
167 -3.22446687 -0.42203955
168 -1.82380268 -3.22446687
169 1.65031807 -1.82380268
170 1.57396110 1.65031807
171 0.27005618 1.57396110
172 -2.06056898 0.27005618
173 -2.29704595 -2.06056898
174 -3.32936982 -2.29704595
175 0.27491285 -3.32936982
176 -0.30384093 0.27491285
177 -0.65478959 -0.30384093
178 -0.05413383 -0.65478959
179 -1.51663920 -0.05413383
180 0.50102858 -1.51663920
181 -0.55793084 0.50102858
182 1.86024817 -0.55793084
183 -0.56601817 1.86024817
184 -6.62063697 -0.56601817
185 1.06635465 -6.62063697
186 2.22765776 1.06635465
187 -0.57641836 2.22765776
188 -0.51218501 -0.57641836
189 0.72085900 -0.51218501
190 -1.10924505 0.72085900
191 0.10495701 -1.10924505
192 2.36378505 0.10495701
193 1.67602248 2.36378505
194 -0.90046852 1.67602248
195 -0.40583523 -0.90046852
196 2.91631036 -0.40583523
197 0.73075078 2.91631036
198 1.50503832 0.73075078
199 1.31301814 1.50503832
200 1.83448685 1.31301814
201 0.52269613 1.83448685
202 -2.77230620 0.52269613
203 -2.96059542 -2.77230620
204 2.03512915 -2.96059542
205 0.16548627 2.03512915
206 1.17020935 0.16548627
207 0.51491673 1.17020935
208 -2.94686906 0.51491673
209 0.71201320 -2.94686906
210 -2.52683924 0.71201320
211 -4.33018434 -2.52683924
212 0.75610199 -4.33018434
213 2.88397152 0.75610199
214 1.09519970 2.88397152
215 0.61012003 1.09519970
216 1.96866056 0.61012003
217 -0.36585737 1.96866056
218 0.97709465 -0.36585737
219 -0.61039575 0.97709465
220 -2.03796100 -0.61039575
221 -0.65222545 -2.03796100
222 0.43877189 -0.65222545
223 -1.43930625 0.43877189
224 0.12600582 -1.43930625
225 -3.93435462 0.12600582
226 -0.32313878 -3.93435462
227 0.70829097 -0.32313878
228 -1.34402431 0.70829097
229 -1.16721875 -1.34402431
230 1.50807626 -1.16721875
231 -3.45379528 1.50807626
232 4.28788589 -3.45379528
233 1.46380636 4.28788589
234 -1.32021870 1.46380636
235 -2.69101224 -1.32021870
236 -7.19285806 -2.69101224
237 -1.90094972 -7.19285806
238 1.44360153 -1.90094972
239 -1.97382847 1.44360153
240 -0.31042625 -1.97382847
241 -1.83902347 -0.31042625
242 1.14742499 -1.83902347
243 1.70218327 1.14742499
244 0.88897222 1.70218327
245 0.01313581 0.88897222
246 0.86353090 0.01313581
247 -2.95692437 0.86353090
248 1.31624199 -2.95692437
249 0.19103002 1.31624199
250 -1.10839460 0.19103002
251 -1.83827116 -1.10839460
252 0.30998519 -1.83827116
253 2.82611475 0.30998519
254 -1.75427496 2.82611475
255 -0.27712369 -1.75427496
256 1.32160328 -0.27712369
257 2.19187456 1.32160328
258 -1.82688230 2.19187456
259 -5.48489291 -1.82688230
260 0.86985104 -5.48489291
261 -4.55911713 0.86985104
262 -0.54089070 -4.55911713
263 0.87771775 -0.54089070
> 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/741z31356120742.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8rl4u1356120742.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/91d7i1356120742.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10ykhf1356120742.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, 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/wessaorg/rcomp/tmp/11bhhc1356120742.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/wessaorg/rcomp/tmp/1218ak1356120742.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/wessaorg/rcomp/tmp/13xhbk1356120742.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/wessaorg/rcomp/tmp/14uutg1356120742.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/wessaorg/rcomp/tmp/153nwa1356120742.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/wessaorg/rcomp/tmp/16yumu1356120742.tab")
+ }
>
> try(system("convert tmp/1qyao1356120742.ps tmp/1qyao1356120742.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zgah1356120742.ps tmp/2zgah1356120742.png",intern=TRUE))
character(0)
> try(system("convert tmp/3p1ob1356120742.ps tmp/3p1ob1356120742.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y9t61356120742.ps tmp/4y9t61356120742.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ajxm1356120742.ps tmp/5ajxm1356120742.png",intern=TRUE))
character(0)
> try(system("convert tmp/6drh91356120742.ps tmp/6drh91356120742.png",intern=TRUE))
character(0)
> try(system("convert tmp/741z31356120742.ps tmp/741z31356120742.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rl4u1356120742.ps tmp/8rl4u1356120742.png",intern=TRUE))
character(0)
> try(system("convert tmp/91d7i1356120742.ps tmp/91d7i1356120742.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ykhf1356120742.ps tmp/10ykhf1356120742.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.330 1.233 13.985