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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+ ,3
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+ ,3
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+ ,43)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('month'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('month','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])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> 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 month Connected Separate Software Happiness Depression Belonging
1 13 1 41 38 12 14 12.0 53
2 16 1 39 32 11 18 11.0 83
3 19 1 30 35 15 11 14.0 66
4 15 1 31 33 6 12 12.0 67
5 14 1 34 37 13 16 21.0 76
6 13 1 35 29 10 18 12.0 78
7 19 1 39 31 12 14 22.0 53
8 15 1 34 36 14 14 11.0 80
9 14 1 36 35 12 15 10.0 74
10 15 1 37 38 9 15 13.0 76
11 16 1 38 31 10 17 10.0 79
12 16 1 36 34 12 19 8.0 54
13 16 1 38 35 12 10 15.0 67
14 16 1 39 38 11 16 14.0 54
15 17 1 33 37 15 18 10.0 87
16 15 1 32 33 12 14 14.0 58
17 15 1 36 32 10 14 14.0 75
18 20 1 38 38 12 17 11.0 88
19 18 1 39 38 11 14 10.0 64
20 16 1 32 32 12 16 13.0 57
21 16 1 32 33 11 18 9.5 66
22 16 1 31 31 12 11 14.0 68
23 19 1 39 38 13 14 12.0 54
24 16 1 37 39 11 12 14.0 56
25 17 1 39 32 12 17 11.0 86
26 17 1 41 32 13 9 9.0 80
27 16 1 36 35 10 16 11.0 76
28 15 1 33 37 14 14 15.0 69
29 16 1 33 33 12 15 14.0 78
30 14 1 34 33 10 11 13.0 67
31 15 1 31 31 12 16 9.0 80
32 12 1 27 32 8 13 15.0 54
33 14 1 37 31 10 17 10.0 71
34 16 1 34 37 12 15 11.0 84
35 14 1 34 30 12 14 13.0 74
36 10 1 32 33 7 16 8.0 71
37 10 1 29 31 9 9 20.0 63
38 14 1 36 33 12 15 12.0 71
39 16 1 29 31 10 17 10.0 76
40 16 1 35 33 10 13 10.0 69
41 16 1 37 32 10 15 9.0 74
42 14 1 34 33 12 16 14.0 75
43 20 1 38 32 15 16 8.0 54
44 14 1 35 33 10 12 14.0 52
45 14 1 38 28 10 15 11.0 69
46 11 1 37 35 12 11 13.0 68
47 14 1 38 39 13 15 9.0 65
48 15 1 33 34 11 15 11.0 75
49 16 1 36 38 11 17 15.0 74
50 14 1 38 32 12 13 11.0 75
51 16 1 32 38 14 16 10.0 72
52 14 1 32 30 10 14 14.0 67
53 12 1 32 33 12 11 18.0 63
54 16 1 34 38 13 12 14.0 62
55 9 1 32 32 5 12 11.0 63
56 14 1 37 35 6 15 14.5 76
57 16 1 39 34 12 16 13.0 74
58 16 1 29 34 12 15 9.0 67
59 15 1 37 36 11 12 10.0 73
60 16 1 35 34 10 12 15.0 70
61 12 1 30 28 7 8 20.0 53
62 16 1 38 34 12 13 12.0 77
63 16 1 34 35 14 11 12.0 80
64 14 1 31 35 11 14 14.0 52
65 16 1 34 31 12 15 13.0 54
66 17 2 35 37 13 10 11.0 80
67 18 2 36 35 14 11 17.0 66
68 18 2 30 27 11 12 12.0 73
69 12 2 39 40 12 15 13.0 63
70 16 2 35 37 12 15 14.0 69
71 10 2 38 36 8 14 13.0 67
72 14 2 31 38 11 16 15.0 54
73 18 2 34 39 14 15 13.0 81
74 18 2 38 41 14 15 10.0 69
75 16 2 34 27 12 13 11.0 84
76 17 2 39 30 9 12 19.0 80
77 16 2 37 37 13 17 13.0 70
78 16 2 34 31 11 13 17.0 69
79 13 2 28 31 12 15 13.0 77
80 16 2 37 27 12 13 9.0 54
81 16 2 33 36 12 15 11.0 79
82 16 2 35 37 12 15 9.0 71
83 15 2 37 33 12 16 12.0 73
84 15 2 32 34 11 15 12.0 72
85 16 2 33 31 10 14 13.0 77
86 14 2 38 39 9 15 13.0 75
87 16 2 33 34 12 14 12.0 69
88 16 2 29 32 12 13 15.0 54
89 15 2 33 33 12 7 22.0 70
90 12 2 31 36 9 17 13.0 73
91 17 2 36 32 15 13 15.0 54
92 16 2 35 41 12 15 13.0 77
93 15 2 32 28 12 14 15.0 82
94 13 2 29 30 12 13 12.5 80
95 16 2 39 36 10 16 11.0 80
96 16 2 37 35 13 12 16.0 69
97 16 2 35 31 9 14 11.0 78
98 16 2 37 34 12 17 11.0 81
99 14 2 32 36 10 15 10.0 76
100 16 2 38 36 14 17 10.0 76
101 16 2 37 35 11 12 16.0 73
102 20 2 36 37 15 16 12.0 85
103 15 2 32 28 11 11 11.0 66
104 16 2 33 39 11 15 16.0 79
105 13 2 40 32 12 9 19.0 68
106 17 2 38 35 12 16 11.0 76
107 16 2 41 39 12 15 16.0 71
108 16 2 36 35 11 10 15.0 54
109 12 2 43 42 7 10 24.0 46
110 16 2 30 34 12 15 14.0 85
111 16 2 31 33 14 11 15.0 74
112 17 2 32 41 11 13 11.0 88
113 13 2 32 33 11 14 15.0 38
114 12 2 37 34 10 18 12.0 76
115 18 2 37 32 13 16 10.0 86
116 14 2 33 40 13 14 14.0 54
117 14 2 34 40 8 14 13.0 67
118 13 2 33 35 11 14 9.0 69
119 16 2 38 36 12 14 15.0 90
120 13 2 33 37 11 12 15.0 54
121 16 2 31 27 13 14 14.0 76
122 13 2 38 39 12 15 11.0 89
123 16 2 37 38 14 15 8.0 76
124 15 2 36 31 13 15 11.0 73
125 16 2 31 33 15 13 11.0 79
126 15 2 39 32 10 17 8.0 90
127 17 2 44 39 11 17 10.0 74
128 15 2 33 36 9 19 11.0 81
129 12 2 35 33 11 15 13.0 72
130 16 2 32 33 10 13 11.0 71
131 10 2 28 32 11 9 20.0 66
132 16 2 40 37 8 15 10.0 77
133 12 2 27 30 11 15 15.0 65
134 14 2 37 38 12 15 12.0 74
135 15 2 32 29 12 16 14.0 85
136 13 2 28 22 9 11 23.0 54
137 15 2 34 35 11 14 14.0 63
138 11 2 30 35 10 11 16.0 54
139 12 2 35 34 8 15 11.0 64
140 11 2 31 35 9 13 12.0 69
141 16 2 32 34 8 15 10.0 54
142 15 2 30 37 9 16 14.0 84
143 17 2 30 35 15 14 12.0 86
144 16 2 31 23 11 15 12.0 77
145 10 2 40 31 8 16 11.0 89
146 18 2 32 27 13 16 12.0 76
147 13 2 36 36 12 11 13.0 60
148 16 2 32 31 12 12 11.0 75
149 13 2 35 32 9 9 19.0 73
150 10 2 38 39 7 16 12.0 85
151 15 2 42 37 13 13 17.0 79
152 16 2 34 38 9 16 9.0 71
153 16 2 35 39 6 12 12.0 72
154 14 1 38 34 8 9 19.0 69
155 10 2 33 31 8 13 18.0 78
156 17 2 36 32 15 13 15.0 54
157 13 2 32 37 6 14 14.0 69
158 15 2 33 36 9 19 11.0 81
159 16 2 34 32 11 13 9.0 84
160 12 2 32 38 8 12 18.0 84
161 13 2 34 36 8 13 16.0 69
162 13 3 27 26 10 10 24.0 66
163 12 3 31 26 8 14 14.0 81
164 17 3 38 33 14 16 20.0 82
165 15 3 34 39 10 10 18.0 72
166 10 3 24 30 8 11 23.0 54
167 14 3 30 33 11 14 12.0 78
168 11 3 26 25 12 12 14.0 74
169 13 3 34 38 12 9 16.0 82
170 16 3 27 37 12 9 18.0 73
171 12 3 37 31 5 11 20.0 55
172 16 3 36 37 12 16 12.0 72
173 12 3 41 35 10 9 12.0 78
174 9 3 29 25 7 13 17.0 59
175 12 3 36 28 12 16 13.0 72
176 15 3 32 35 11 13 9.0 78
177 12 3 37 33 8 9 16.0 68
178 12 3 30 30 9 12 18.0 69
179 14 3 31 31 10 16 10.0 67
180 12 3 38 37 9 11 14.0 74
181 16 3 36 36 12 14 11.0 54
182 11 3 35 30 6 13 9.0 67
183 19 3 31 36 15 15 11.0 70
184 15 3 38 32 12 14 10.0 80
185 8 3 22 28 12 16 11.0 89
186 16 3 32 36 12 13 19.0 76
187 17 3 36 34 11 14 14.0 74
188 12 3 39 31 7 15 12.0 87
189 11 3 28 28 7 13 14.0 54
190 11 3 32 36 5 11 21.0 61
191 14 3 32 36 12 11 13.0 38
192 16 3 38 40 12 14 10.0 75
193 12 3 32 33 3 15 15.0 69
194 16 3 35 37 11 11 16.0 62
195 13 3 32 32 10 15 14.0 72
196 15 3 37 38 12 12 12.0 70
197 16 3 34 31 9 14 19.0 79
198 16 3 33 37 12 14 15.0 87
199 14 3 33 33 9 8 19.0 62
200 16 3 26 32 12 13 13.0 77
201 16 3 30 30 12 9 17.0 69
202 14 3 24 30 10 15 12.0 69
203 11 3 34 31 9 17 11.0 75
204 12 3 34 32 12 13 14.0 54
205 15 3 33 34 8 15 11.0 72
206 15 3 34 36 11 15 13.0 74
207 16 3 35 37 11 14 12.0 85
208 16 3 35 36 12 16 15.0 52
209 11 3 36 33 10 13 14.0 70
210 15 3 34 33 10 16 12.0 84
211 12 3 34 33 12 9 17.0 64
212 12 3 41 44 12 16 11.0 84
213 15 3 32 39 11 11 18.0 87
214 15 3 30 32 8 10 13.0 79
215 16 3 35 35 12 11 17.0 67
216 14 3 28 25 10 15 13.0 65
217 17 3 33 35 11 17 11.0 85
218 14 3 39 34 10 14 12.0 83
219 13 3 36 35 8 8 22.0 61
220 15 3 36 39 12 15 14.0 82
221 13 3 35 33 12 11 12.0 76
222 14 3 38 36 10 16 12.0 58
223 15 3 33 32 12 10 17.0 72
224 12 3 31 32 9 15 9.0 72
225 13 3 34 36 9 9 21.0 38
226 8 3 32 36 6 16 10.0 78
227 14 3 31 32 10 19 11.0 54
228 14 3 33 34 9 12 12.0 63
229 11 3 34 33 9 8 23.0 66
230 12 3 34 35 9 11 13.0 70
231 13 3 34 30 6 14 12.0 71
232 10 3 33 38 10 9 16.0 67
233 16 3 32 34 6 15 9.0 58
234 18 3 41 33 14 13 17.0 72
235 13 3 34 32 10 16 9.0 72
236 11 3 36 31 10 11 14.0 70
237 4 3 37 30 6 12 17.0 76
238 13 3 36 27 12 13 13.0 50
239 16 3 29 31 12 10 11.0 72
240 10 3 37 30 7 11 12.0 72
241 12 3 27 32 8 12 10.0 88
242 12 3 35 35 11 8 19.0 53
243 10 3 28 28 3 12 16.0 58
244 13 3 35 33 6 12 16.0 66
245 15 3 37 31 10 15 14.0 82
246 12 3 29 35 8 11 20.0 69
247 14 3 32 35 9 13 15.0 68
248 10 3 36 32 9 14 23.0 44
249 12 3 19 21 8 10 20.0 56
250 12 3 21 20 9 12 16.0 53
251 11 3 31 34 7 15 14.0 70
252 10 3 33 32 7 13 17.0 78
253 12 3 36 34 6 13 11.0 71
254 16 3 33 32 9 13 13.0 72
255 12 3 37 33 10 12 17.0 68
256 14 3 34 33 11 12 15.0 67
257 16 3 35 37 12 9 21.0 75
258 14 3 31 32 8 9 18.0 62
259 13 3 37 34 11 15 15.0 67
260 4 3 35 30 3 10 8.0 83
261 15 3 27 30 11 14 12.0 64
262 11 3 34 38 12 15 12.0 68
263 11 3 40 36 7 7 22.0 62
264 14 3 29 32 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) month Connected Separate
5.11368 -0.40460 0.03478 0.04334
Software Happiness Depression Belonging
0.57606 0.07962 -0.02610 0.02829
Belonging_Final
-0.03265
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9727 -1.1695 0.2867 1.1592 4.6323
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.11368 1.96917 2.597 0.00995 **
month -0.40460 0.15952 -2.536 0.01180 *
Connected 0.03478 0.03476 1.001 0.31788
Separate 0.04334 0.03528 1.228 0.22040
Software 0.57606 0.05277 10.916 < 2e-16 ***
Happiness 0.07962 0.05784 1.376 0.16988
Depression -0.02610 0.04244 -0.615 0.53905
Belonging 0.02829 0.03777 0.749 0.45450
Belonging_Final -0.03265 0.05610 -0.582 0.56105
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.865 on 255 degrees of freedom
Multiple R-squared: 0.441, Adjusted R-squared: 0.4235
F-statistic: 25.15 on 8 and 255 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,] 0.249272273 0.498544547 0.7507277
[2,] 0.168954109 0.337908219 0.8310459
[3,] 0.188652474 0.377304949 0.8113475
[4,] 0.113822200 0.227644399 0.8861778
[5,] 0.077358179 0.154716357 0.9226418
[6,] 0.111089922 0.222179843 0.8889101
[7,] 0.350297244 0.700594488 0.6497028
[8,] 0.273076148 0.546152296 0.7269239
[9,] 0.200364780 0.400729560 0.7996352
[10,] 0.147329038 0.294658076 0.8526710
[11,] 0.142429822 0.284859644 0.8575702
[12,] 0.329613611 0.659227221 0.6703864
[13,] 0.396108111 0.792216222 0.6038919
[14,] 0.364643783 0.729287565 0.6353562
[15,] 0.343013785 0.686027571 0.6569862
[16,] 0.419522217 0.839044434 0.5804778
[17,] 0.431039270 0.862078539 0.5689607
[18,] 0.411178258 0.822356515 0.5888217
[19,] 0.444562864 0.889125729 0.5554371
[20,] 0.389182053 0.778364107 0.6108179
[21,] 0.346620753 0.693241507 0.6533792
[22,] 0.316677131 0.633354262 0.6833229
[23,] 0.275674898 0.551349797 0.7243251
[24,] 0.233139525 0.466279050 0.7668605
[25,] 0.363623900 0.727247799 0.6363761
[26,] 0.400480018 0.800960036 0.5995200
[27,] 0.392518457 0.785036914 0.6074815
[28,] 0.420646898 0.841293796 0.5793531
[29,] 0.394036669 0.788073338 0.6059633
[30,] 0.352608511 0.705217021 0.6473915
[31,] 0.317815561 0.635631122 0.6821844
[32,] 0.330602998 0.661205997 0.6693970
[33,] 0.284193040 0.568386080 0.7158070
[34,] 0.259307354 0.518614708 0.7406926
[35,] 0.457373137 0.914746273 0.5426269
[36,] 0.613015043 0.773969915 0.3869850
[37,] 0.564790965 0.870418070 0.4352090
[38,] 0.550115831 0.899768338 0.4498842
[39,] 0.538886872 0.922226256 0.4611131
[40,] 0.496029188 0.992058376 0.5039708
[41,] 0.448976552 0.897953103 0.5510234
[42,] 0.469409971 0.938819942 0.5305900
[43,] 0.449833836 0.899667672 0.5501662
[44,] 0.452066183 0.904132367 0.5479338
[45,] 0.420708590 0.841417180 0.5792914
[46,] 0.377122147 0.754244294 0.6228779
[47,] 0.343504288 0.687008576 0.6564957
[48,] 0.305381737 0.610763474 0.6946183
[49,] 0.304715665 0.609431330 0.6952843
[50,] 0.274885364 0.549770729 0.7251146
[51,] 0.243391513 0.486783027 0.7566085
[52,] 0.213205556 0.426411111 0.7867944
[53,] 0.184850255 0.369700511 0.8151497
[54,] 0.159612090 0.319224181 0.8403879
[55,] 0.135701683 0.271403365 0.8642983
[56,] 0.118078555 0.236157110 0.8819214
[57,] 0.147491712 0.294983424 0.8525083
[58,] 0.347255539 0.694511077 0.6527445
[59,] 0.309383521 0.618767042 0.6906165
[60,] 0.466018474 0.932036948 0.5339815
[61,] 0.427694883 0.855389767 0.5723051
[62,] 0.415756314 0.831512629 0.5842437
[63,] 0.394545849 0.789091698 0.6054542
[64,] 0.359444377 0.718888754 0.6405556
[65,] 0.407073803 0.814147605 0.5929262
[66,] 0.374300585 0.748601169 0.6256994
[67,] 0.346591345 0.693182689 0.6534087
[68,] 0.375124097 0.750248194 0.6248759
[69,] 0.344523837 0.689047674 0.6554762
[70,] 0.311892770 0.623785539 0.6881072
[71,] 0.277800972 0.555601945 0.7221990
[72,] 0.254136867 0.508273734 0.7458631
[73,] 0.223381796 0.446763591 0.7766182
[74,] 0.213624304 0.427248607 0.7863757
[75,] 0.186095184 0.372190367 0.8139048
[76,] 0.162784483 0.325568967 0.8372155
[77,] 0.147969314 0.295938628 0.8520307
[78,] 0.127536796 0.255073591 0.8724632
[79,] 0.129326333 0.258652666 0.8706737
[80,] 0.111535332 0.223070664 0.8884647
[81,] 0.096128398 0.192256796 0.9038716
[82,] 0.082550296 0.165100591 0.9174497
[83,] 0.087742539 0.175485078 0.9122575
[84,] 0.078087986 0.156175972 0.9219120
[85,] 0.065829406 0.131658811 0.9341706
[86,] 0.068906807 0.137813615 0.9310932
[87,] 0.057357952 0.114715903 0.9426420
[88,] 0.047590636 0.095181272 0.9524094
[89,] 0.042543858 0.085087717 0.9574561
[90,] 0.036985931 0.073971862 0.9630141
[91,] 0.042000243 0.084000487 0.9579998
[92,] 0.035665390 0.071330780 0.9643346
[93,] 0.031666520 0.063333040 0.9683335
[94,] 0.038809498 0.077618995 0.9611905
[95,] 0.033580103 0.067160206 0.9664199
[96,] 0.027290386 0.054580772 0.9727096
[97,] 0.026013415 0.052026830 0.9739866
[98,] 0.021222130 0.042444260 0.9787779
[99,] 0.017203818 0.034407637 0.9827962
[100,] 0.013632186 0.027264372 0.9863678
[101,] 0.013622757 0.027245513 0.9863772
[102,] 0.012487296 0.024974592 0.9875127
[103,] 0.019146176 0.038292352 0.9808538
[104,] 0.017761714 0.035523429 0.9822383
[105,] 0.017603646 0.035207292 0.9823964
[106,] 0.014365082 0.028730163 0.9856349
[107,] 0.014933316 0.029866632 0.9850667
[108,] 0.012069116 0.024138232 0.9879309
[109,] 0.011017215 0.022034430 0.9889828
[110,] 0.008849280 0.017698560 0.9911507
[111,] 0.014052017 0.028104034 0.9859480
[112,] 0.012043124 0.024086249 0.9879569
[113,] 0.010823044 0.021646088 0.9891770
[114,] 0.009037311 0.018074622 0.9909627
[115,] 0.007284510 0.014569021 0.9927155
[116,] 0.006399142 0.012798283 0.9936009
[117,] 0.005106591 0.010213182 0.9948934
[118,] 0.007833751 0.015667501 0.9921662
[119,] 0.008079630 0.016159261 0.9919204
[120,] 0.018094933 0.036189866 0.9819051
[121,] 0.020368964 0.040737929 0.9796310
[122,] 0.023261761 0.046523522 0.9767382
[123,] 0.023256224 0.046512448 0.9767438
[124,] 0.019036038 0.038072076 0.9809640
[125,] 0.015637249 0.031274498 0.9843628
[126,] 0.012443014 0.024886029 0.9875570
[127,] 0.016577269 0.033154538 0.9834227
[128,] 0.014924063 0.029848125 0.9850759
[129,] 0.019031155 0.038062311 0.9809688
[130,] 0.026112930 0.052225859 0.9738871
[131,] 0.023296615 0.046593229 0.9767034
[132,] 0.019013388 0.038026776 0.9809866
[133,] 0.018077456 0.036154912 0.9819225
[134,] 0.035502616 0.071005232 0.9644974
[135,] 0.038422197 0.076844393 0.9615778
[136,] 0.043198752 0.086397504 0.9568012
[137,] 0.036964585 0.073929170 0.9630354
[138,] 0.030326830 0.060653660 0.9696732
[139,] 0.045729337 0.091458674 0.9542707
[140,] 0.041381104 0.082762209 0.9586189
[141,] 0.041087092 0.082174183 0.9589129
[142,] 0.071708180 0.143416360 0.9282918
[143,] 0.063159712 0.126319425 0.9368403
[144,] 0.077112812 0.154225624 0.9228872
[145,] 0.064814840 0.129629680 0.9351852
[146,] 0.056421290 0.112842581 0.9435787
[147,] 0.047850295 0.095700591 0.9521497
[148,] 0.045457012 0.090914024 0.9545430
[149,] 0.039035534 0.078071068 0.9609645
[150,] 0.031868811 0.063737623 0.9681312
[151,] 0.026187933 0.052375865 0.9738121
[152,] 0.021581342 0.043162684 0.9784187
[153,] 0.018252422 0.036504845 0.9817476
[154,] 0.016138330 0.032276661 0.9838617
[155,] 0.016251666 0.032503332 0.9837483
[156,] 0.012956801 0.025913601 0.9870432
[157,] 0.018479417 0.036958835 0.9815206
[158,] 0.017894260 0.035788519 0.9821057
[159,] 0.017018918 0.034037836 0.9829811
[160,] 0.016561667 0.033123335 0.9834383
[161,] 0.013436048 0.026872095 0.9865640
[162,] 0.012958356 0.025916711 0.9870416
[163,] 0.013877447 0.027754894 0.9861226
[164,] 0.017240763 0.034481526 0.9827592
[165,] 0.013895174 0.027790348 0.9861048
[166,] 0.011051999 0.022103998 0.9889480
[167,] 0.008870743 0.017741487 0.9911293
[168,] 0.006883216 0.013766432 0.9931168
[169,] 0.005774436 0.011548871 0.9942256
[170,] 0.004853936 0.009707871 0.9951461
[171,] 0.003788799 0.007577598 0.9962112
[172,] 0.004241499 0.008482999 0.9957585
[173,] 0.003270547 0.006541094 0.9967295
[174,] 0.072840711 0.145681421 0.9271593
[175,] 0.063836939 0.127673878 0.9361631
[176,] 0.075551377 0.151102755 0.9244486
[177,] 0.064037139 0.128074277 0.9359629
[178,] 0.053654156 0.107308313 0.9463458
[179,] 0.044308331 0.088616662 0.9556917
[180,] 0.038733534 0.077467069 0.9612665
[181,] 0.032480446 0.064960891 0.9675196
[182,] 0.038777019 0.077554038 0.9612230
[183,] 0.042580372 0.085160744 0.9574196
[184,] 0.035541480 0.071082959 0.9644585
[185,] 0.029143015 0.058286030 0.9708570
[186,] 0.039343221 0.078686442 0.9606568
[187,] 0.032175175 0.064350349 0.9678248
[188,] 0.029969169 0.059938339 0.9700308
[189,] 0.025468536 0.050937072 0.9745315
[190,] 0.024209926 0.048419852 0.9757901
[191,] 0.020183007 0.040366014 0.9798170
[192,] 0.025527130 0.051054260 0.9744729
[193,] 0.028059869 0.056119738 0.9719401
[194,] 0.031222434 0.062444868 0.9687776
[195,] 0.024571621 0.049143242 0.9754284
[196,] 0.024269913 0.048539826 0.9757301
[197,] 0.019969288 0.039938576 0.9800307
[198,] 0.021672832 0.043345664 0.9783272
[199,] 0.017456347 0.034912694 0.9825437
[200,] 0.018929280 0.037858560 0.9810707
[201,] 0.029270319 0.058540638 0.9707297
[202,] 0.022869354 0.045738709 0.9771306
[203,] 0.033024021 0.066048042 0.9669760
[204,] 0.028456855 0.056913710 0.9715431
[205,] 0.022084984 0.044169968 0.9779150
[206,] 0.024359112 0.048718225 0.9756409
[207,] 0.020489589 0.040979178 0.9795104
[208,] 0.018320551 0.036641101 0.9816794
[209,] 0.013754798 0.027509597 0.9862452
[210,] 0.011927236 0.023854471 0.9880728
[211,] 0.008581221 0.017162442 0.9914188
[212,] 0.006346574 0.012693148 0.9936534
[213,] 0.004964717 0.009929434 0.9950353
[214,] 0.004294646 0.008589292 0.9957054
[215,] 0.009946292 0.019892583 0.9900537
[216,] 0.007534384 0.015068767 0.9924656
[217,] 0.005373386 0.010746773 0.9946266
[218,] 0.003807178 0.007614357 0.9961928
[219,] 0.002664712 0.005329424 0.9973353
[220,] 0.002671582 0.005343163 0.9973284
[221,] 0.004943364 0.009886728 0.9950566
[222,] 0.025946735 0.051893471 0.9740533
[223,] 0.038457941 0.076915881 0.9615421
[224,] 0.027718788 0.055437577 0.9722812
[225,] 0.023961814 0.047923628 0.9760382
[226,] 0.187771175 0.375542351 0.8122288
[227,] 0.147044861 0.294089722 0.8529551
[228,] 0.130216166 0.260432332 0.8697838
[229,] 0.098868434 0.197736868 0.9011316
[230,] 0.076600142 0.153200283 0.9233999
[231,] 0.061463418 0.122926836 0.9385366
[232,] 0.056209776 0.112419552 0.9437902
[233,] 0.103562676 0.207125352 0.8964373
[234,] 0.090783773 0.181567545 0.9092162
[235,] 0.069638293 0.139276586 0.9303617
[236,] 0.046529644 0.093059287 0.9534704
[237,] 0.037806000 0.075612000 0.9621940
[238,] 0.034074745 0.068149490 0.9659253
[239,] 0.041448869 0.082897738 0.9585511
[240,] 0.021822448 0.043644896 0.9781776
[241,] 0.054742676 0.109485353 0.9452573
> postscript(file="/var/wessaorg/rcomp/tmp/13e0s1353952026.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/2tqcr1353952026.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3s9r91353952026.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4p9xx1353952026.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5rmxg1353952026.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-2.950927427 0.381817133 2.083335628 3.127025193 -2.358051988 -1.736016272
7 8 9 10 11 12
3.846299991 -2.007756051 -1.948445497 0.701963529 1.137382762 -0.101807701
13 14 15 16 17 18
0.610689034 0.592021392 -1.004395063 -0.379815924 0.489441182 3.616619183
19 20 21 22 23 24
2.625165134 0.506467834 0.697230535 0.958823175 2.546938147 1.010762105
25 26 27 28 29 30
0.833157851 0.778791226 1.028275393 -1.763968014 0.233833016 -0.077895767
31 32 33 34 35 36
-0.746027284 -0.737377122 -0.797418619 0.071499880 -1.602200306 -2.921926401
37 38 39 40 41 42
-3.014672990 -1.789991690 1.437350999 1.527886673 1.272815400 -1.828353335
43 44 45 46 47 48
2.425987887 -0.166310878 -0.492903544 -4.579946776 -2.604228986 -0.194229434
49 50 51 52 53 54
0.370906087 -1.730933528 -1.147151290 -0.221677409 -3.145340373 0.130447599
55 56 57 58 59 60
-2.364552178 1.599325081 -0.010771712 0.412355641 -0.248651715 1.699039550
61 62 63 64 65 66
0.431959429 0.151906615 -0.732096822 -0.849165795 0.546781501 1.213271705
67 68 69 70 71 72
1.933602545 3.776399158 -3.703958865 0.552161863 -3.029195508 -0.699009795
73 74 75 76 77 78
1.178461283 0.952614163 0.938146133 3.731342200 -0.274453484 1.725904951
79 80 81 82 83 84
-2.033478554 1.075230233 0.434465248 0.463024793 -0.556378965 0.290823812
85 86 87 88 89 90
2.024351240 -0.008573324 0.779169251 1.293406531 0.645248973 -1.705080737
91 92 93 94 95 96
0.321735886 0.256979711 -0.019563985 -1.963598029 1.302627336 0.316937043
97 98 99 100 101 102
2.417701080 0.231489430 -0.319858082 -1.024703834 1.421202664 2.531232510
103 104 105 106 107 108
0.947695374 1.043666722 -1.801167422 1.341788163 0.285054577 1.734830472
109 110 111 112 113 114
-0.144502540 0.729968270 -0.019022897 2.027100876 -1.623526260 -2.561106515
115 116 117 118 119 120
1.817433595 -1.874239203 0.936575639 -1.766415230 0.427246196 -1.406745685
121 122 123 124 125 126
0.560835235 -2.891167271 -0.904258187 -0.892153489 -0.902236171 0.296367937
127 128 129 130 131 132
1.388689572 1.016139203 -2.744083838 2.006357737 -3.757672592 2.482367886
133 134 135 136 137 138
-2.216163118 -1.623796371 -0.169866756 0.857844295 0.499053080 -2.468624925
139 140 141 142 143 144
-1.015724374 -2.419446534 3.116868495 1.244149429 -0.009779507 1.726205342
145 146 147 148 149 150
-3.356731563 2.347253794 -2.022884772 1.037980818 0.122740838 -2.983866911
151 152 153 154 155 156
-1.149480124 1.972426862 4.088948985 1.118379703 -2.511077298 0.321735886
157 158 159 160 161 162
1.225159115 1.016139203 1.277952243 -0.902448709 0.276588644 0.640555249
163 164 165 166 167 168
-0.219714058 0.844074307 1.507288989 -1.369085649 -0.151183747 -3.047381403
169 170 171 172 173 174
-1.628420757 1.671359890 1.789630485 0.770582649 -1.678958916 -2.142243663
175 176 177 178 179 180
-2.813249493 0.563279913 -0.044313330 -0.527125898 0.347961979 -1.242363629
181 182 183 184 185 186
1.097128568 -0.230816591 2.369755033 -0.070957371 -6.500257228 1.261477725
187 188 189 190 191 192
2.598884232 -0.276935001 -0.337592691 0.766460632 -0.738347363 0.593164124
193 194 195 196 197 198
2.445135861 2.068898527 -0.589629361 0.067529462 3.102911981 0.981990529
199 200 201 202 203 204
1.650521446 1.458638605 2.055406796 0.677381539 -2.427517795 -2.436710987
205 206 207 208 209 210
2.330073087 0.541347575 1.532062754 1.068353080 -2.556278024 1.022060198
211 212 213 214 215 216
-2.202902206 -3.778443865 0.855979821 2.969296732 1.431517041 0.926865366
217 218 219 220 221 222
2.325387625 0.057640177 1.109497163 -0.205965436 -1.671017171 -0.131920566
223 224 225 226 227 228
0.699881753 -1.109293774 0.288333593 -3.747213134 0.198419826 0.979732572
229 230 231 232 233 234
-1.229759183 -0.896838278 1.754791518 -3.213065762 4.632282599 2.052583830
235 236 237 238 239 240
-0.869328864 -2.343002739 -6.972696730 -1.267817848 1.693092913 -1.682392341
241 242 243 244 245 246
-0.287911980 -1.501192112 1.148577467 1.897137210 1.290751900 0.031494268
247 248 249 250 251 252
1.122268014 -2.535800023 1.237586914 0.260609056 -0.922062430 -1.730463173
253 254 255 256 257 258
0.598033801 3.117443737 -1.376552283 0.095170915 1.480120220 2.331720115
259 260 261 262 263 264
-1.356690157 -5.383345806 1.152740672 -4.108360954 -0.347629194 1.085550529
> postscript(file="/var/wessaorg/rcomp/tmp/6w8ck1353952026.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 -2.950927427 NA
1 0.381817133 -2.950927427
2 2.083335628 0.381817133
3 3.127025193 2.083335628
4 -2.358051988 3.127025193
5 -1.736016272 -2.358051988
6 3.846299991 -1.736016272
7 -2.007756051 3.846299991
8 -1.948445497 -2.007756051
9 0.701963529 -1.948445497
10 1.137382762 0.701963529
11 -0.101807701 1.137382762
12 0.610689034 -0.101807701
13 0.592021392 0.610689034
14 -1.004395063 0.592021392
15 -0.379815924 -1.004395063
16 0.489441182 -0.379815924
17 3.616619183 0.489441182
18 2.625165134 3.616619183
19 0.506467834 2.625165134
20 0.697230535 0.506467834
21 0.958823175 0.697230535
22 2.546938147 0.958823175
23 1.010762105 2.546938147
24 0.833157851 1.010762105
25 0.778791226 0.833157851
26 1.028275393 0.778791226
27 -1.763968014 1.028275393
28 0.233833016 -1.763968014
29 -0.077895767 0.233833016
30 -0.746027284 -0.077895767
31 -0.737377122 -0.746027284
32 -0.797418619 -0.737377122
33 0.071499880 -0.797418619
34 -1.602200306 0.071499880
35 -2.921926401 -1.602200306
36 -3.014672990 -2.921926401
37 -1.789991690 -3.014672990
38 1.437350999 -1.789991690
39 1.527886673 1.437350999
40 1.272815400 1.527886673
41 -1.828353335 1.272815400
42 2.425987887 -1.828353335
43 -0.166310878 2.425987887
44 -0.492903544 -0.166310878
45 -4.579946776 -0.492903544
46 -2.604228986 -4.579946776
47 -0.194229434 -2.604228986
48 0.370906087 -0.194229434
49 -1.730933528 0.370906087
50 -1.147151290 -1.730933528
51 -0.221677409 -1.147151290
52 -3.145340373 -0.221677409
53 0.130447599 -3.145340373
54 -2.364552178 0.130447599
55 1.599325081 -2.364552178
56 -0.010771712 1.599325081
57 0.412355641 -0.010771712
58 -0.248651715 0.412355641
59 1.699039550 -0.248651715
60 0.431959429 1.699039550
61 0.151906615 0.431959429
62 -0.732096822 0.151906615
63 -0.849165795 -0.732096822
64 0.546781501 -0.849165795
65 1.213271705 0.546781501
66 1.933602545 1.213271705
67 3.776399158 1.933602545
68 -3.703958865 3.776399158
69 0.552161863 -3.703958865
70 -3.029195508 0.552161863
71 -0.699009795 -3.029195508
72 1.178461283 -0.699009795
73 0.952614163 1.178461283
74 0.938146133 0.952614163
75 3.731342200 0.938146133
76 -0.274453484 3.731342200
77 1.725904951 -0.274453484
78 -2.033478554 1.725904951
79 1.075230233 -2.033478554
80 0.434465248 1.075230233
81 0.463024793 0.434465248
82 -0.556378965 0.463024793
83 0.290823812 -0.556378965
84 2.024351240 0.290823812
85 -0.008573324 2.024351240
86 0.779169251 -0.008573324
87 1.293406531 0.779169251
88 0.645248973 1.293406531
89 -1.705080737 0.645248973
90 0.321735886 -1.705080737
91 0.256979711 0.321735886
92 -0.019563985 0.256979711
93 -1.963598029 -0.019563985
94 1.302627336 -1.963598029
95 0.316937043 1.302627336
96 2.417701080 0.316937043
97 0.231489430 2.417701080
98 -0.319858082 0.231489430
99 -1.024703834 -0.319858082
100 1.421202664 -1.024703834
101 2.531232510 1.421202664
102 0.947695374 2.531232510
103 1.043666722 0.947695374
104 -1.801167422 1.043666722
105 1.341788163 -1.801167422
106 0.285054577 1.341788163
107 1.734830472 0.285054577
108 -0.144502540 1.734830472
109 0.729968270 -0.144502540
110 -0.019022897 0.729968270
111 2.027100876 -0.019022897
112 -1.623526260 2.027100876
113 -2.561106515 -1.623526260
114 1.817433595 -2.561106515
115 -1.874239203 1.817433595
116 0.936575639 -1.874239203
117 -1.766415230 0.936575639
118 0.427246196 -1.766415230
119 -1.406745685 0.427246196
120 0.560835235 -1.406745685
121 -2.891167271 0.560835235
122 -0.904258187 -2.891167271
123 -0.892153489 -0.904258187
124 -0.902236171 -0.892153489
125 0.296367937 -0.902236171
126 1.388689572 0.296367937
127 1.016139203 1.388689572
128 -2.744083838 1.016139203
129 2.006357737 -2.744083838
130 -3.757672592 2.006357737
131 2.482367886 -3.757672592
132 -2.216163118 2.482367886
133 -1.623796371 -2.216163118
134 -0.169866756 -1.623796371
135 0.857844295 -0.169866756
136 0.499053080 0.857844295
137 -2.468624925 0.499053080
138 -1.015724374 -2.468624925
139 -2.419446534 -1.015724374
140 3.116868495 -2.419446534
141 1.244149429 3.116868495
142 -0.009779507 1.244149429
143 1.726205342 -0.009779507
144 -3.356731563 1.726205342
145 2.347253794 -3.356731563
146 -2.022884772 2.347253794
147 1.037980818 -2.022884772
148 0.122740838 1.037980818
149 -2.983866911 0.122740838
150 -1.149480124 -2.983866911
151 1.972426862 -1.149480124
152 4.088948985 1.972426862
153 1.118379703 4.088948985
154 -2.511077298 1.118379703
155 0.321735886 -2.511077298
156 1.225159115 0.321735886
157 1.016139203 1.225159115
158 1.277952243 1.016139203
159 -0.902448709 1.277952243
160 0.276588644 -0.902448709
161 0.640555249 0.276588644
162 -0.219714058 0.640555249
163 0.844074307 -0.219714058
164 1.507288989 0.844074307
165 -1.369085649 1.507288989
166 -0.151183747 -1.369085649
167 -3.047381403 -0.151183747
168 -1.628420757 -3.047381403
169 1.671359890 -1.628420757
170 1.789630485 1.671359890
171 0.770582649 1.789630485
172 -1.678958916 0.770582649
173 -2.142243663 -1.678958916
174 -2.813249493 -2.142243663
175 0.563279913 -2.813249493
176 -0.044313330 0.563279913
177 -0.527125898 -0.044313330
178 0.347961979 -0.527125898
179 -1.242363629 0.347961979
180 1.097128568 -1.242363629
181 -0.230816591 1.097128568
182 2.369755033 -0.230816591
183 -0.070957371 2.369755033
184 -6.500257228 -0.070957371
185 1.261477725 -6.500257228
186 2.598884232 1.261477725
187 -0.276935001 2.598884232
188 -0.337592691 -0.276935001
189 0.766460632 -0.337592691
190 -0.738347363 0.766460632
191 0.593164124 -0.738347363
192 2.445135861 0.593164124
193 2.068898527 2.445135861
194 -0.589629361 2.068898527
195 0.067529462 -0.589629361
196 3.102911981 0.067529462
197 0.981990529 3.102911981
198 1.650521446 0.981990529
199 1.458638605 1.650521446
200 2.055406796 1.458638605
201 0.677381539 2.055406796
202 -2.427517795 0.677381539
203 -2.436710987 -2.427517795
204 2.330073087 -2.436710987
205 0.541347575 2.330073087
206 1.532062754 0.541347575
207 1.068353080 1.532062754
208 -2.556278024 1.068353080
209 1.022060198 -2.556278024
210 -2.202902206 1.022060198
211 -3.778443865 -2.202902206
212 0.855979821 -3.778443865
213 2.969296732 0.855979821
214 1.431517041 2.969296732
215 0.926865366 1.431517041
216 2.325387625 0.926865366
217 0.057640177 2.325387625
218 1.109497163 0.057640177
219 -0.205965436 1.109497163
220 -1.671017171 -0.205965436
221 -0.131920566 -1.671017171
222 0.699881753 -0.131920566
223 -1.109293774 0.699881753
224 0.288333593 -1.109293774
225 -3.747213134 0.288333593
226 0.198419826 -3.747213134
227 0.979732572 0.198419826
228 -1.229759183 0.979732572
229 -0.896838278 -1.229759183
230 1.754791518 -0.896838278
231 -3.213065762 1.754791518
232 4.632282599 -3.213065762
233 2.052583830 4.632282599
234 -0.869328864 2.052583830
235 -2.343002739 -0.869328864
236 -6.972696730 -2.343002739
237 -1.267817848 -6.972696730
238 1.693092913 -1.267817848
239 -1.682392341 1.693092913
240 -0.287911980 -1.682392341
241 -1.501192112 -0.287911980
242 1.148577467 -1.501192112
243 1.897137210 1.148577467
244 1.290751900 1.897137210
245 0.031494268 1.290751900
246 1.122268014 0.031494268
247 -2.535800023 1.122268014
248 1.237586914 -2.535800023
249 0.260609056 1.237586914
250 -0.922062430 0.260609056
251 -1.730463173 -0.922062430
252 0.598033801 -1.730463173
253 3.117443737 0.598033801
254 -1.376552283 3.117443737
255 0.095170915 -1.376552283
256 1.480120220 0.095170915
257 2.331720115 1.480120220
258 -1.356690157 2.331720115
259 -5.383345806 -1.356690157
260 1.152740672 -5.383345806
261 -4.108360954 1.152740672
262 -0.347629194 -4.108360954
263 1.085550529 -0.347629194
264 NA 1.085550529
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.381817133 -2.950927427
[2,] 2.083335628 0.381817133
[3,] 3.127025193 2.083335628
[4,] -2.358051988 3.127025193
[5,] -1.736016272 -2.358051988
[6,] 3.846299991 -1.736016272
[7,] -2.007756051 3.846299991
[8,] -1.948445497 -2.007756051
[9,] 0.701963529 -1.948445497
[10,] 1.137382762 0.701963529
[11,] -0.101807701 1.137382762
[12,] 0.610689034 -0.101807701
[13,] 0.592021392 0.610689034
[14,] -1.004395063 0.592021392
[15,] -0.379815924 -1.004395063
[16,] 0.489441182 -0.379815924
[17,] 3.616619183 0.489441182
[18,] 2.625165134 3.616619183
[19,] 0.506467834 2.625165134
[20,] 0.697230535 0.506467834
[21,] 0.958823175 0.697230535
[22,] 2.546938147 0.958823175
[23,] 1.010762105 2.546938147
[24,] 0.833157851 1.010762105
[25,] 0.778791226 0.833157851
[26,] 1.028275393 0.778791226
[27,] -1.763968014 1.028275393
[28,] 0.233833016 -1.763968014
[29,] -0.077895767 0.233833016
[30,] -0.746027284 -0.077895767
[31,] -0.737377122 -0.746027284
[32,] -0.797418619 -0.737377122
[33,] 0.071499880 -0.797418619
[34,] -1.602200306 0.071499880
[35,] -2.921926401 -1.602200306
[36,] -3.014672990 -2.921926401
[37,] -1.789991690 -3.014672990
[38,] 1.437350999 -1.789991690
[39,] 1.527886673 1.437350999
[40,] 1.272815400 1.527886673
[41,] -1.828353335 1.272815400
[42,] 2.425987887 -1.828353335
[43,] -0.166310878 2.425987887
[44,] -0.492903544 -0.166310878
[45,] -4.579946776 -0.492903544
[46,] -2.604228986 -4.579946776
[47,] -0.194229434 -2.604228986
[48,] 0.370906087 -0.194229434
[49,] -1.730933528 0.370906087
[50,] -1.147151290 -1.730933528
[51,] -0.221677409 -1.147151290
[52,] -3.145340373 -0.221677409
[53,] 0.130447599 -3.145340373
[54,] -2.364552178 0.130447599
[55,] 1.599325081 -2.364552178
[56,] -0.010771712 1.599325081
[57,] 0.412355641 -0.010771712
[58,] -0.248651715 0.412355641
[59,] 1.699039550 -0.248651715
[60,] 0.431959429 1.699039550
[61,] 0.151906615 0.431959429
[62,] -0.732096822 0.151906615
[63,] -0.849165795 -0.732096822
[64,] 0.546781501 -0.849165795
[65,] 1.213271705 0.546781501
[66,] 1.933602545 1.213271705
[67,] 3.776399158 1.933602545
[68,] -3.703958865 3.776399158
[69,] 0.552161863 -3.703958865
[70,] -3.029195508 0.552161863
[71,] -0.699009795 -3.029195508
[72,] 1.178461283 -0.699009795
[73,] 0.952614163 1.178461283
[74,] 0.938146133 0.952614163
[75,] 3.731342200 0.938146133
[76,] -0.274453484 3.731342200
[77,] 1.725904951 -0.274453484
[78,] -2.033478554 1.725904951
[79,] 1.075230233 -2.033478554
[80,] 0.434465248 1.075230233
[81,] 0.463024793 0.434465248
[82,] -0.556378965 0.463024793
[83,] 0.290823812 -0.556378965
[84,] 2.024351240 0.290823812
[85,] -0.008573324 2.024351240
[86,] 0.779169251 -0.008573324
[87,] 1.293406531 0.779169251
[88,] 0.645248973 1.293406531
[89,] -1.705080737 0.645248973
[90,] 0.321735886 -1.705080737
[91,] 0.256979711 0.321735886
[92,] -0.019563985 0.256979711
[93,] -1.963598029 -0.019563985
[94,] 1.302627336 -1.963598029
[95,] 0.316937043 1.302627336
[96,] 2.417701080 0.316937043
[97,] 0.231489430 2.417701080
[98,] -0.319858082 0.231489430
[99,] -1.024703834 -0.319858082
[100,] 1.421202664 -1.024703834
[101,] 2.531232510 1.421202664
[102,] 0.947695374 2.531232510
[103,] 1.043666722 0.947695374
[104,] -1.801167422 1.043666722
[105,] 1.341788163 -1.801167422
[106,] 0.285054577 1.341788163
[107,] 1.734830472 0.285054577
[108,] -0.144502540 1.734830472
[109,] 0.729968270 -0.144502540
[110,] -0.019022897 0.729968270
[111,] 2.027100876 -0.019022897
[112,] -1.623526260 2.027100876
[113,] -2.561106515 -1.623526260
[114,] 1.817433595 -2.561106515
[115,] -1.874239203 1.817433595
[116,] 0.936575639 -1.874239203
[117,] -1.766415230 0.936575639
[118,] 0.427246196 -1.766415230
[119,] -1.406745685 0.427246196
[120,] 0.560835235 -1.406745685
[121,] -2.891167271 0.560835235
[122,] -0.904258187 -2.891167271
[123,] -0.892153489 -0.904258187
[124,] -0.902236171 -0.892153489
[125,] 0.296367937 -0.902236171
[126,] 1.388689572 0.296367937
[127,] 1.016139203 1.388689572
[128,] -2.744083838 1.016139203
[129,] 2.006357737 -2.744083838
[130,] -3.757672592 2.006357737
[131,] 2.482367886 -3.757672592
[132,] -2.216163118 2.482367886
[133,] -1.623796371 -2.216163118
[134,] -0.169866756 -1.623796371
[135,] 0.857844295 -0.169866756
[136,] 0.499053080 0.857844295
[137,] -2.468624925 0.499053080
[138,] -1.015724374 -2.468624925
[139,] -2.419446534 -1.015724374
[140,] 3.116868495 -2.419446534
[141,] 1.244149429 3.116868495
[142,] -0.009779507 1.244149429
[143,] 1.726205342 -0.009779507
[144,] -3.356731563 1.726205342
[145,] 2.347253794 -3.356731563
[146,] -2.022884772 2.347253794
[147,] 1.037980818 -2.022884772
[148,] 0.122740838 1.037980818
[149,] -2.983866911 0.122740838
[150,] -1.149480124 -2.983866911
[151,] 1.972426862 -1.149480124
[152,] 4.088948985 1.972426862
[153,] 1.118379703 4.088948985
[154,] -2.511077298 1.118379703
[155,] 0.321735886 -2.511077298
[156,] 1.225159115 0.321735886
[157,] 1.016139203 1.225159115
[158,] 1.277952243 1.016139203
[159,] -0.902448709 1.277952243
[160,] 0.276588644 -0.902448709
[161,] 0.640555249 0.276588644
[162,] -0.219714058 0.640555249
[163,] 0.844074307 -0.219714058
[164,] 1.507288989 0.844074307
[165,] -1.369085649 1.507288989
[166,] -0.151183747 -1.369085649
[167,] -3.047381403 -0.151183747
[168,] -1.628420757 -3.047381403
[169,] 1.671359890 -1.628420757
[170,] 1.789630485 1.671359890
[171,] 0.770582649 1.789630485
[172,] -1.678958916 0.770582649
[173,] -2.142243663 -1.678958916
[174,] -2.813249493 -2.142243663
[175,] 0.563279913 -2.813249493
[176,] -0.044313330 0.563279913
[177,] -0.527125898 -0.044313330
[178,] 0.347961979 -0.527125898
[179,] -1.242363629 0.347961979
[180,] 1.097128568 -1.242363629
[181,] -0.230816591 1.097128568
[182,] 2.369755033 -0.230816591
[183,] -0.070957371 2.369755033
[184,] -6.500257228 -0.070957371
[185,] 1.261477725 -6.500257228
[186,] 2.598884232 1.261477725
[187,] -0.276935001 2.598884232
[188,] -0.337592691 -0.276935001
[189,] 0.766460632 -0.337592691
[190,] -0.738347363 0.766460632
[191,] 0.593164124 -0.738347363
[192,] 2.445135861 0.593164124
[193,] 2.068898527 2.445135861
[194,] -0.589629361 2.068898527
[195,] 0.067529462 -0.589629361
[196,] 3.102911981 0.067529462
[197,] 0.981990529 3.102911981
[198,] 1.650521446 0.981990529
[199,] 1.458638605 1.650521446
[200,] 2.055406796 1.458638605
[201,] 0.677381539 2.055406796
[202,] -2.427517795 0.677381539
[203,] -2.436710987 -2.427517795
[204,] 2.330073087 -2.436710987
[205,] 0.541347575 2.330073087
[206,] 1.532062754 0.541347575
[207,] 1.068353080 1.532062754
[208,] -2.556278024 1.068353080
[209,] 1.022060198 -2.556278024
[210,] -2.202902206 1.022060198
[211,] -3.778443865 -2.202902206
[212,] 0.855979821 -3.778443865
[213,] 2.969296732 0.855979821
[214,] 1.431517041 2.969296732
[215,] 0.926865366 1.431517041
[216,] 2.325387625 0.926865366
[217,] 0.057640177 2.325387625
[218,] 1.109497163 0.057640177
[219,] -0.205965436 1.109497163
[220,] -1.671017171 -0.205965436
[221,] -0.131920566 -1.671017171
[222,] 0.699881753 -0.131920566
[223,] -1.109293774 0.699881753
[224,] 0.288333593 -1.109293774
[225,] -3.747213134 0.288333593
[226,] 0.198419826 -3.747213134
[227,] 0.979732572 0.198419826
[228,] -1.229759183 0.979732572
[229,] -0.896838278 -1.229759183
[230,] 1.754791518 -0.896838278
[231,] -3.213065762 1.754791518
[232,] 4.632282599 -3.213065762
[233,] 2.052583830 4.632282599
[234,] -0.869328864 2.052583830
[235,] -2.343002739 -0.869328864
[236,] -6.972696730 -2.343002739
[237,] -1.267817848 -6.972696730
[238,] 1.693092913 -1.267817848
[239,] -1.682392341 1.693092913
[240,] -0.287911980 -1.682392341
[241,] -1.501192112 -0.287911980
[242,] 1.148577467 -1.501192112
[243,] 1.897137210 1.148577467
[244,] 1.290751900 1.897137210
[245,] 0.031494268 1.290751900
[246,] 1.122268014 0.031494268
[247,] -2.535800023 1.122268014
[248,] 1.237586914 -2.535800023
[249,] 0.260609056 1.237586914
[250,] -0.922062430 0.260609056
[251,] -1.730463173 -0.922062430
[252,] 0.598033801 -1.730463173
[253,] 3.117443737 0.598033801
[254,] -1.376552283 3.117443737
[255,] 0.095170915 -1.376552283
[256,] 1.480120220 0.095170915
[257,] 2.331720115 1.480120220
[258,] -1.356690157 2.331720115
[259,] -5.383345806 -1.356690157
[260,] 1.152740672 -5.383345806
[261,] -4.108360954 1.152740672
[262,] -0.347629194 -4.108360954
[263,] 1.085550529 -0.347629194
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.381817133 -2.950927427
2 2.083335628 0.381817133
3 3.127025193 2.083335628
4 -2.358051988 3.127025193
5 -1.736016272 -2.358051988
6 3.846299991 -1.736016272
7 -2.007756051 3.846299991
8 -1.948445497 -2.007756051
9 0.701963529 -1.948445497
10 1.137382762 0.701963529
11 -0.101807701 1.137382762
12 0.610689034 -0.101807701
13 0.592021392 0.610689034
14 -1.004395063 0.592021392
15 -0.379815924 -1.004395063
16 0.489441182 -0.379815924
17 3.616619183 0.489441182
18 2.625165134 3.616619183
19 0.506467834 2.625165134
20 0.697230535 0.506467834
21 0.958823175 0.697230535
22 2.546938147 0.958823175
23 1.010762105 2.546938147
24 0.833157851 1.010762105
25 0.778791226 0.833157851
26 1.028275393 0.778791226
27 -1.763968014 1.028275393
28 0.233833016 -1.763968014
29 -0.077895767 0.233833016
30 -0.746027284 -0.077895767
31 -0.737377122 -0.746027284
32 -0.797418619 -0.737377122
33 0.071499880 -0.797418619
34 -1.602200306 0.071499880
35 -2.921926401 -1.602200306
36 -3.014672990 -2.921926401
37 -1.789991690 -3.014672990
38 1.437350999 -1.789991690
39 1.527886673 1.437350999
40 1.272815400 1.527886673
41 -1.828353335 1.272815400
42 2.425987887 -1.828353335
43 -0.166310878 2.425987887
44 -0.492903544 -0.166310878
45 -4.579946776 -0.492903544
46 -2.604228986 -4.579946776
47 -0.194229434 -2.604228986
48 0.370906087 -0.194229434
49 -1.730933528 0.370906087
50 -1.147151290 -1.730933528
51 -0.221677409 -1.147151290
52 -3.145340373 -0.221677409
53 0.130447599 -3.145340373
54 -2.364552178 0.130447599
55 1.599325081 -2.364552178
56 -0.010771712 1.599325081
57 0.412355641 -0.010771712
58 -0.248651715 0.412355641
59 1.699039550 -0.248651715
60 0.431959429 1.699039550
61 0.151906615 0.431959429
62 -0.732096822 0.151906615
63 -0.849165795 -0.732096822
64 0.546781501 -0.849165795
65 1.213271705 0.546781501
66 1.933602545 1.213271705
67 3.776399158 1.933602545
68 -3.703958865 3.776399158
69 0.552161863 -3.703958865
70 -3.029195508 0.552161863
71 -0.699009795 -3.029195508
72 1.178461283 -0.699009795
73 0.952614163 1.178461283
74 0.938146133 0.952614163
75 3.731342200 0.938146133
76 -0.274453484 3.731342200
77 1.725904951 -0.274453484
78 -2.033478554 1.725904951
79 1.075230233 -2.033478554
80 0.434465248 1.075230233
81 0.463024793 0.434465248
82 -0.556378965 0.463024793
83 0.290823812 -0.556378965
84 2.024351240 0.290823812
85 -0.008573324 2.024351240
86 0.779169251 -0.008573324
87 1.293406531 0.779169251
88 0.645248973 1.293406531
89 -1.705080737 0.645248973
90 0.321735886 -1.705080737
91 0.256979711 0.321735886
92 -0.019563985 0.256979711
93 -1.963598029 -0.019563985
94 1.302627336 -1.963598029
95 0.316937043 1.302627336
96 2.417701080 0.316937043
97 0.231489430 2.417701080
98 -0.319858082 0.231489430
99 -1.024703834 -0.319858082
100 1.421202664 -1.024703834
101 2.531232510 1.421202664
102 0.947695374 2.531232510
103 1.043666722 0.947695374
104 -1.801167422 1.043666722
105 1.341788163 -1.801167422
106 0.285054577 1.341788163
107 1.734830472 0.285054577
108 -0.144502540 1.734830472
109 0.729968270 -0.144502540
110 -0.019022897 0.729968270
111 2.027100876 -0.019022897
112 -1.623526260 2.027100876
113 -2.561106515 -1.623526260
114 1.817433595 -2.561106515
115 -1.874239203 1.817433595
116 0.936575639 -1.874239203
117 -1.766415230 0.936575639
118 0.427246196 -1.766415230
119 -1.406745685 0.427246196
120 0.560835235 -1.406745685
121 -2.891167271 0.560835235
122 -0.904258187 -2.891167271
123 -0.892153489 -0.904258187
124 -0.902236171 -0.892153489
125 0.296367937 -0.902236171
126 1.388689572 0.296367937
127 1.016139203 1.388689572
128 -2.744083838 1.016139203
129 2.006357737 -2.744083838
130 -3.757672592 2.006357737
131 2.482367886 -3.757672592
132 -2.216163118 2.482367886
133 -1.623796371 -2.216163118
134 -0.169866756 -1.623796371
135 0.857844295 -0.169866756
136 0.499053080 0.857844295
137 -2.468624925 0.499053080
138 -1.015724374 -2.468624925
139 -2.419446534 -1.015724374
140 3.116868495 -2.419446534
141 1.244149429 3.116868495
142 -0.009779507 1.244149429
143 1.726205342 -0.009779507
144 -3.356731563 1.726205342
145 2.347253794 -3.356731563
146 -2.022884772 2.347253794
147 1.037980818 -2.022884772
148 0.122740838 1.037980818
149 -2.983866911 0.122740838
150 -1.149480124 -2.983866911
151 1.972426862 -1.149480124
152 4.088948985 1.972426862
153 1.118379703 4.088948985
154 -2.511077298 1.118379703
155 0.321735886 -2.511077298
156 1.225159115 0.321735886
157 1.016139203 1.225159115
158 1.277952243 1.016139203
159 -0.902448709 1.277952243
160 0.276588644 -0.902448709
161 0.640555249 0.276588644
162 -0.219714058 0.640555249
163 0.844074307 -0.219714058
164 1.507288989 0.844074307
165 -1.369085649 1.507288989
166 -0.151183747 -1.369085649
167 -3.047381403 -0.151183747
168 -1.628420757 -3.047381403
169 1.671359890 -1.628420757
170 1.789630485 1.671359890
171 0.770582649 1.789630485
172 -1.678958916 0.770582649
173 -2.142243663 -1.678958916
174 -2.813249493 -2.142243663
175 0.563279913 -2.813249493
176 -0.044313330 0.563279913
177 -0.527125898 -0.044313330
178 0.347961979 -0.527125898
179 -1.242363629 0.347961979
180 1.097128568 -1.242363629
181 -0.230816591 1.097128568
182 2.369755033 -0.230816591
183 -0.070957371 2.369755033
184 -6.500257228 -0.070957371
185 1.261477725 -6.500257228
186 2.598884232 1.261477725
187 -0.276935001 2.598884232
188 -0.337592691 -0.276935001
189 0.766460632 -0.337592691
190 -0.738347363 0.766460632
191 0.593164124 -0.738347363
192 2.445135861 0.593164124
193 2.068898527 2.445135861
194 -0.589629361 2.068898527
195 0.067529462 -0.589629361
196 3.102911981 0.067529462
197 0.981990529 3.102911981
198 1.650521446 0.981990529
199 1.458638605 1.650521446
200 2.055406796 1.458638605
201 0.677381539 2.055406796
202 -2.427517795 0.677381539
203 -2.436710987 -2.427517795
204 2.330073087 -2.436710987
205 0.541347575 2.330073087
206 1.532062754 0.541347575
207 1.068353080 1.532062754
208 -2.556278024 1.068353080
209 1.022060198 -2.556278024
210 -2.202902206 1.022060198
211 -3.778443865 -2.202902206
212 0.855979821 -3.778443865
213 2.969296732 0.855979821
214 1.431517041 2.969296732
215 0.926865366 1.431517041
216 2.325387625 0.926865366
217 0.057640177 2.325387625
218 1.109497163 0.057640177
219 -0.205965436 1.109497163
220 -1.671017171 -0.205965436
221 -0.131920566 -1.671017171
222 0.699881753 -0.131920566
223 -1.109293774 0.699881753
224 0.288333593 -1.109293774
225 -3.747213134 0.288333593
226 0.198419826 -3.747213134
227 0.979732572 0.198419826
228 -1.229759183 0.979732572
229 -0.896838278 -1.229759183
230 1.754791518 -0.896838278
231 -3.213065762 1.754791518
232 4.632282599 -3.213065762
233 2.052583830 4.632282599
234 -0.869328864 2.052583830
235 -2.343002739 -0.869328864
236 -6.972696730 -2.343002739
237 -1.267817848 -6.972696730
238 1.693092913 -1.267817848
239 -1.682392341 1.693092913
240 -0.287911980 -1.682392341
241 -1.501192112 -0.287911980
242 1.148577467 -1.501192112
243 1.897137210 1.148577467
244 1.290751900 1.897137210
245 0.031494268 1.290751900
246 1.122268014 0.031494268
247 -2.535800023 1.122268014
248 1.237586914 -2.535800023
249 0.260609056 1.237586914
250 -0.922062430 0.260609056
251 -1.730463173 -0.922062430
252 0.598033801 -1.730463173
253 3.117443737 0.598033801
254 -1.376552283 3.117443737
255 0.095170915 -1.376552283
256 1.480120220 0.095170915
257 2.331720115 1.480120220
258 -1.356690157 2.331720115
259 -5.383345806 -1.356690157
260 1.152740672 -5.383345806
261 -4.108360954 1.152740672
262 -0.347629194 -4.108360954
263 1.085550529 -0.347629194
> 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/7mt7s1353952026.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/8dys71353952026.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/9ntm41353952026.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/10vtd41353952026.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11l0jg1353952026.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12obpe1353952026.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/136shz1353952026.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14nukf1353952026.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/152od41353952026.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/1663361353952026.tab")
+ }
>
> try(system("convert tmp/13e0s1353952026.ps tmp/13e0s1353952026.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tqcr1353952026.ps tmp/2tqcr1353952026.png",intern=TRUE))
character(0)
> try(system("convert tmp/3s9r91353952026.ps tmp/3s9r91353952026.png",intern=TRUE))
character(0)
> try(system("convert tmp/4p9xx1353952026.ps tmp/4p9xx1353952026.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rmxg1353952026.ps tmp/5rmxg1353952026.png",intern=TRUE))
character(0)
> try(system("convert tmp/6w8ck1353952026.ps tmp/6w8ck1353952026.png",intern=TRUE))
character(0)
> try(system("convert tmp/7mt7s1353952026.ps tmp/7mt7s1353952026.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dys71353952026.ps tmp/8dys71353952026.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ntm41353952026.ps tmp/9ntm41353952026.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vtd41353952026.ps tmp/10vtd41353952026.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
13.842 0.991 14.904