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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+ ,30
+ ,4
+ ,3
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+ ,8
+ ,83
+ ,52
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+ ,27
+ ,30
+ ,15
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+ ,64
+ ,38
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+ ,34
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+ ,11
+ ,12
+ ,15
+ ,12
+ ,68
+ ,41
+ ,263
+ ,40
+ ,36
+ ,11
+ ,7
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+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('t'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('t','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'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 t Connected Separate Software Happiness Depression Belonging
1 13 1 41 38 12 14 12.0 53
2 16 2 39 32 11 18 11.0 83
3 19 3 30 35 15 11 14.0 66
4 15 4 31 33 6 12 12.0 67
5 14 5 34 37 13 16 21.0 76
6 13 6 35 29 10 18 12.0 78
7 19 7 39 31 12 14 22.0 53
8 15 8 34 36 14 14 11.0 80
9 14 9 36 35 12 15 10.0 74
10 15 10 37 38 9 15 13.0 76
11 16 11 38 31 10 17 10.0 79
12 16 12 36 34 12 19 8.0 54
13 16 13 38 35 12 10 15.0 67
14 16 14 39 38 11 16 14.0 54
15 17 15 33 37 15 18 10.0 87
16 15 16 32 33 12 14 14.0 58
17 15 17 36 32 10 14 14.0 75
18 20 18 38 38 12 17 11.0 88
19 18 19 39 38 11 14 10.0 64
20 16 20 32 32 12 16 13.0 57
21 16 21 32 33 11 18 9.5 66
22 16 22 31 31 12 11 14.0 68
23 19 23 39 38 13 14 12.0 54
24 16 24 37 39 11 12 14.0 56
25 17 25 39 32 12 17 11.0 86
26 17 26 41 32 13 9 9.0 80
27 16 27 36 35 10 16 11.0 76
28 15 28 33 37 14 14 15.0 69
29 16 29 33 33 12 15 14.0 78
30 14 30 34 33 10 11 13.0 67
31 15 31 31 31 12 16 9.0 80
32 12 32 27 32 8 13 15.0 54
33 14 33 37 31 10 17 10.0 71
34 16 34 34 37 12 15 11.0 84
35 14 35 34 30 12 14 13.0 74
36 10 36 32 33 7 16 8.0 71
37 10 37 29 31 9 9 20.0 63
38 14 38 36 33 12 15 12.0 71
39 16 39 29 31 10 17 10.0 76
40 16 40 35 33 10 13 10.0 69
41 16 41 37 32 10 15 9.0 74
42 14 42 34 33 12 16 14.0 75
43 20 43 38 32 15 16 8.0 54
44 14 44 35 33 10 12 14.0 52
45 14 45 38 28 10 15 11.0 69
46 11 46 37 35 12 11 13.0 68
47 14 47 38 39 13 15 9.0 65
48 15 48 33 34 11 15 11.0 75
49 16 49 36 38 11 17 15.0 74
50 14 50 38 32 12 13 11.0 75
51 16 51 32 38 14 16 10.0 72
52 14 52 32 30 10 14 14.0 67
53 12 53 32 33 12 11 18.0 63
54 16 54 34 38 13 12 14.0 62
55 9 55 32 32 5 12 11.0 63
56 14 56 37 35 6 15 14.5 76
57 16 57 39 34 12 16 13.0 74
58 16 58 29 34 12 15 9.0 67
59 15 59 37 36 11 12 10.0 73
60 16 60 35 34 10 12 15.0 70
61 12 61 30 28 7 8 20.0 53
62 16 62 38 34 12 13 12.0 77
63 16 63 34 35 14 11 12.0 80
64 14 64 31 35 11 14 14.0 52
65 16 65 34 31 12 15 13.0 54
66 17 66 35 37 13 10 11.0 80
67 18 67 36 35 14 11 17.0 66
68 18 68 30 27 11 12 12.0 73
69 12 69 39 40 12 15 13.0 63
70 16 70 35 37 12 15 14.0 69
71 10 71 38 36 8 14 13.0 67
72 14 72 31 38 11 16 15.0 54
73 18 73 34 39 14 15 13.0 81
74 18 74 38 41 14 15 10.0 69
75 16 75 34 27 12 13 11.0 84
76 17 76 39 30 9 12 19.0 80
77 16 77 37 37 13 17 13.0 70
78 16 78 34 31 11 13 17.0 69
79 13 79 28 31 12 15 13.0 77
80 16 80 37 27 12 13 9.0 54
81 16 81 33 36 12 15 11.0 79
82 16 82 35 37 12 15 9.0 71
83 15 83 37 33 12 16 12.0 73
84 15 84 32 34 11 15 12.0 72
85 16 85 33 31 10 14 13.0 77
86 14 86 38 39 9 15 13.0 75
87 16 87 33 34 12 14 12.0 69
88 16 88 29 32 12 13 15.0 54
89 15 89 33 33 12 7 22.0 70
90 12 90 31 36 9 17 13.0 73
91 17 91 36 32 15 13 15.0 54
92 16 92 35 41 12 15 13.0 77
93 15 93 32 28 12 14 15.0 82
94 13 94 29 30 12 13 12.5 80
95 16 95 39 36 10 16 11.0 80
96 16 96 37 35 13 12 16.0 69
97 16 97 35 31 9 14 11.0 78
98 16 98 37 34 12 17 11.0 81
99 14 99 32 36 10 15 10.0 76
100 16 100 38 36 14 17 10.0 76
101 16 101 37 35 11 12 16.0 73
102 20 102 36 37 15 16 12.0 85
103 15 103 32 28 11 11 11.0 66
104 16 104 33 39 11 15 16.0 79
105 13 105 40 32 12 9 19.0 68
106 17 106 38 35 12 16 11.0 76
107 16 107 41 39 12 15 16.0 71
108 16 108 36 35 11 10 15.0 54
109 12 109 43 42 7 10 24.0 46
110 16 110 30 34 12 15 14.0 85
111 16 111 31 33 14 11 15.0 74
112 17 112 32 41 11 13 11.0 88
113 13 113 32 33 11 14 15.0 38
114 12 114 37 34 10 18 12.0 76
115 18 115 37 32 13 16 10.0 86
116 14 116 33 40 13 14 14.0 54
117 14 117 34 40 8 14 13.0 67
118 13 118 33 35 11 14 9.0 69
119 16 119 38 36 12 14 15.0 90
120 13 120 33 37 11 12 15.0 54
121 16 121 31 27 13 14 14.0 76
122 13 122 38 39 12 15 11.0 89
123 16 123 37 38 14 15 8.0 76
124 15 124 36 31 13 15 11.0 73
125 16 125 31 33 15 13 11.0 79
126 15 126 39 32 10 17 8.0 90
127 17 127 44 39 11 17 10.0 74
128 15 128 33 36 9 19 11.0 81
129 12 129 35 33 11 15 13.0 72
130 16 130 32 33 10 13 11.0 71
131 10 131 28 32 11 9 20.0 66
132 16 132 40 37 8 15 10.0 77
133 12 133 27 30 11 15 15.0 65
134 14 134 37 38 12 15 12.0 74
135 15 135 32 29 12 16 14.0 85
136 13 136 28 22 9 11 23.0 54
137 15 137 34 35 11 14 14.0 63
138 11 138 30 35 10 11 16.0 54
139 12 139 35 34 8 15 11.0 64
140 11 140 31 35 9 13 12.0 69
141 16 141 32 34 8 15 10.0 54
142 15 142 30 37 9 16 14.0 84
143 17 143 30 35 15 14 12.0 86
144 16 144 31 23 11 15 12.0 77
145 10 145 40 31 8 16 11.0 89
146 18 146 32 27 13 16 12.0 76
147 13 147 36 36 12 11 13.0 60
148 16 148 32 31 12 12 11.0 75
149 13 149 35 32 9 9 19.0 73
150 10 150 38 39 7 16 12.0 85
151 15 151 42 37 13 13 17.0 79
152 16 152 34 38 9 16 9.0 71
153 16 153 35 39 6 12 12.0 72
154 14 154 38 34 8 9 19.0 69
155 10 155 33 31 8 13 18.0 78
156 17 156 36 32 15 13 15.0 54
157 13 157 32 37 6 14 14.0 69
158 15 158 33 36 9 19 11.0 81
159 16 159 34 32 11 13 9.0 84
160 12 160 32 38 8 12 18.0 84
161 13 161 34 36 8 13 16.0 69
162 13 162 27 26 10 10 24.0 66
163 12 163 31 26 8 14 14.0 81
164 17 164 38 33 14 16 20.0 82
165 15 165 34 39 10 10 18.0 72
166 10 166 24 30 8 11 23.0 54
167 14 167 30 33 11 14 12.0 78
168 11 168 26 25 12 12 14.0 74
169 13 169 34 38 12 9 16.0 82
170 16 170 27 37 12 9 18.0 73
171 12 171 37 31 5 11 20.0 55
172 16 172 36 37 12 16 12.0 72
173 12 173 41 35 10 9 12.0 78
174 9 174 29 25 7 13 17.0 59
175 12 175 36 28 12 16 13.0 72
176 15 176 32 35 11 13 9.0 78
177 12 177 37 33 8 9 16.0 68
178 12 178 30 30 9 12 18.0 69
179 14 179 31 31 10 16 10.0 67
180 12 180 38 37 9 11 14.0 74
181 16 181 36 36 12 14 11.0 54
182 11 182 35 30 6 13 9.0 67
183 19 183 31 36 15 15 11.0 70
184 15 184 38 32 12 14 10.0 80
185 8 185 22 28 12 16 11.0 89
186 16 186 32 36 12 13 19.0 76
187 17 187 36 34 11 14 14.0 74
188 12 188 39 31 7 15 12.0 87
189 11 189 28 28 7 13 14.0 54
190 11 190 32 36 5 11 21.0 61
191 14 191 32 36 12 11 13.0 38
192 16 192 38 40 12 14 10.0 75
193 12 193 32 33 3 15 15.0 69
194 16 194 35 37 11 11 16.0 62
195 13 195 32 32 10 15 14.0 72
196 15 196 37 38 12 12 12.0 70
197 16 197 34 31 9 14 19.0 79
198 16 198 33 37 12 14 15.0 87
199 14 199 33 33 9 8 19.0 62
200 16 200 26 32 12 13 13.0 77
201 16 201 30 30 12 9 17.0 69
202 14 202 24 30 10 15 12.0 69
203 11 203 34 31 9 17 11.0 75
204 12 204 34 32 12 13 14.0 54
205 15 205 33 34 8 15 11.0 72
206 15 206 34 36 11 15 13.0 74
207 16 207 35 37 11 14 12.0 85
208 16 208 35 36 12 16 15.0 52
209 11 209 36 33 10 13 14.0 70
210 15 210 34 33 10 16 12.0 84
211 12 211 34 33 12 9 17.0 64
212 12 212 41 44 12 16 11.0 84
213 15 213 32 39 11 11 18.0 87
214 15 214 30 32 8 10 13.0 79
215 16 215 35 35 12 11 17.0 67
216 14 216 28 25 10 15 13.0 65
217 17 217 33 35 11 17 11.0 85
218 14 218 39 34 10 14 12.0 83
219 13 219 36 35 8 8 22.0 61
220 15 220 36 39 12 15 14.0 82
221 13 221 35 33 12 11 12.0 76
222 14 222 38 36 10 16 12.0 58
223 15 223 33 32 12 10 17.0 72
224 12 224 31 32 9 15 9.0 72
225 13 225 34 36 9 9 21.0 38
226 8 226 32 36 6 16 10.0 78
227 14 227 31 32 10 19 11.0 54
228 14 228 33 34 9 12 12.0 63
229 11 229 34 33 9 8 23.0 66
230 12 230 34 35 9 11 13.0 70
231 13 231 34 30 6 14 12.0 71
232 10 232 33 38 10 9 16.0 67
233 16 233 32 34 6 15 9.0 58
234 18 234 41 33 14 13 17.0 72
235 13 235 34 32 10 16 9.0 72
236 11 236 36 31 10 11 14.0 70
237 4 237 37 30 6 12 17.0 76
238 13 238 36 27 12 13 13.0 50
239 16 239 29 31 12 10 11.0 72
240 10 240 37 30 7 11 12.0 72
241 12 241 27 32 8 12 10.0 88
242 12 242 35 35 11 8 19.0 53
243 10 243 28 28 3 12 16.0 58
244 13 244 35 33 6 12 16.0 66
245 15 245 37 31 10 15 14.0 82
246 12 246 29 35 8 11 20.0 69
247 14 247 32 35 9 13 15.0 68
248 10 248 36 32 9 14 23.0 44
249 12 249 19 21 8 10 20.0 56
250 12 250 21 20 9 12 16.0 53
251 11 251 31 34 7 15 14.0 70
252 10 252 33 32 7 13 17.0 78
253 12 253 36 34 6 13 11.0 71
254 16 254 33 32 9 13 13.0 72
255 12 255 37 33 10 12 17.0 68
256 14 256 34 33 11 12 15.0 67
257 16 257 35 37 12 9 21.0 75
258 14 258 31 32 8 9 18.0 62
259 13 259 37 34 11 15 15.0 67
260 4 260 35 30 3 10 8.0 83
261 15 261 27 30 11 14 12.0 64
262 11 262 34 38 12 15 12.0 68
263 11 263 40 36 7 7 22.0 62
264 14 264 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) t Connected Separate
5.334627 -0.004938 0.033432 0.042910
Software Happiness Depression Belonging
0.557382 0.070714 -0.028988 0.023564
Belonging_Final
-0.025260
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9166 -1.0426 0.2822 1.2093 4.6857
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.334627 1.962792 2.718 0.00702 **
t -0.004938 0.001684 -2.933 0.00367 **
Connected 0.033432 0.034590 0.967 0.33471
Separate 0.042910 0.035132 1.221 0.22307
Software 0.557382 0.053864 10.348 < 2e-16 ***
Happiness 0.070714 0.057909 1.221 0.22317
Depression -0.028988 0.042089 -0.689 0.49162
Belonging 0.023564 0.037386 0.630 0.52906
Belonging_Final -0.025260 0.055660 -0.454 0.65035
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.857 on 255 degrees of freedom
Multiple R-squared: 0.4456, Adjusted R-squared: 0.4282
F-statistic: 25.62 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,] 0.094906934 0.189813867 0.9050931
[2,] 0.168821052 0.337642105 0.8311789
[3,] 0.178863828 0.357727656 0.8211362
[4,] 0.102108222 0.204216444 0.8978918
[5,] 0.078428197 0.156856394 0.9215718
[6,] 0.087023766 0.174047532 0.9129762
[7,] 0.213223440 0.426446880 0.7867766
[8,] 0.175810464 0.351620929 0.8241895
[9,] 0.139406042 0.278812084 0.8605940
[10,] 0.107616873 0.215233746 0.8923831
[11,] 0.131400316 0.262800633 0.8685997
[12,] 0.229848580 0.459697160 0.7701514
[13,] 0.345400678 0.690801356 0.6545993
[14,] 0.282158757 0.564317513 0.7178412
[15,] 0.237323312 0.474646624 0.7626767
[16,] 0.227896246 0.455792491 0.7721038
[17,] 0.338485052 0.676970104 0.6615149
[18,] 0.289325481 0.578650962 0.7106745
[19,] 0.397882860 0.795765720 0.6021171
[20,] 0.356890257 0.713780515 0.6431097
[21,] 0.329416582 0.658833164 0.6705834
[22,] 0.314121460 0.628242920 0.6858785
[23,] 0.278262895 0.556525790 0.7217371
[24,] 0.236820684 0.473641369 0.7631793
[25,] 0.366904764 0.733809527 0.6330952
[26,] 0.395621729 0.791243457 0.6043783
[27,] 0.371576673 0.743153345 0.6284233
[28,] 0.416207603 0.832415205 0.5837924
[29,] 0.396546651 0.793093301 0.6034533
[30,] 0.357799571 0.715599141 0.6422004
[31,] 0.319486474 0.638972948 0.6805135
[32,] 0.338409103 0.676818206 0.6615909
[33,] 0.291402618 0.582805236 0.7085974
[34,] 0.264493102 0.528986203 0.7355069
[35,] 0.441612475 0.883224950 0.5583875
[36,] 0.532267003 0.935465995 0.4677330
[37,] 0.489201696 0.978403393 0.5107983
[38,] 0.513642655 0.972714690 0.4863573
[39,] 0.486621948 0.973243896 0.5133781
[40,] 0.441557259 0.883114518 0.5584427
[41,] 0.400425011 0.800850022 0.5995750
[42,] 0.399864147 0.799728295 0.6001359
[43,] 0.358080564 0.716161128 0.6419194
[44,] 0.346095997 0.692191993 0.6539040
[45,] 0.343301127 0.686602254 0.6566989
[46,] 0.304239889 0.608479778 0.6957601
[47,] 0.285720086 0.571440172 0.7142799
[48,] 0.255885143 0.511770286 0.7441149
[49,] 0.276307802 0.552615603 0.7236922
[50,] 0.252992904 0.505985808 0.7470071
[51,] 0.228275085 0.456550171 0.7717249
[52,] 0.199677702 0.399355404 0.8003223
[53,] 0.172011899 0.344023798 0.8279881
[54,] 0.151781538 0.303563076 0.8482185
[55,] 0.143071387 0.286142774 0.8569286
[56,] 0.139047554 0.278095107 0.8609524
[57,] 0.269203231 0.538406462 0.7307968
[58,] 0.364495176 0.728990352 0.6355048
[59,] 0.334000902 0.668001805 0.6659991
[60,] 0.443101781 0.886203562 0.5568982
[61,] 0.408390199 0.816780398 0.5916098
[62,] 0.408893218 0.817786435 0.5911068
[63,] 0.397135724 0.794271447 0.6028643
[64,] 0.360034057 0.720068113 0.6399659
[65,] 0.425793182 0.851586363 0.5742068
[66,] 0.388042441 0.776084882 0.6119576
[67,] 0.362094759 0.724189517 0.6379052
[68,] 0.376793250 0.753586499 0.6232068
[69,] 0.343116850 0.686233701 0.6568831
[70,] 0.314522821 0.629045643 0.6854772
[71,] 0.280862484 0.561724969 0.7191375
[72,] 0.253282202 0.506564405 0.7467178
[73,] 0.223423532 0.446847064 0.7765765
[74,] 0.217294113 0.434588226 0.7827059
[75,] 0.189821596 0.379643192 0.8101784
[76,] 0.167487050 0.334974100 0.8325129
[77,] 0.155126911 0.310253823 0.8448731
[78,] 0.133636740 0.267273480 0.8663633
[79,] 0.132269927 0.264539853 0.8677301
[80,] 0.113156521 0.226313043 0.8868435
[81,] 0.098884736 0.197769472 0.9011153
[82,] 0.084107031 0.168214063 0.9158930
[83,] 0.087387903 0.174775806 0.9126121
[84,] 0.079055976 0.158111952 0.9209440
[85,] 0.066156816 0.132313631 0.9338432
[86,] 0.070875730 0.141751461 0.9291243
[87,] 0.058872468 0.117744936 0.9411275
[88,] 0.048848841 0.097697682 0.9511512
[89,] 0.042921172 0.085842344 0.9570788
[90,] 0.037574430 0.075148859 0.9624256
[91,] 0.043465022 0.086930044 0.9565350
[92,] 0.036635802 0.073271604 0.9633642
[93,] 0.032852846 0.065705693 0.9671472
[94,] 0.039139202 0.078278404 0.9608608
[95,] 0.034051571 0.068103142 0.9659484
[96,] 0.027651645 0.055303290 0.9723484
[97,] 0.026780271 0.053560541 0.9732197
[98,] 0.021754995 0.043509990 0.9782450
[99,] 0.017726901 0.035453801 0.9822731
[100,] 0.014054539 0.028109078 0.9859455
[101,] 0.014195181 0.028390362 0.9858048
[102,] 0.012888492 0.025776984 0.9871115
[103,] 0.018860640 0.037721279 0.9811394
[104,] 0.017729977 0.035459954 0.9822700
[105,] 0.017234496 0.034468992 0.9827655
[106,] 0.014162764 0.028325528 0.9858372
[107,] 0.014253226 0.028506453 0.9857468
[108,] 0.011488413 0.022976825 0.9885116
[109,] 0.010285054 0.020570109 0.9897149
[110,] 0.008261910 0.016523819 0.9917381
[111,] 0.012518578 0.025037157 0.9874814
[112,] 0.010447201 0.020894403 0.9895528
[113,] 0.008973745 0.017947491 0.9910263
[114,] 0.007295192 0.014590384 0.9927048
[115,] 0.005773670 0.011547340 0.9942263
[116,] 0.005258363 0.010516726 0.9947416
[117,] 0.004250609 0.008501218 0.9957494
[118,] 0.006006641 0.012013283 0.9939934
[119,] 0.006603666 0.013207332 0.9933963
[120,] 0.013738602 0.027477203 0.9862614
[121,] 0.016419826 0.032839652 0.9835802
[122,] 0.017602040 0.035204079 0.9823980
[123,] 0.016695082 0.033390165 0.9833049
[124,] 0.013344148 0.026688296 0.9866559
[125,] 0.011222517 0.022445035 0.9887775
[126,] 0.008916657 0.017833315 0.9910833
[127,] 0.011150542 0.022301084 0.9888495
[128,] 0.009557228 0.019114455 0.9904428
[129,] 0.011312387 0.022624774 0.9886876
[130,] 0.017959078 0.035918156 0.9820409
[131,] 0.016548886 0.033097772 0.9834511
[132,] 0.013305854 0.026611707 0.9866941
[133,] 0.013698861 0.027397722 0.9863011
[134,] 0.024579466 0.049158932 0.9754205
[135,] 0.029835940 0.059671880 0.9701641
[136,] 0.031317522 0.062635044 0.9686825
[137,] 0.027711794 0.055423588 0.9722882
[138,] 0.022552407 0.045104814 0.9774476
[139,] 0.031496616 0.062993232 0.9685034
[140,] 0.026599897 0.053199794 0.9734001
[141,] 0.028945975 0.057891950 0.9710540
[142,] 0.058537603 0.117075206 0.9414624
[143,] 0.056404986 0.112809972 0.9435950
[144,] 0.064608244 0.129216487 0.9353918
[145,] 0.054937053 0.109874106 0.9450629
[146,] 0.049018871 0.098037742 0.9509811
[147,] 0.042689542 0.085379085 0.9573105
[148,] 0.041329549 0.082659097 0.9586705
[149,] 0.035466990 0.070933980 0.9645330
[150,] 0.028956466 0.057912931 0.9710435
[151,] 0.023635708 0.047271416 0.9763643
[152,] 0.019309639 0.038619278 0.9806904
[153,] 0.016329110 0.032658220 0.9836709
[154,] 0.014450083 0.028900167 0.9855499
[155,] 0.014948612 0.029897224 0.9850514
[156,] 0.011884217 0.023768435 0.9881158
[157,] 0.017513278 0.035026557 0.9824867
[158,] 0.017634008 0.035268016 0.9823660
[159,] 0.016735388 0.033470777 0.9832646
[160,] 0.015456662 0.030913323 0.9845433
[161,] 0.012472373 0.024944746 0.9875276
[162,] 0.012088212 0.024176424 0.9879118
[163,] 0.013774829 0.027549657 0.9862252
[164,] 0.017933506 0.035867012 0.9820665
[165,] 0.014378215 0.028756431 0.9856218
[166,] 0.011352380 0.022704760 0.9886476
[167,] 0.009545563 0.019091125 0.9904544
[168,] 0.007389758 0.014779516 0.9926102
[169,] 0.006509052 0.013018104 0.9934909
[170,] 0.005310520 0.010621041 0.9946895
[171,] 0.004086687 0.008173374 0.9959133
[172,] 0.004424693 0.008849386 0.9955753
[173,] 0.003352023 0.006704046 0.9966480
[174,] 0.091632702 0.183265405 0.9083673
[175,] 0.081927934 0.163855867 0.9180721
[176,] 0.090420002 0.180840005 0.9095800
[177,] 0.075900513 0.151801026 0.9240995
[178,] 0.067746923 0.135493845 0.9322531
[179,] 0.056589712 0.113179424 0.9434103
[180,] 0.052365080 0.104730160 0.9476349
[181,] 0.043257361 0.086514722 0.9567426
[182,] 0.044375219 0.088750438 0.9556248
[183,] 0.044859205 0.089718410 0.9551408
[184,] 0.038791360 0.077582720 0.9612086
[185,] 0.031214253 0.062428506 0.9687857
[186,] 0.039414893 0.078829787 0.9605851
[187,] 0.032183611 0.064367223 0.9678164
[188,] 0.028964272 0.057928543 0.9710357
[189,] 0.024856920 0.049713841 0.9751431
[190,] 0.023246267 0.046492534 0.9767537
[191,] 0.019650123 0.039300245 0.9803499
[192,] 0.025806160 0.051612320 0.9741938
[193,] 0.030055872 0.060111744 0.9699441
[194,] 0.031477354 0.062954707 0.9685226
[195,] 0.024673454 0.049346908 0.9753265
[196,] 0.023619571 0.047239143 0.9763804
[197,] 0.019309718 0.038619436 0.9806903
[198,] 0.021531713 0.043063425 0.9784683
[199,] 0.017159994 0.034319988 0.9828400
[200,] 0.019720061 0.039440122 0.9802799
[201,] 0.031986014 0.063972029 0.9680140
[202,] 0.024900999 0.049801998 0.9750990
[203,] 0.032199633 0.064399267 0.9678004
[204,] 0.027323206 0.054646411 0.9726768
[205,] 0.021137575 0.042275150 0.9788624
[206,] 0.023220552 0.046441103 0.9767794
[207,] 0.019579733 0.039159465 0.9804203
[208,] 0.018191434 0.036382867 0.9818086
[209,] 0.013910465 0.027820931 0.9860895
[210,] 0.011584636 0.023169271 0.9884154
[211,] 0.008318636 0.016637273 0.9916814
[212,] 0.006380204 0.012760408 0.9936198
[213,] 0.004809066 0.009618132 0.9951909
[214,] 0.004093750 0.008187501 0.9959062
[215,] 0.009466810 0.018933620 0.9905332
[216,] 0.007417951 0.014835902 0.9925820
[217,] 0.005488961 0.010977923 0.9945110
[218,] 0.003925897 0.007851793 0.9960741
[219,] 0.002890463 0.005780927 0.9971095
[220,] 0.002606182 0.005212364 0.9973938
[221,] 0.006137343 0.012274686 0.9938627
[222,] 0.024764967 0.049529935 0.9752350
[223,] 0.037747758 0.075495516 0.9622522
[224,] 0.027231099 0.054462198 0.9727689
[225,] 0.022825449 0.045650898 0.9771746
[226,] 0.197370722 0.394741444 0.8026293
[227,] 0.155667522 0.311335043 0.8443325
[228,] 0.129607141 0.259214282 0.8703929
[229,] 0.100544198 0.201088396 0.8994558
[230,] 0.088408181 0.176816362 0.9115918
[231,] 0.137938494 0.275876989 0.8620615
[232,] 0.112959828 0.225919657 0.8870402
[233,] 0.118293731 0.236587461 0.8817063
[234,] 0.096819834 0.193639669 0.9031802
[235,] 0.082174010 0.164348020 0.9178260
[236,] 0.053056083 0.106112166 0.9469439
[237,] 0.045202873 0.090405746 0.9547971
[238,] 0.040451904 0.080903807 0.9595481
[239,] 0.115496462 0.230992924 0.8845035
[240,] 0.094703841 0.189407682 0.9052962
[241,] 0.431439000 0.862878000 0.5685610
> postscript(file="/var/wessaorg/rcomp/tmp/1ymya1353953016.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2aij41353953016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33zvk1353953016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4d4xd1353953016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/576no1353953016.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
-3.102298221 0.245508554 1.948287423 2.844541916 -2.431877832 -1.869129571
7 8 9 10 11 12
3.710736971 -2.098463663 -2.062068866 0.543207803 1.009137890 -0.177005218
13 14 15 16 17 18
0.478496420 0.504384283 -1.006512051 -0.471600438 0.384029318 3.571776300
19 20 21 22 23 24
2.520984906 0.444210606 0.634956735 0.855854122 2.517513085 0.914481509
25 26 27 28 29 30
0.826979440 0.730492812 1.002205332 -1.760323978 0.244499178 -0.181416578
31 32 33 34 35 36
-0.754683476 -0.834815524 -0.811839163 0.063104612 -1.570370764 -3.005541434
37 38 39 40 41 42
-3.074708695 -1.754899768 1.443200553 1.508493443 1.277020449 -1.750019986
43 44 45 46 47 48
2.535021239 -0.162354026 -0.465001909 -4.527891846 -2.537742048 -0.138218462
49 50 51 52 53 54
0.491833471 -1.650896199 -1.013287553 -0.161379564 -3.053366612 0.177010959
55 56 57 58 59 60
-2.397791027 1.614711519 0.133816130 0.517006043 -0.148945246 1.781689396
61 62 63 64 65 66
0.433922695 0.304398347 -0.568097897 -0.714497575 0.708091983 0.951831948
67 68 69 70 71 72
1.708070000 3.523165161 -3.911285460 0.344750275 -3.338794504 -0.912773043
73 74 75 76 77 78
1.032210414 0.811332375 0.735049423 3.487857269 -0.413649675 1.511437148
79 80 81 82 83 84
-2.210529169 0.878982388 0.287274495 0.287717529 -0.683966261 0.122140390
85 86 87 88 89 90
1.837417717 -0.184805355 0.637027251 1.145316210 0.476380270 -1.871340301
91 92 93 94 95 96
0.253960372 0.165281862 -0.110267757 -2.070740981 1.201561147 0.230086471
97 98 99 100 101 102
2.280392742 0.185275621 -0.433941180 -1.025810198 1.325803102 2.544064759
103 104 105 106 107 108
0.818154279 0.999697148 -1.867506176 1.311712397 0.277149621 1.650845580
109 110 111 112 113 114
-0.275456138 0.713725048 -0.017651315 1.973299951 -1.566454216 -2.570117240
115 116 117 118 119 120
1.847860891 -1.850512307 0.850437998 -1.782387174 0.462550890 -1.416850975
121 122 123 124 125 126
0.608832531 -2.839724798 -0.830654743 -0.827396197 -0.805322704 0.335034965
127 128 129 130 131 132
1.472209186 1.038338342 -2.684050075 2.035062790 -3.729699165 2.509846847
133 134 135 136 137 138
-2.146227277 -1.523753324 -0.060594176 0.890344539 0.564325197 -2.434249991
139 140 141 142 143 144
-0.975933494 -2.359707165 3.164075491 1.342747174 0.176053779 1.856556734
145 146 147 148 149 150
-3.265660580 2.524706201 -1.900643556 1.172495986 0.197545859 -2.887279570
151 152 153 154 155 156
-0.927635686 2.124290496 4.147065404 1.611980680 -2.409036113 0.574926510
157 158 159 160 161 162
1.314913659 1.186476560 1.459952990 -0.747215610 0.400414580 0.392666584
163 164 165 166 167 168
-0.446513743 0.764450432 1.300316767 -1.613687684 -0.327870372 -3.210686251
169 170 171 172 173 174
-1.797873078 1.526714914 1.520357883 0.666125952 -1.866121650 -2.352065885
175 176 177 178 179 180
-2.903882575 0.446600156 -0.261503599 -0.679444147 0.224142391 -1.375191100
181 182 183 184 185 186
1.012217824 -0.439178441 2.347219736 -0.130955841 -6.567169685 1.232691977
187 188 189 190 191 192
2.553322055 -0.391463876 -0.468734491 0.580687856 -0.713241554 0.582050572
193 194 195 196 197 198
2.269481008 1.994956854 -0.628570597 0.038277629 3.066474539 0.997070790
199 200 201 202 203 204
1.570980621 1.476564003 2.020913293 0.670948723 -2.405240124 -2.427510351
205 206 207 208 209 210
2.304099234 0.579004290 1.542715447 1.150106666 -2.547521027 1.075822786
211 212 213 214 215 216
-2.175381646 -3.688307789 0.900497361 2.932807324 1.512998471 0.969406554
217 218 219 220 221 222
2.427871407 0.120767734 1.126273919 -0.087549794 -1.577012700 0.005359026
223 224 225 226 227 228
0.802026355 -1.039494366 0.433260160 -3.695135417 0.339358634 1.086160183
229 230 231 232 233 234
-1.166321626 -0.818218205 1.808724003 -3.136680595 4.685664825 2.269592673
235 236 237 238 239 240
-0.713568415 -2.212208868 -6.916582701 -1.097183889 1.858485315 -1.590676066
241 242 243 244 245 246
-0.173002681 -1.347837056 1.188173325 2.010175644 1.485771091 0.186548364
247 248 249 250 251 252
1.296266786 -2.330697678 1.361911326 0.447271006 -0.735677772 -1.545609222
253 254 255 256 257 258
0.745840576 3.324417558 -1.149002649 0.339177797 1.779214818 2.505552324
259 260 261 262 263 264
-1.051873737 -5.297891218 1.442625011 -3.776313919 -0.152783633 1.357303915
> postscript(file="/var/wessaorg/rcomp/tmp/612fv1353953016.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 -3.102298221 NA
1 0.245508554 -3.102298221
2 1.948287423 0.245508554
3 2.844541916 1.948287423
4 -2.431877832 2.844541916
5 -1.869129571 -2.431877832
6 3.710736971 -1.869129571
7 -2.098463663 3.710736971
8 -2.062068866 -2.098463663
9 0.543207803 -2.062068866
10 1.009137890 0.543207803
11 -0.177005218 1.009137890
12 0.478496420 -0.177005218
13 0.504384283 0.478496420
14 -1.006512051 0.504384283
15 -0.471600438 -1.006512051
16 0.384029318 -0.471600438
17 3.571776300 0.384029318
18 2.520984906 3.571776300
19 0.444210606 2.520984906
20 0.634956735 0.444210606
21 0.855854122 0.634956735
22 2.517513085 0.855854122
23 0.914481509 2.517513085
24 0.826979440 0.914481509
25 0.730492812 0.826979440
26 1.002205332 0.730492812
27 -1.760323978 1.002205332
28 0.244499178 -1.760323978
29 -0.181416578 0.244499178
30 -0.754683476 -0.181416578
31 -0.834815524 -0.754683476
32 -0.811839163 -0.834815524
33 0.063104612 -0.811839163
34 -1.570370764 0.063104612
35 -3.005541434 -1.570370764
36 -3.074708695 -3.005541434
37 -1.754899768 -3.074708695
38 1.443200553 -1.754899768
39 1.508493443 1.443200553
40 1.277020449 1.508493443
41 -1.750019986 1.277020449
42 2.535021239 -1.750019986
43 -0.162354026 2.535021239
44 -0.465001909 -0.162354026
45 -4.527891846 -0.465001909
46 -2.537742048 -4.527891846
47 -0.138218462 -2.537742048
48 0.491833471 -0.138218462
49 -1.650896199 0.491833471
50 -1.013287553 -1.650896199
51 -0.161379564 -1.013287553
52 -3.053366612 -0.161379564
53 0.177010959 -3.053366612
54 -2.397791027 0.177010959
55 1.614711519 -2.397791027
56 0.133816130 1.614711519
57 0.517006043 0.133816130
58 -0.148945246 0.517006043
59 1.781689396 -0.148945246
60 0.433922695 1.781689396
61 0.304398347 0.433922695
62 -0.568097897 0.304398347
63 -0.714497575 -0.568097897
64 0.708091983 -0.714497575
65 0.951831948 0.708091983
66 1.708070000 0.951831948
67 3.523165161 1.708070000
68 -3.911285460 3.523165161
69 0.344750275 -3.911285460
70 -3.338794504 0.344750275
71 -0.912773043 -3.338794504
72 1.032210414 -0.912773043
73 0.811332375 1.032210414
74 0.735049423 0.811332375
75 3.487857269 0.735049423
76 -0.413649675 3.487857269
77 1.511437148 -0.413649675
78 -2.210529169 1.511437148
79 0.878982388 -2.210529169
80 0.287274495 0.878982388
81 0.287717529 0.287274495
82 -0.683966261 0.287717529
83 0.122140390 -0.683966261
84 1.837417717 0.122140390
85 -0.184805355 1.837417717
86 0.637027251 -0.184805355
87 1.145316210 0.637027251
88 0.476380270 1.145316210
89 -1.871340301 0.476380270
90 0.253960372 -1.871340301
91 0.165281862 0.253960372
92 -0.110267757 0.165281862
93 -2.070740981 -0.110267757
94 1.201561147 -2.070740981
95 0.230086471 1.201561147
96 2.280392742 0.230086471
97 0.185275621 2.280392742
98 -0.433941180 0.185275621
99 -1.025810198 -0.433941180
100 1.325803102 -1.025810198
101 2.544064759 1.325803102
102 0.818154279 2.544064759
103 0.999697148 0.818154279
104 -1.867506176 0.999697148
105 1.311712397 -1.867506176
106 0.277149621 1.311712397
107 1.650845580 0.277149621
108 -0.275456138 1.650845580
109 0.713725048 -0.275456138
110 -0.017651315 0.713725048
111 1.973299951 -0.017651315
112 -1.566454216 1.973299951
113 -2.570117240 -1.566454216
114 1.847860891 -2.570117240
115 -1.850512307 1.847860891
116 0.850437998 -1.850512307
117 -1.782387174 0.850437998
118 0.462550890 -1.782387174
119 -1.416850975 0.462550890
120 0.608832531 -1.416850975
121 -2.839724798 0.608832531
122 -0.830654743 -2.839724798
123 -0.827396197 -0.830654743
124 -0.805322704 -0.827396197
125 0.335034965 -0.805322704
126 1.472209186 0.335034965
127 1.038338342 1.472209186
128 -2.684050075 1.038338342
129 2.035062790 -2.684050075
130 -3.729699165 2.035062790
131 2.509846847 -3.729699165
132 -2.146227277 2.509846847
133 -1.523753324 -2.146227277
134 -0.060594176 -1.523753324
135 0.890344539 -0.060594176
136 0.564325197 0.890344539
137 -2.434249991 0.564325197
138 -0.975933494 -2.434249991
139 -2.359707165 -0.975933494
140 3.164075491 -2.359707165
141 1.342747174 3.164075491
142 0.176053779 1.342747174
143 1.856556734 0.176053779
144 -3.265660580 1.856556734
145 2.524706201 -3.265660580
146 -1.900643556 2.524706201
147 1.172495986 -1.900643556
148 0.197545859 1.172495986
149 -2.887279570 0.197545859
150 -0.927635686 -2.887279570
151 2.124290496 -0.927635686
152 4.147065404 2.124290496
153 1.611980680 4.147065404
154 -2.409036113 1.611980680
155 0.574926510 -2.409036113
156 1.314913659 0.574926510
157 1.186476560 1.314913659
158 1.459952990 1.186476560
159 -0.747215610 1.459952990
160 0.400414580 -0.747215610
161 0.392666584 0.400414580
162 -0.446513743 0.392666584
163 0.764450432 -0.446513743
164 1.300316767 0.764450432
165 -1.613687684 1.300316767
166 -0.327870372 -1.613687684
167 -3.210686251 -0.327870372
168 -1.797873078 -3.210686251
169 1.526714914 -1.797873078
170 1.520357883 1.526714914
171 0.666125952 1.520357883
172 -1.866121650 0.666125952
173 -2.352065885 -1.866121650
174 -2.903882575 -2.352065885
175 0.446600156 -2.903882575
176 -0.261503599 0.446600156
177 -0.679444147 -0.261503599
178 0.224142391 -0.679444147
179 -1.375191100 0.224142391
180 1.012217824 -1.375191100
181 -0.439178441 1.012217824
182 2.347219736 -0.439178441
183 -0.130955841 2.347219736
184 -6.567169685 -0.130955841
185 1.232691977 -6.567169685
186 2.553322055 1.232691977
187 -0.391463876 2.553322055
188 -0.468734491 -0.391463876
189 0.580687856 -0.468734491
190 -0.713241554 0.580687856
191 0.582050572 -0.713241554
192 2.269481008 0.582050572
193 1.994956854 2.269481008
194 -0.628570597 1.994956854
195 0.038277629 -0.628570597
196 3.066474539 0.038277629
197 0.997070790 3.066474539
198 1.570980621 0.997070790
199 1.476564003 1.570980621
200 2.020913293 1.476564003
201 0.670948723 2.020913293
202 -2.405240124 0.670948723
203 -2.427510351 -2.405240124
204 2.304099234 -2.427510351
205 0.579004290 2.304099234
206 1.542715447 0.579004290
207 1.150106666 1.542715447
208 -2.547521027 1.150106666
209 1.075822786 -2.547521027
210 -2.175381646 1.075822786
211 -3.688307789 -2.175381646
212 0.900497361 -3.688307789
213 2.932807324 0.900497361
214 1.512998471 2.932807324
215 0.969406554 1.512998471
216 2.427871407 0.969406554
217 0.120767734 2.427871407
218 1.126273919 0.120767734
219 -0.087549794 1.126273919
220 -1.577012700 -0.087549794
221 0.005359026 -1.577012700
222 0.802026355 0.005359026
223 -1.039494366 0.802026355
224 0.433260160 -1.039494366
225 -3.695135417 0.433260160
226 0.339358634 -3.695135417
227 1.086160183 0.339358634
228 -1.166321626 1.086160183
229 -0.818218205 -1.166321626
230 1.808724003 -0.818218205
231 -3.136680595 1.808724003
232 4.685664825 -3.136680595
233 2.269592673 4.685664825
234 -0.713568415 2.269592673
235 -2.212208868 -0.713568415
236 -6.916582701 -2.212208868
237 -1.097183889 -6.916582701
238 1.858485315 -1.097183889
239 -1.590676066 1.858485315
240 -0.173002681 -1.590676066
241 -1.347837056 -0.173002681
242 1.188173325 -1.347837056
243 2.010175644 1.188173325
244 1.485771091 2.010175644
245 0.186548364 1.485771091
246 1.296266786 0.186548364
247 -2.330697678 1.296266786
248 1.361911326 -2.330697678
249 0.447271006 1.361911326
250 -0.735677772 0.447271006
251 -1.545609222 -0.735677772
252 0.745840576 -1.545609222
253 3.324417558 0.745840576
254 -1.149002649 3.324417558
255 0.339177797 -1.149002649
256 1.779214818 0.339177797
257 2.505552324 1.779214818
258 -1.051873737 2.505552324
259 -5.297891218 -1.051873737
260 1.442625011 -5.297891218
261 -3.776313919 1.442625011
262 -0.152783633 -3.776313919
263 1.357303915 -0.152783633
264 NA 1.357303915
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.245508554 -3.102298221
[2,] 1.948287423 0.245508554
[3,] 2.844541916 1.948287423
[4,] -2.431877832 2.844541916
[5,] -1.869129571 -2.431877832
[6,] 3.710736971 -1.869129571
[7,] -2.098463663 3.710736971
[8,] -2.062068866 -2.098463663
[9,] 0.543207803 -2.062068866
[10,] 1.009137890 0.543207803
[11,] -0.177005218 1.009137890
[12,] 0.478496420 -0.177005218
[13,] 0.504384283 0.478496420
[14,] -1.006512051 0.504384283
[15,] -0.471600438 -1.006512051
[16,] 0.384029318 -0.471600438
[17,] 3.571776300 0.384029318
[18,] 2.520984906 3.571776300
[19,] 0.444210606 2.520984906
[20,] 0.634956735 0.444210606
[21,] 0.855854122 0.634956735
[22,] 2.517513085 0.855854122
[23,] 0.914481509 2.517513085
[24,] 0.826979440 0.914481509
[25,] 0.730492812 0.826979440
[26,] 1.002205332 0.730492812
[27,] -1.760323978 1.002205332
[28,] 0.244499178 -1.760323978
[29,] -0.181416578 0.244499178
[30,] -0.754683476 -0.181416578
[31,] -0.834815524 -0.754683476
[32,] -0.811839163 -0.834815524
[33,] 0.063104612 -0.811839163
[34,] -1.570370764 0.063104612
[35,] -3.005541434 -1.570370764
[36,] -3.074708695 -3.005541434
[37,] -1.754899768 -3.074708695
[38,] 1.443200553 -1.754899768
[39,] 1.508493443 1.443200553
[40,] 1.277020449 1.508493443
[41,] -1.750019986 1.277020449
[42,] 2.535021239 -1.750019986
[43,] -0.162354026 2.535021239
[44,] -0.465001909 -0.162354026
[45,] -4.527891846 -0.465001909
[46,] -2.537742048 -4.527891846
[47,] -0.138218462 -2.537742048
[48,] 0.491833471 -0.138218462
[49,] -1.650896199 0.491833471
[50,] -1.013287553 -1.650896199
[51,] -0.161379564 -1.013287553
[52,] -3.053366612 -0.161379564
[53,] 0.177010959 -3.053366612
[54,] -2.397791027 0.177010959
[55,] 1.614711519 -2.397791027
[56,] 0.133816130 1.614711519
[57,] 0.517006043 0.133816130
[58,] -0.148945246 0.517006043
[59,] 1.781689396 -0.148945246
[60,] 0.433922695 1.781689396
[61,] 0.304398347 0.433922695
[62,] -0.568097897 0.304398347
[63,] -0.714497575 -0.568097897
[64,] 0.708091983 -0.714497575
[65,] 0.951831948 0.708091983
[66,] 1.708070000 0.951831948
[67,] 3.523165161 1.708070000
[68,] -3.911285460 3.523165161
[69,] 0.344750275 -3.911285460
[70,] -3.338794504 0.344750275
[71,] -0.912773043 -3.338794504
[72,] 1.032210414 -0.912773043
[73,] 0.811332375 1.032210414
[74,] 0.735049423 0.811332375
[75,] 3.487857269 0.735049423
[76,] -0.413649675 3.487857269
[77,] 1.511437148 -0.413649675
[78,] -2.210529169 1.511437148
[79,] 0.878982388 -2.210529169
[80,] 0.287274495 0.878982388
[81,] 0.287717529 0.287274495
[82,] -0.683966261 0.287717529
[83,] 0.122140390 -0.683966261
[84,] 1.837417717 0.122140390
[85,] -0.184805355 1.837417717
[86,] 0.637027251 -0.184805355
[87,] 1.145316210 0.637027251
[88,] 0.476380270 1.145316210
[89,] -1.871340301 0.476380270
[90,] 0.253960372 -1.871340301
[91,] 0.165281862 0.253960372
[92,] -0.110267757 0.165281862
[93,] -2.070740981 -0.110267757
[94,] 1.201561147 -2.070740981
[95,] 0.230086471 1.201561147
[96,] 2.280392742 0.230086471
[97,] 0.185275621 2.280392742
[98,] -0.433941180 0.185275621
[99,] -1.025810198 -0.433941180
[100,] 1.325803102 -1.025810198
[101,] 2.544064759 1.325803102
[102,] 0.818154279 2.544064759
[103,] 0.999697148 0.818154279
[104,] -1.867506176 0.999697148
[105,] 1.311712397 -1.867506176
[106,] 0.277149621 1.311712397
[107,] 1.650845580 0.277149621
[108,] -0.275456138 1.650845580
[109,] 0.713725048 -0.275456138
[110,] -0.017651315 0.713725048
[111,] 1.973299951 -0.017651315
[112,] -1.566454216 1.973299951
[113,] -2.570117240 -1.566454216
[114,] 1.847860891 -2.570117240
[115,] -1.850512307 1.847860891
[116,] 0.850437998 -1.850512307
[117,] -1.782387174 0.850437998
[118,] 0.462550890 -1.782387174
[119,] -1.416850975 0.462550890
[120,] 0.608832531 -1.416850975
[121,] -2.839724798 0.608832531
[122,] -0.830654743 -2.839724798
[123,] -0.827396197 -0.830654743
[124,] -0.805322704 -0.827396197
[125,] 0.335034965 -0.805322704
[126,] 1.472209186 0.335034965
[127,] 1.038338342 1.472209186
[128,] -2.684050075 1.038338342
[129,] 2.035062790 -2.684050075
[130,] -3.729699165 2.035062790
[131,] 2.509846847 -3.729699165
[132,] -2.146227277 2.509846847
[133,] -1.523753324 -2.146227277
[134,] -0.060594176 -1.523753324
[135,] 0.890344539 -0.060594176
[136,] 0.564325197 0.890344539
[137,] -2.434249991 0.564325197
[138,] -0.975933494 -2.434249991
[139,] -2.359707165 -0.975933494
[140,] 3.164075491 -2.359707165
[141,] 1.342747174 3.164075491
[142,] 0.176053779 1.342747174
[143,] 1.856556734 0.176053779
[144,] -3.265660580 1.856556734
[145,] 2.524706201 -3.265660580
[146,] -1.900643556 2.524706201
[147,] 1.172495986 -1.900643556
[148,] 0.197545859 1.172495986
[149,] -2.887279570 0.197545859
[150,] -0.927635686 -2.887279570
[151,] 2.124290496 -0.927635686
[152,] 4.147065404 2.124290496
[153,] 1.611980680 4.147065404
[154,] -2.409036113 1.611980680
[155,] 0.574926510 -2.409036113
[156,] 1.314913659 0.574926510
[157,] 1.186476560 1.314913659
[158,] 1.459952990 1.186476560
[159,] -0.747215610 1.459952990
[160,] 0.400414580 -0.747215610
[161,] 0.392666584 0.400414580
[162,] -0.446513743 0.392666584
[163,] 0.764450432 -0.446513743
[164,] 1.300316767 0.764450432
[165,] -1.613687684 1.300316767
[166,] -0.327870372 -1.613687684
[167,] -3.210686251 -0.327870372
[168,] -1.797873078 -3.210686251
[169,] 1.526714914 -1.797873078
[170,] 1.520357883 1.526714914
[171,] 0.666125952 1.520357883
[172,] -1.866121650 0.666125952
[173,] -2.352065885 -1.866121650
[174,] -2.903882575 -2.352065885
[175,] 0.446600156 -2.903882575
[176,] -0.261503599 0.446600156
[177,] -0.679444147 -0.261503599
[178,] 0.224142391 -0.679444147
[179,] -1.375191100 0.224142391
[180,] 1.012217824 -1.375191100
[181,] -0.439178441 1.012217824
[182,] 2.347219736 -0.439178441
[183,] -0.130955841 2.347219736
[184,] -6.567169685 -0.130955841
[185,] 1.232691977 -6.567169685
[186,] 2.553322055 1.232691977
[187,] -0.391463876 2.553322055
[188,] -0.468734491 -0.391463876
[189,] 0.580687856 -0.468734491
[190,] -0.713241554 0.580687856
[191,] 0.582050572 -0.713241554
[192,] 2.269481008 0.582050572
[193,] 1.994956854 2.269481008
[194,] -0.628570597 1.994956854
[195,] 0.038277629 -0.628570597
[196,] 3.066474539 0.038277629
[197,] 0.997070790 3.066474539
[198,] 1.570980621 0.997070790
[199,] 1.476564003 1.570980621
[200,] 2.020913293 1.476564003
[201,] 0.670948723 2.020913293
[202,] -2.405240124 0.670948723
[203,] -2.427510351 -2.405240124
[204,] 2.304099234 -2.427510351
[205,] 0.579004290 2.304099234
[206,] 1.542715447 0.579004290
[207,] 1.150106666 1.542715447
[208,] -2.547521027 1.150106666
[209,] 1.075822786 -2.547521027
[210,] -2.175381646 1.075822786
[211,] -3.688307789 -2.175381646
[212,] 0.900497361 -3.688307789
[213,] 2.932807324 0.900497361
[214,] 1.512998471 2.932807324
[215,] 0.969406554 1.512998471
[216,] 2.427871407 0.969406554
[217,] 0.120767734 2.427871407
[218,] 1.126273919 0.120767734
[219,] -0.087549794 1.126273919
[220,] -1.577012700 -0.087549794
[221,] 0.005359026 -1.577012700
[222,] 0.802026355 0.005359026
[223,] -1.039494366 0.802026355
[224,] 0.433260160 -1.039494366
[225,] -3.695135417 0.433260160
[226,] 0.339358634 -3.695135417
[227,] 1.086160183 0.339358634
[228,] -1.166321626 1.086160183
[229,] -0.818218205 -1.166321626
[230,] 1.808724003 -0.818218205
[231,] -3.136680595 1.808724003
[232,] 4.685664825 -3.136680595
[233,] 2.269592673 4.685664825
[234,] -0.713568415 2.269592673
[235,] -2.212208868 -0.713568415
[236,] -6.916582701 -2.212208868
[237,] -1.097183889 -6.916582701
[238,] 1.858485315 -1.097183889
[239,] -1.590676066 1.858485315
[240,] -0.173002681 -1.590676066
[241,] -1.347837056 -0.173002681
[242,] 1.188173325 -1.347837056
[243,] 2.010175644 1.188173325
[244,] 1.485771091 2.010175644
[245,] 0.186548364 1.485771091
[246,] 1.296266786 0.186548364
[247,] -2.330697678 1.296266786
[248,] 1.361911326 -2.330697678
[249,] 0.447271006 1.361911326
[250,] -0.735677772 0.447271006
[251,] -1.545609222 -0.735677772
[252,] 0.745840576 -1.545609222
[253,] 3.324417558 0.745840576
[254,] -1.149002649 3.324417558
[255,] 0.339177797 -1.149002649
[256,] 1.779214818 0.339177797
[257,] 2.505552324 1.779214818
[258,] -1.051873737 2.505552324
[259,] -5.297891218 -1.051873737
[260,] 1.442625011 -5.297891218
[261,] -3.776313919 1.442625011
[262,] -0.152783633 -3.776313919
[263,] 1.357303915 -0.152783633
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.245508554 -3.102298221
2 1.948287423 0.245508554
3 2.844541916 1.948287423
4 -2.431877832 2.844541916
5 -1.869129571 -2.431877832
6 3.710736971 -1.869129571
7 -2.098463663 3.710736971
8 -2.062068866 -2.098463663
9 0.543207803 -2.062068866
10 1.009137890 0.543207803
11 -0.177005218 1.009137890
12 0.478496420 -0.177005218
13 0.504384283 0.478496420
14 -1.006512051 0.504384283
15 -0.471600438 -1.006512051
16 0.384029318 -0.471600438
17 3.571776300 0.384029318
18 2.520984906 3.571776300
19 0.444210606 2.520984906
20 0.634956735 0.444210606
21 0.855854122 0.634956735
22 2.517513085 0.855854122
23 0.914481509 2.517513085
24 0.826979440 0.914481509
25 0.730492812 0.826979440
26 1.002205332 0.730492812
27 -1.760323978 1.002205332
28 0.244499178 -1.760323978
29 -0.181416578 0.244499178
30 -0.754683476 -0.181416578
31 -0.834815524 -0.754683476
32 -0.811839163 -0.834815524
33 0.063104612 -0.811839163
34 -1.570370764 0.063104612
35 -3.005541434 -1.570370764
36 -3.074708695 -3.005541434
37 -1.754899768 -3.074708695
38 1.443200553 -1.754899768
39 1.508493443 1.443200553
40 1.277020449 1.508493443
41 -1.750019986 1.277020449
42 2.535021239 -1.750019986
43 -0.162354026 2.535021239
44 -0.465001909 -0.162354026
45 -4.527891846 -0.465001909
46 -2.537742048 -4.527891846
47 -0.138218462 -2.537742048
48 0.491833471 -0.138218462
49 -1.650896199 0.491833471
50 -1.013287553 -1.650896199
51 -0.161379564 -1.013287553
52 -3.053366612 -0.161379564
53 0.177010959 -3.053366612
54 -2.397791027 0.177010959
55 1.614711519 -2.397791027
56 0.133816130 1.614711519
57 0.517006043 0.133816130
58 -0.148945246 0.517006043
59 1.781689396 -0.148945246
60 0.433922695 1.781689396
61 0.304398347 0.433922695
62 -0.568097897 0.304398347
63 -0.714497575 -0.568097897
64 0.708091983 -0.714497575
65 0.951831948 0.708091983
66 1.708070000 0.951831948
67 3.523165161 1.708070000
68 -3.911285460 3.523165161
69 0.344750275 -3.911285460
70 -3.338794504 0.344750275
71 -0.912773043 -3.338794504
72 1.032210414 -0.912773043
73 0.811332375 1.032210414
74 0.735049423 0.811332375
75 3.487857269 0.735049423
76 -0.413649675 3.487857269
77 1.511437148 -0.413649675
78 -2.210529169 1.511437148
79 0.878982388 -2.210529169
80 0.287274495 0.878982388
81 0.287717529 0.287274495
82 -0.683966261 0.287717529
83 0.122140390 -0.683966261
84 1.837417717 0.122140390
85 -0.184805355 1.837417717
86 0.637027251 -0.184805355
87 1.145316210 0.637027251
88 0.476380270 1.145316210
89 -1.871340301 0.476380270
90 0.253960372 -1.871340301
91 0.165281862 0.253960372
92 -0.110267757 0.165281862
93 -2.070740981 -0.110267757
94 1.201561147 -2.070740981
95 0.230086471 1.201561147
96 2.280392742 0.230086471
97 0.185275621 2.280392742
98 -0.433941180 0.185275621
99 -1.025810198 -0.433941180
100 1.325803102 -1.025810198
101 2.544064759 1.325803102
102 0.818154279 2.544064759
103 0.999697148 0.818154279
104 -1.867506176 0.999697148
105 1.311712397 -1.867506176
106 0.277149621 1.311712397
107 1.650845580 0.277149621
108 -0.275456138 1.650845580
109 0.713725048 -0.275456138
110 -0.017651315 0.713725048
111 1.973299951 -0.017651315
112 -1.566454216 1.973299951
113 -2.570117240 -1.566454216
114 1.847860891 -2.570117240
115 -1.850512307 1.847860891
116 0.850437998 -1.850512307
117 -1.782387174 0.850437998
118 0.462550890 -1.782387174
119 -1.416850975 0.462550890
120 0.608832531 -1.416850975
121 -2.839724798 0.608832531
122 -0.830654743 -2.839724798
123 -0.827396197 -0.830654743
124 -0.805322704 -0.827396197
125 0.335034965 -0.805322704
126 1.472209186 0.335034965
127 1.038338342 1.472209186
128 -2.684050075 1.038338342
129 2.035062790 -2.684050075
130 -3.729699165 2.035062790
131 2.509846847 -3.729699165
132 -2.146227277 2.509846847
133 -1.523753324 -2.146227277
134 -0.060594176 -1.523753324
135 0.890344539 -0.060594176
136 0.564325197 0.890344539
137 -2.434249991 0.564325197
138 -0.975933494 -2.434249991
139 -2.359707165 -0.975933494
140 3.164075491 -2.359707165
141 1.342747174 3.164075491
142 0.176053779 1.342747174
143 1.856556734 0.176053779
144 -3.265660580 1.856556734
145 2.524706201 -3.265660580
146 -1.900643556 2.524706201
147 1.172495986 -1.900643556
148 0.197545859 1.172495986
149 -2.887279570 0.197545859
150 -0.927635686 -2.887279570
151 2.124290496 -0.927635686
152 4.147065404 2.124290496
153 1.611980680 4.147065404
154 -2.409036113 1.611980680
155 0.574926510 -2.409036113
156 1.314913659 0.574926510
157 1.186476560 1.314913659
158 1.459952990 1.186476560
159 -0.747215610 1.459952990
160 0.400414580 -0.747215610
161 0.392666584 0.400414580
162 -0.446513743 0.392666584
163 0.764450432 -0.446513743
164 1.300316767 0.764450432
165 -1.613687684 1.300316767
166 -0.327870372 -1.613687684
167 -3.210686251 -0.327870372
168 -1.797873078 -3.210686251
169 1.526714914 -1.797873078
170 1.520357883 1.526714914
171 0.666125952 1.520357883
172 -1.866121650 0.666125952
173 -2.352065885 -1.866121650
174 -2.903882575 -2.352065885
175 0.446600156 -2.903882575
176 -0.261503599 0.446600156
177 -0.679444147 -0.261503599
178 0.224142391 -0.679444147
179 -1.375191100 0.224142391
180 1.012217824 -1.375191100
181 -0.439178441 1.012217824
182 2.347219736 -0.439178441
183 -0.130955841 2.347219736
184 -6.567169685 -0.130955841
185 1.232691977 -6.567169685
186 2.553322055 1.232691977
187 -0.391463876 2.553322055
188 -0.468734491 -0.391463876
189 0.580687856 -0.468734491
190 -0.713241554 0.580687856
191 0.582050572 -0.713241554
192 2.269481008 0.582050572
193 1.994956854 2.269481008
194 -0.628570597 1.994956854
195 0.038277629 -0.628570597
196 3.066474539 0.038277629
197 0.997070790 3.066474539
198 1.570980621 0.997070790
199 1.476564003 1.570980621
200 2.020913293 1.476564003
201 0.670948723 2.020913293
202 -2.405240124 0.670948723
203 -2.427510351 -2.405240124
204 2.304099234 -2.427510351
205 0.579004290 2.304099234
206 1.542715447 0.579004290
207 1.150106666 1.542715447
208 -2.547521027 1.150106666
209 1.075822786 -2.547521027
210 -2.175381646 1.075822786
211 -3.688307789 -2.175381646
212 0.900497361 -3.688307789
213 2.932807324 0.900497361
214 1.512998471 2.932807324
215 0.969406554 1.512998471
216 2.427871407 0.969406554
217 0.120767734 2.427871407
218 1.126273919 0.120767734
219 -0.087549794 1.126273919
220 -1.577012700 -0.087549794
221 0.005359026 -1.577012700
222 0.802026355 0.005359026
223 -1.039494366 0.802026355
224 0.433260160 -1.039494366
225 -3.695135417 0.433260160
226 0.339358634 -3.695135417
227 1.086160183 0.339358634
228 -1.166321626 1.086160183
229 -0.818218205 -1.166321626
230 1.808724003 -0.818218205
231 -3.136680595 1.808724003
232 4.685664825 -3.136680595
233 2.269592673 4.685664825
234 -0.713568415 2.269592673
235 -2.212208868 -0.713568415
236 -6.916582701 -2.212208868
237 -1.097183889 -6.916582701
238 1.858485315 -1.097183889
239 -1.590676066 1.858485315
240 -0.173002681 -1.590676066
241 -1.347837056 -0.173002681
242 1.188173325 -1.347837056
243 2.010175644 1.188173325
244 1.485771091 2.010175644
245 0.186548364 1.485771091
246 1.296266786 0.186548364
247 -2.330697678 1.296266786
248 1.361911326 -2.330697678
249 0.447271006 1.361911326
250 -0.735677772 0.447271006
251 -1.545609222 -0.735677772
252 0.745840576 -1.545609222
253 3.324417558 0.745840576
254 -1.149002649 3.324417558
255 0.339177797 -1.149002649
256 1.779214818 0.339177797
257 2.505552324 1.779214818
258 -1.051873737 2.505552324
259 -5.297891218 -1.051873737
260 1.442625011 -5.297891218
261 -3.776313919 1.442625011
262 -0.152783633 -3.776313919
263 1.357303915 -0.152783633
> 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/7tggm1353953016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8n6il1353953016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9yoey1353953016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10lfuv1353953016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111uhs1353953016.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/12r0ma1353953016.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/13wun61353953016.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/14ancu1353953016.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/15id5h1353953016.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/16wj1v1353953016.tab")
+ }
>
> try(system("convert tmp/1ymya1353953016.ps tmp/1ymya1353953016.png",intern=TRUE))
character(0)
> try(system("convert tmp/2aij41353953016.ps tmp/2aij41353953016.png",intern=TRUE))
character(0)
> try(system("convert tmp/33zvk1353953016.ps tmp/33zvk1353953016.png",intern=TRUE))
character(0)
> try(system("convert tmp/4d4xd1353953016.ps tmp/4d4xd1353953016.png",intern=TRUE))
character(0)
> try(system("convert tmp/576no1353953016.ps tmp/576no1353953016.png",intern=TRUE))
character(0)
> try(system("convert tmp/612fv1353953016.ps tmp/612fv1353953016.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tggm1353953016.ps tmp/7tggm1353953016.png",intern=TRUE))
character(0)
> try(system("convert tmp/8n6il1353953016.ps tmp/8n6il1353953016.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yoey1353953016.ps tmp/9yoey1353953016.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lfuv1353953016.ps tmp/10lfuv1353953016.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.463 1.063 12.876