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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+ ,4
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+ ,41
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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])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
month Connected Separate Learning Software Happiness Depression Belonging
1 9 41 38 13 12 14 12.0 53
2 9 39 32 16 11 18 11.0 83
3 9 30 35 19 15 11 14.0 66
4 9 31 33 15 6 12 12.0 67
5 9 34 37 14 13 16 21.0 76
6 9 35 29 13 10 18 12.0 78
7 9 39 31 19 12 14 22.0 53
8 9 34 36 15 14 14 11.0 80
9 9 36 35 14 12 15 10.0 74
10 9 37 38 15 9 15 13.0 76
11 9 38 31 16 10 17 10.0 79
12 9 36 34 16 12 19 8.0 54
13 9 38 35 16 12 10 15.0 67
14 9 39 38 16 11 16 14.0 54
15 9 33 37 17 15 18 10.0 87
16 9 32 33 15 12 14 14.0 58
17 9 36 32 15 10 14 14.0 75
18 9 38 38 20 12 17 11.0 88
19 9 39 38 18 11 14 10.0 64
20 9 32 32 16 12 16 13.0 57
21 9 32 33 16 11 18 9.5 66
22 9 31 31 16 12 11 14.0 68
23 9 39 38 19 13 14 12.0 54
24 9 37 39 16 11 12 14.0 56
25 9 39 32 17 12 17 11.0 86
26 9 41 32 17 13 9 9.0 80
27 9 36 35 16 10 16 11.0 76
28 9 33 37 15 14 14 15.0 69
29 9 33 33 16 12 15 14.0 78
30 9 34 33 14 10 11 13.0 67
31 9 31 31 15 12 16 9.0 80
32 9 27 32 12 8 13 15.0 54
33 9 37 31 14 10 17 10.0 71
34 9 34 37 16 12 15 11.0 84
35 9 34 30 14 12 14 13.0 74
36 9 32 33 10 7 16 8.0 71
37 9 29 31 10 9 9 20.0 63
38 9 36 33 14 12 15 12.0 71
39 9 29 31 16 10 17 10.0 76
40 9 35 33 16 10 13 10.0 69
41 9 37 32 16 10 15 9.0 74
42 9 34 33 14 12 16 14.0 75
43 9 38 32 20 15 16 8.0 54
44 9 35 33 14 10 12 14.0 52
45 9 38 28 14 10 15 11.0 69
46 9 37 35 11 12 11 13.0 68
47 9 38 39 14 13 15 9.0 65
48 9 33 34 15 11 15 11.0 75
49 9 36 38 16 11 17 15.0 74
50 9 38 32 14 12 13 11.0 75
51 9 32 38 16 14 16 10.0 72
52 9 32 30 14 10 14 14.0 67
53 9 32 33 12 12 11 18.0 63
54 9 34 38 16 13 12 14.0 62
55 9 32 32 9 5 12 11.0 63
56 9 37 35 14 6 15 14.5 76
57 9 39 34 16 12 16 13.0 74
58 9 29 34 16 12 15 9.0 67
59 9 37 36 15 11 12 10.0 73
60 9 35 34 16 10 12 15.0 70
61 9 30 28 12 7 8 20.0 53
62 9 38 34 16 12 13 12.0 77
63 9 34 35 16 14 11 12.0 80
64 9 31 35 14 11 14 14.0 52
65 9 34 31 16 12 15 13.0 54
66 10 35 37 17 13 10 11.0 80
67 10 36 35 18 14 11 17.0 66
68 10 30 27 18 11 12 12.0 73
69 10 39 40 12 12 15 13.0 63
70 10 35 37 16 12 15 14.0 69
71 10 38 36 10 8 14 13.0 67
72 10 31 38 14 11 16 15.0 54
73 10 34 39 18 14 15 13.0 81
74 10 38 41 18 14 15 10.0 69
75 10 34 27 16 12 13 11.0 84
76 10 39 30 17 9 12 19.0 80
77 10 37 37 16 13 17 13.0 70
78 10 34 31 16 11 13 17.0 69
79 10 28 31 13 12 15 13.0 77
80 10 37 27 16 12 13 9.0 54
81 10 33 36 16 12 15 11.0 79
82 10 35 37 16 12 15 9.0 71
83 10 37 33 15 12 16 12.0 73
84 10 32 34 15 11 15 12.0 72
85 10 33 31 16 10 14 13.0 77
86 10 38 39 14 9 15 13.0 75
87 10 33 34 16 12 14 12.0 69
88 10 29 32 16 12 13 15.0 54
89 10 33 33 15 12 7 22.0 70
90 10 31 36 12 9 17 13.0 73
91 10 36 32 17 15 13 15.0 54
92 10 35 41 16 12 15 13.0 77
93 10 32 28 15 12 14 15.0 82
94 10 29 30 13 12 13 12.5 80
95 10 39 36 16 10 16 11.0 80
96 10 37 35 16 13 12 16.0 69
97 10 35 31 16 9 14 11.0 78
98 10 37 34 16 12 17 11.0 81
99 10 32 36 14 10 15 10.0 76
100 10 38 36 16 14 17 10.0 76
101 10 37 35 16 11 12 16.0 73
102 10 36 37 20 15 16 12.0 85
103 10 32 28 15 11 11 11.0 66
104 10 33 39 16 11 15 16.0 79
105 10 40 32 13 12 9 19.0 68
106 10 38 35 17 12 16 11.0 76
107 10 41 39 16 12 15 16.0 71
108 10 36 35 16 11 10 15.0 54
109 10 43 42 12 7 10 24.0 46
110 10 30 34 16 12 15 14.0 85
111 10 31 33 16 14 11 15.0 74
112 10 32 41 17 11 13 11.0 88
113 10 32 33 13 11 14 15.0 38
114 10 37 34 12 10 18 12.0 76
115 10 37 32 18 13 16 10.0 86
116 10 33 40 14 13 14 14.0 54
117 10 34 40 14 8 14 13.0 67
118 10 33 35 13 11 14 9.0 69
119 10 38 36 16 12 14 15.0 90
120 10 33 37 13 11 12 15.0 54
121 10 31 27 16 13 14 14.0 76
122 10 38 39 13 12 15 11.0 89
123 10 37 38 16 14 15 8.0 76
124 10 36 31 15 13 15 11.0 73
125 10 31 33 16 15 13 11.0 79
126 10 39 32 15 10 17 8.0 90
127 10 44 39 17 11 17 10.0 74
128 10 33 36 15 9 19 11.0 81
129 10 35 33 12 11 15 13.0 72
130 10 32 33 16 10 13 11.0 71
131 10 28 32 10 11 9 20.0 66
132 10 40 37 16 8 15 10.0 77
133 10 27 30 12 11 15 15.0 65
134 10 37 38 14 12 15 12.0 74
135 10 32 29 15 12 16 14.0 85
136 10 28 22 13 9 11 23.0 54
137 10 34 35 15 11 14 14.0 63
138 10 30 35 11 10 11 16.0 54
139 10 35 34 12 8 15 11.0 64
140 10 31 35 11 9 13 12.0 69
141 10 32 34 16 8 15 10.0 54
142 10 30 37 15 9 16 14.0 84
143 10 30 35 17 15 14 12.0 86
144 10 31 23 16 11 15 12.0 77
145 10 40 31 10 8 16 11.0 89
146 10 32 27 18 13 16 12.0 76
147 10 36 36 13 12 11 13.0 60
148 10 32 31 16 12 12 11.0 75
149 10 35 32 13 9 9 19.0 73
150 10 38 39 10 7 16 12.0 85
151 10 42 37 15 13 13 17.0 79
152 10 34 38 16 9 16 9.0 71
153 10 35 39 16 6 12 12.0 72
154 9 38 34 14 8 9 19.0 69
155 10 33 31 10 8 13 18.0 78
156 10 36 32 17 15 13 15.0 54
157 10 32 37 13 6 14 14.0 69
158 10 33 36 15 9 19 11.0 81
159 10 34 32 16 11 13 9.0 84
160 10 32 38 12 8 12 18.0 84
161 10 34 36 13 8 13 16.0 69
162 11 27 26 13 10 10 24.0 66
163 11 31 26 12 8 14 14.0 81
164 11 38 33 17 14 16 20.0 82
165 11 34 39 15 10 10 18.0 72
166 11 24 30 10 8 11 23.0 54
167 11 30 33 14 11 14 12.0 78
168 11 26 25 11 12 12 14.0 74
169 11 34 38 13 12 9 16.0 82
170 11 27 37 16 12 9 18.0 73
171 11 37 31 12 5 11 20.0 55
172 11 36 37 16 12 16 12.0 72
173 11 41 35 12 10 9 12.0 78
174 11 29 25 9 7 13 17.0 59
175 11 36 28 12 12 16 13.0 72
176 11 32 35 15 11 13 9.0 78
177 11 37 33 12 8 9 16.0 68
178 11 30 30 12 9 12 18.0 69
179 11 31 31 14 10 16 10.0 67
180 11 38 37 12 9 11 14.0 74
181 11 36 36 16 12 14 11.0 54
182 11 35 30 11 6 13 9.0 67
183 11 31 36 19 15 15 11.0 70
184 11 38 32 15 12 14 10.0 80
185 11 22 28 8 12 16 11.0 89
186 11 32 36 16 12 13 19.0 76
187 11 36 34 17 11 14 14.0 74
188 11 39 31 12 7 15 12.0 87
189 11 28 28 11 7 13 14.0 54
190 11 32 36 11 5 11 21.0 61
191 11 32 36 14 12 11 13.0 38
192 11 38 40 16 12 14 10.0 75
193 11 32 33 12 3 15 15.0 69
194 11 35 37 16 11 11 16.0 62
195 11 32 32 13 10 15 14.0 72
196 11 37 38 15 12 12 12.0 70
197 11 34 31 16 9 14 19.0 79
198 11 33 37 16 12 14 15.0 87
199 11 33 33 14 9 8 19.0 62
200 11 26 32 16 12 13 13.0 77
201 11 30 30 16 12 9 17.0 69
202 11 24 30 14 10 15 12.0 69
203 11 34 31 11 9 17 11.0 75
204 11 34 32 12 12 13 14.0 54
205 11 33 34 15 8 15 11.0 72
206 11 34 36 15 11 15 13.0 74
207 11 35 37 16 11 14 12.0 85
208 11 35 36 16 12 16 15.0 52
209 11 36 33 11 10 13 14.0 70
210 11 34 33 15 10 16 12.0 84
211 11 34 33 12 12 9 17.0 64
212 11 41 44 12 12 16 11.0 84
213 11 32 39 15 11 11 18.0 87
214 11 30 32 15 8 10 13.0 79
215 11 35 35 16 12 11 17.0 67
216 11 28 25 14 10 15 13.0 65
217 11 33 35 17 11 17 11.0 85
218 11 39 34 14 10 14 12.0 83
219 11 36 35 13 8 8 22.0 61
220 11 36 39 15 12 15 14.0 82
221 11 35 33 13 12 11 12.0 76
222 11 38 36 14 10 16 12.0 58
223 11 33 32 15 12 10 17.0 72
224 11 31 32 12 9 15 9.0 72
225 11 34 36 13 9 9 21.0 38
226 11 32 36 8 6 16 10.0 78
227 11 31 32 14 10 19 11.0 54
228 11 33 34 14 9 12 12.0 63
229 11 34 33 11 9 8 23.0 66
230 11 34 35 12 9 11 13.0 70
231 11 34 30 13 6 14 12.0 71
232 11 33 38 10 10 9 16.0 67
233 11 32 34 16 6 15 9.0 58
234 11 41 33 18 14 13 17.0 72
235 11 34 32 13 10 16 9.0 72
236 11 36 31 11 10 11 14.0 70
237 11 37 30 4 6 12 17.0 76
238 11 36 27 13 12 13 13.0 50
239 11 29 31 16 12 10 11.0 72
240 11 37 30 10 7 11 12.0 72
241 11 27 32 12 8 12 10.0 88
242 11 35 35 12 11 8 19.0 53
243 11 28 28 10 3 12 16.0 58
244 11 35 33 13 6 12 16.0 66
245 11 37 31 15 10 15 14.0 82
246 11 29 35 12 8 11 20.0 69
247 11 32 35 14 9 13 15.0 68
248 11 36 32 10 9 14 23.0 44
249 11 19 21 12 8 10 20.0 56
250 11 21 20 12 9 12 16.0 53
251 11 31 34 11 7 15 14.0 70
252 11 33 32 10 7 13 17.0 78
253 11 36 34 12 6 13 11.0 71
254 11 33 32 16 9 13 13.0 72
255 11 37 33 12 10 12 17.0 68
256 11 34 33 14 11 12 15.0 67
257 11 35 37 16 12 9 21.0 75
258 11 31 32 14 8 9 18.0 62
259 11 37 34 13 11 15 15.0 67
260 11 35 30 4 3 10 8.0 83
261 11 27 30 15 11 14 12.0 64
262 11 34 38 11 12 15 12.0 68
263 11 40 36 11 7 7 22.0 62
264 11 29 32 14 9 14 12.0 72
Belonging_Final t
1 32 1
2 51 2
3 42 3
4 41 4
5 46 5
6 47 6
7 37 7
8 49 8
9 45 9
10 47 10
11 49 11
12 33 12
13 42 13
14 33 14
15 53 15
16 36 16
17 45 17
18 54 18
19 41 19
20 36 20
21 41 21
22 44 22
23 33 23
24 37 24
25 52 25
26 47 26
27 43 27
28 44 28
29 45 29
30 44 30
31 49 31
32 33 32
33 43 33
34 54 34
35 42 35
36 44 36
37 37 37
38 43 38
39 46 39
40 42 40
41 45 41
42 44 42
43 33 43
44 31 44
45 42 45
46 40 46
47 43 47
48 46 48
49 42 49
50 45 50
51 44 51
52 40 52
53 37 53
54 46 54
55 36 55
56 47 56
57 45 57
58 42 58
59 43 59
60 43 60
61 32 61
62 45 62
63 48 63
64 31 64
65 33 65
66 49 66
67 42 67
68 41 68
69 38 69
70 42 70
71 44 71
72 33 72
73 48 73
74 40 74
75 50 75
76 49 76
77 43 77
78 44 78
79 47 79
80 33 80
81 46 81
82 45 82
83 43 83
84 44 84
85 47 85
86 45 86
87 42 87
88 33 88
89 43 89
90 46 90
91 33 91
92 46 92
93 48 93
94 47 94
95 47 95
96 43 96
97 46 97
98 48 98
99 46 99
100 45 100
101 45 101
102 52 102
103 42 103
104 47 104
105 41 105
106 47 106
107 43 107
108 33 108
109 30 109
110 52 110
111 44 111
112 55 112
113 11 113
114 47 114
115 53 115
116 33 116
117 44 117
118 42 118
119 55 119
120 33 120
121 46 121
122 54 122
123 47 123
124 45 124
125 47 125
126 55 126
127 44 127
128 53 128
129 44 129
130 42 130
131 40 131
132 46 132
133 40 133
134 46 134
135 53 135
136 33 136
137 42 137
138 35 138
139 40 139
140 41 140
141 33 141
142 51 142
143 53 143
144 46 144
145 55 145
146 47 146
147 38 147
148 46 148
149 46 149
150 53 150
151 47 151
152 41 152
153 44 153
154 43 154
155 51 155
156 33 156
157 43 157
158 53 158
159 51 159
160 50 160
161 46 161
162 43 162
163 47 163
164 50 164
165 43 165
166 33 166
167 48 167
168 44 168
169 50 169
170 41 170
171 34 171
172 44 172
173 47 173
174 35 174
175 44 175
176 44 176
177 43 177
178 41 178
179 41 179
180 42 180
181 33 181
182 41 182
183 44 183
184 48 184
185 55 185
186 44 186
187 43 187
188 52 188
189 30 189
190 39 190
191 11 191
192 44 192
193 42 193
194 41 194
195 44 195
196 44 196
197 48 197
198 53 198
199 37 199
200 44 200
201 44 201
202 40 202
203 42 203
204 35 204
205 43 205
206 45 206
207 55 207
208 31 208
209 44 209
210 50 210
211 40 211
212 53 212
213 54 213
214 49 214
215 40 215
216 41 216
217 52 217
218 52 218
219 36 219
220 52 220
221 46 221
222 31 222
223 44 223
224 44 224
225 11 225
226 46 226
227 33 227
228 34 228
229 42 229
230 43 230
231 43 231
232 44 232
233 36 233
234 46 234
235 44 235
236 43 236
237 50 237
238 33 238
239 43 239
240 44 240
241 53 241
242 34 242
243 35 243
244 40 244
245 53 245
246 42 246
247 43 247
248 29 248
249 36 249
250 30 250
251 42 251
252 47 252
253 44 253
254 45 254
255 44 255
256 43 256
257 43 257
258 40 258
259 41 259
260 52 260
261 38 261
262 41 262
263 39 263
264 43 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning
8.202389 -0.003467 0.001352 0.003810
Software Happiness Depression Belonging
0.019314 0.007948 0.012380 0.015397
Belonging_Final t
-0.021062 0.009782
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.29437 -0.21266 -0.02041 0.19546 0.61648
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.2023892 0.3060543 26.800 < 2e-16 ***
Connected -0.0034669 0.0053269 -0.651 0.51574
Separate 0.0013519 0.0054162 0.250 0.80310
Learning 0.0038098 0.0096262 0.396 0.69260
Software 0.0193143 0.0098664 1.958 0.05137 .
Happiness 0.0079476 0.0089276 0.890 0.37419
Depression 0.0123800 0.0064758 1.912 0.05703 .
Belonging 0.0153971 0.0057513 2.677 0.00791 **
Belonging_Final -0.0210622 0.0085594 -2.461 0.01453 *
t 0.0097820 0.0002632 37.171 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2855 on 254 degrees of freedom
Multiple R-squared: 0.8736, Adjusted R-squared: 0.8692
F-statistic: 195.1 on 9 and 254 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,] 2.280314e-45 4.560629e-45 1.000000e+00
[2,] 3.520748e-58 7.041496e-58 1.000000e+00
[3,] 1.372402e-71 2.744804e-71 1.000000e+00
[4,] 0.000000e+00 0.000000e+00 1.000000e+00
[5,] 2.446611e-100 4.893222e-100 1.000000e+00
[6,] 1.174623e-115 2.349245e-115 1.000000e+00
[7,] 8.406288e-131 1.681258e-130 1.000000e+00
[8,] 1.573626e-151 3.147251e-151 1.000000e+00
[9,] 2.224920e-161 4.449839e-161 1.000000e+00
[10,] 7.260871e-174 1.452174e-173 1.000000e+00
[11,] 1.225645e-185 2.451290e-185 1.000000e+00
[12,] 3.292103e-207 6.584207e-207 1.000000e+00
[13,] 1.079954e-220 2.159908e-220 1.000000e+00
[14,] 3.147537e-238 6.295075e-238 1.000000e+00
[15,] 9.972773e-249 1.994555e-248 1.000000e+00
[16,] 6.081782e-262 1.216356e-261 1.000000e+00
[17,] 5.666677e-286 1.133335e-285 1.000000e+00
[18,] 2.555065e-283 5.110130e-283 1.000000e+00
[19,] 1.496870e-306 2.993741e-306 1.000000e+00
[20,] 4.940656e-324 9.881313e-324 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 0.000000e+00 0.000000e+00 1.000000e+00
[24,] 0.000000e+00 0.000000e+00 1.000000e+00
[25,] 0.000000e+00 0.000000e+00 1.000000e+00
[26,] 0.000000e+00 0.000000e+00 1.000000e+00
[27,] 0.000000e+00 0.000000e+00 1.000000e+00
[28,] 0.000000e+00 0.000000e+00 1.000000e+00
[29,] 0.000000e+00 0.000000e+00 1.000000e+00
[30,] 0.000000e+00 0.000000e+00 1.000000e+00
[31,] 0.000000e+00 0.000000e+00 1.000000e+00
[32,] 0.000000e+00 0.000000e+00 1.000000e+00
[33,] 0.000000e+00 0.000000e+00 1.000000e+00
[34,] 0.000000e+00 0.000000e+00 1.000000e+00
[35,] 0.000000e+00 0.000000e+00 1.000000e+00
[36,] 0.000000e+00 0.000000e+00 1.000000e+00
[37,] 0.000000e+00 0.000000e+00 1.000000e+00
[38,] 0.000000e+00 0.000000e+00 1.000000e+00
[39,] 0.000000e+00 0.000000e+00 1.000000e+00
[40,] 0.000000e+00 0.000000e+00 1.000000e+00
[41,] 0.000000e+00 0.000000e+00 1.000000e+00
[42,] 0.000000e+00 0.000000e+00 1.000000e+00
[43,] 0.000000e+00 0.000000e+00 1.000000e+00
[44,] 0.000000e+00 0.000000e+00 1.000000e+00
[45,] 0.000000e+00 0.000000e+00 1.000000e+00
[46,] 0.000000e+00 0.000000e+00 1.000000e+00
[47,] 0.000000e+00 0.000000e+00 1.000000e+00
[48,] 0.000000e+00 0.000000e+00 1.000000e+00
[49,] 0.000000e+00 0.000000e+00 1.000000e+00
[50,] 0.000000e+00 0.000000e+00 1.000000e+00
[51,] 0.000000e+00 0.000000e+00 1.000000e+00
[52,] 0.000000e+00 0.000000e+00 1.000000e+00
[53,] 0.000000e+00 0.000000e+00 1.000000e+00
[54,] 2.792190e-10 5.584379e-10 1.000000e+00
[55,] 1.500292e-05 3.000583e-05 9.999850e-01
[56,] 9.622506e-04 1.924501e-03 9.990377e-01
[57,] 4.062353e-02 8.124706e-02 9.593765e-01
[58,] 1.291982e-01 2.583963e-01 8.708018e-01
[59,] 3.255282e-01 6.510564e-01 6.744718e-01
[60,] 4.591952e-01 9.183903e-01 5.408048e-01
[61,] 5.084726e-01 9.830548e-01 4.915274e-01
[62,] 5.363217e-01 9.273566e-01 4.636783e-01
[63,] 6.627445e-01 6.745110e-01 3.372555e-01
[64,] 6.800624e-01 6.398751e-01 3.199376e-01
[65,] 6.908002e-01 6.183997e-01 3.091998e-01
[66,] 7.067530e-01 5.864940e-01 2.932470e-01
[67,] 7.374428e-01 5.251145e-01 2.625572e-01
[68,] 7.845009e-01 4.309982e-01 2.154991e-01
[69,] 7.802859e-01 4.394282e-01 2.197141e-01
[70,] 7.799334e-01 4.401333e-01 2.200666e-01
[71,] 7.670617e-01 4.658765e-01 2.329383e-01
[72,] 7.571912e-01 4.856176e-01 2.428088e-01
[73,] 7.414311e-01 5.171378e-01 2.585689e-01
[74,] 7.218094e-01 5.563811e-01 2.781906e-01
[75,] 7.014138e-01 5.971724e-01 2.985862e-01
[76,] 6.822136e-01 6.355728e-01 3.177864e-01
[77,] 6.515280e-01 6.969440e-01 3.484720e-01
[78,] 6.262286e-01 7.475428e-01 3.737714e-01
[79,] 5.932941e-01 8.134118e-01 4.067059e-01
[80,] 5.581900e-01 8.836199e-01 4.418100e-01
[81,] 5.206999e-01 9.586002e-01 4.793001e-01
[82,] 4.888525e-01 9.777051e-01 5.111475e-01
[83,] 4.536816e-01 9.073632e-01 5.463184e-01
[84,] 4.196268e-01 8.392537e-01 5.803732e-01
[85,] 3.855905e-01 7.711810e-01 6.144095e-01
[86,] 3.528487e-01 7.056974e-01 6.471513e-01
[87,] 3.205757e-01 6.411513e-01 6.794243e-01
[88,] 2.905633e-01 5.811265e-01 7.094367e-01
[89,] 2.637478e-01 5.274956e-01 7.362522e-01
[90,] 2.517694e-01 5.035388e-01 7.482306e-01
[91,] 2.262083e-01 4.524167e-01 7.737917e-01
[92,] 2.066013e-01 4.132026e-01 7.933987e-01
[93,] 1.816647e-01 3.633294e-01 8.183353e-01
[94,] 1.644829e-01 3.289658e-01 8.355171e-01
[95,] 1.503890e-01 3.007780e-01 8.496110e-01
[96,] 1.309363e-01 2.618727e-01 8.690637e-01
[97,] 1.164673e-01 2.329346e-01 8.835327e-01
[98,] 1.061849e-01 2.123698e-01 8.938151e-01
[99,] 9.348154e-02 1.869631e-01 9.065185e-01
[100,] 8.304278e-02 1.660856e-01 9.169572e-01
[101,] 7.652908e-02 1.530582e-01 9.234709e-01
[102,] 6.667663e-02 1.333533e-01 9.333234e-01
[103,] 6.159429e-02 1.231886e-01 9.384057e-01
[104,] 5.300993e-02 1.060199e-01 9.469901e-01
[105,] 4.589169e-02 9.178338e-02 9.541083e-01
[106,] 3.803925e-02 7.607851e-02 9.619607e-01
[107,] 3.634898e-02 7.269796e-02 9.636510e-01
[108,] 3.043474e-02 6.086949e-02 9.695653e-01
[109,] 2.826913e-02 5.653826e-02 9.717309e-01
[110,] 2.498120e-02 4.996240e-02 9.750188e-01
[111,] 2.183087e-02 4.366174e-02 9.781691e-01
[112,] 1.925683e-02 3.851367e-02 9.807432e-01
[113,] 1.823482e-02 3.646964e-02 9.817652e-01
[114,] 1.596976e-02 3.193952e-02 9.840302e-01
[115,] 1.485747e-02 2.971494e-02 9.851425e-01
[116,] 1.336318e-02 2.672636e-02 9.866368e-01
[117,] 1.177673e-02 2.355346e-02 9.882233e-01
[118,] 1.058661e-02 2.117322e-02 9.894134e-01
[119,] 1.013523e-02 2.027046e-02 9.898648e-01
[120,] 8.962044e-03 1.792409e-02 9.910380e-01
[121,] 8.623199e-03 1.724640e-02 9.913768e-01
[122,] 8.200126e-03 1.640025e-02 9.917999e-01
[123,] 8.796218e-03 1.759244e-02 9.912038e-01
[124,] 9.152507e-03 1.830501e-02 9.908475e-01
[125,] 8.353325e-03 1.670665e-02 9.916467e-01
[126,] 7.533503e-03 1.506701e-02 9.924665e-01
[127,] 6.555940e-03 1.311188e-02 9.934441e-01
[128,] 6.668329e-03 1.333666e-02 9.933317e-01
[129,] 6.147065e-03 1.229413e-02 9.938529e-01
[130,] 7.423994e-03 1.484799e-02 9.925760e-01
[131,] 1.118795e-02 2.237589e-02 9.888121e-01
[132,] 1.418062e-02 2.836124e-02 9.858194e-01
[133,] 1.520264e-02 3.040528e-02 9.847974e-01
[134,] 2.267362e-02 4.534723e-02 9.773264e-01
[135,] 2.684798e-02 5.369596e-02 9.731520e-01
[136,] 3.851900e-02 7.703800e-02 9.614810e-01
[137,] 4.975231e-02 9.950462e-02 9.502477e-01
[138,] 5.725780e-02 1.145156e-01 9.427422e-01
[139,] 1.080702e-01 2.161404e-01 8.919298e-01
[140,] 1.514500e-01 3.029001e-01 8.485500e-01
[141,] 1.762480e-01 3.524960e-01 8.237520e-01
[142,] 9.860929e-01 2.781415e-02 1.390707e-02
[143,] 9.964338e-01 7.132302e-03 3.566151e-03
[144,] 9.998857e-01 2.286060e-04 1.143030e-04
[145,] 9.999915e-01 1.694109e-05 8.470543e-06
[146,] 9.999999e-01 1.697252e-07 8.486260e-08
[147,] 1.000000e+00 1.987449e-12 9.937245e-13
[148,] 1.000000e+00 6.227295e-22 3.113648e-22
[149,] 1.000000e+00 0.000000e+00 0.000000e+00
[150,] 1.000000e+00 0.000000e+00 0.000000e+00
[151,] 1.000000e+00 0.000000e+00 0.000000e+00
[152,] 1.000000e+00 0.000000e+00 0.000000e+00
[153,] 1.000000e+00 0.000000e+00 0.000000e+00
[154,] 1.000000e+00 0.000000e+00 0.000000e+00
[155,] 1.000000e+00 0.000000e+00 0.000000e+00
[156,] 1.000000e+00 0.000000e+00 0.000000e+00
[157,] 1.000000e+00 0.000000e+00 0.000000e+00
[158,] 1.000000e+00 0.000000e+00 0.000000e+00
[159,] 1.000000e+00 0.000000e+00 0.000000e+00
[160,] 1.000000e+00 0.000000e+00 0.000000e+00
[161,] 1.000000e+00 0.000000e+00 0.000000e+00
[162,] 1.000000e+00 0.000000e+00 0.000000e+00
[163,] 1.000000e+00 0.000000e+00 0.000000e+00
[164,] 1.000000e+00 0.000000e+00 0.000000e+00
[165,] 1.000000e+00 0.000000e+00 0.000000e+00
[166,] 1.000000e+00 0.000000e+00 0.000000e+00
[167,] 1.000000e+00 0.000000e+00 0.000000e+00
[168,] 1.000000e+00 0.000000e+00 0.000000e+00
[169,] 1.000000e+00 0.000000e+00 0.000000e+00
[170,] 1.000000e+00 0.000000e+00 0.000000e+00
[171,] 1.000000e+00 0.000000e+00 0.000000e+00
[172,] 1.000000e+00 0.000000e+00 0.000000e+00
[173,] 1.000000e+00 0.000000e+00 0.000000e+00
[174,] 1.000000e+00 0.000000e+00 0.000000e+00
[175,] 1.000000e+00 0.000000e+00 0.000000e+00
[176,] 1.000000e+00 0.000000e+00 0.000000e+00
[177,] 1.000000e+00 0.000000e+00 0.000000e+00
[178,] 1.000000e+00 0.000000e+00 0.000000e+00
[179,] 1.000000e+00 0.000000e+00 0.000000e+00
[180,] 1.000000e+00 0.000000e+00 0.000000e+00
[181,] 1.000000e+00 0.000000e+00 0.000000e+00
[182,] 1.000000e+00 0.000000e+00 0.000000e+00
[183,] 1.000000e+00 0.000000e+00 0.000000e+00
[184,] 1.000000e+00 0.000000e+00 0.000000e+00
[185,] 1.000000e+00 0.000000e+00 0.000000e+00
[186,] 1.000000e+00 0.000000e+00 0.000000e+00
[187,] 1.000000e+00 0.000000e+00 0.000000e+00
[188,] 1.000000e+00 0.000000e+00 0.000000e+00
[189,] 1.000000e+00 0.000000e+00 0.000000e+00
[190,] 1.000000e+00 0.000000e+00 0.000000e+00
[191,] 1.000000e+00 0.000000e+00 0.000000e+00
[192,] 1.000000e+00 0.000000e+00 0.000000e+00
[193,] 1.000000e+00 0.000000e+00 0.000000e+00
[194,] 1.000000e+00 0.000000e+00 0.000000e+00
[195,] 1.000000e+00 0.000000e+00 0.000000e+00
[196,] 1.000000e+00 0.000000e+00 0.000000e+00
[197,] 1.000000e+00 0.000000e+00 0.000000e+00
[198,] 1.000000e+00 0.000000e+00 0.000000e+00
[199,] 1.000000e+00 0.000000e+00 0.000000e+00
[200,] 1.000000e+00 0.000000e+00 0.000000e+00
[201,] 1.000000e+00 0.000000e+00 0.000000e+00
[202,] 1.000000e+00 0.000000e+00 0.000000e+00
[203,] 1.000000e+00 0.000000e+00 0.000000e+00
[204,] 1.000000e+00 0.000000e+00 0.000000e+00
[205,] 1.000000e+00 0.000000e+00 0.000000e+00
[206,] 1.000000e+00 0.000000e+00 0.000000e+00
[207,] 1.000000e+00 0.000000e+00 0.000000e+00
[208,] 1.000000e+00 0.000000e+00 0.000000e+00
[209,] 1.000000e+00 0.000000e+00 0.000000e+00
[210,] 1.000000e+00 0.000000e+00 0.000000e+00
[211,] 1.000000e+00 0.000000e+00 0.000000e+00
[212,] 1.000000e+00 0.000000e+00 0.000000e+00
[213,] 1.000000e+00 0.000000e+00 0.000000e+00
[214,] 1.000000e+00 0.000000e+00 0.000000e+00
[215,] 1.000000e+00 0.000000e+00 0.000000e+00
[216,] 1.000000e+00 0.000000e+00 0.000000e+00
[217,] 1.000000e+00 0.000000e+00 0.000000e+00
[218,] 1.000000e+00 0.000000e+00 0.000000e+00
[219,] 1.000000e+00 8.893182e-323 4.446591e-323
[220,] 1.000000e+00 0.000000e+00 0.000000e+00
[221,] 1.000000e+00 1.425088e-301 7.125442e-302
[222,] 1.000000e+00 6.588852e-282 3.294426e-282
[223,] 1.000000e+00 4.531229e-278 2.265615e-278
[224,] 1.000000e+00 8.185120e-255 4.092560e-255
[225,] 1.000000e+00 3.304471e-246 1.652236e-246
[226,] 1.000000e+00 4.403412e-229 2.201706e-229
[227,] 1.000000e+00 2.743249e-210 1.371624e-210
[228,] 1.000000e+00 5.994997e-213 2.997498e-213
[229,] 1.000000e+00 1.125575e-191 5.627877e-192
[230,] 1.000000e+00 5.583106e-170 2.791553e-170
[231,] 1.000000e+00 3.818049e-165 1.909024e-165
[232,] 1.000000e+00 2.657864e-150 1.328932e-150
[233,] 1.000000e+00 3.763362e-130 1.881681e-130
[234,] 1.000000e+00 6.165419e-126 3.082709e-126
[235,] 1.000000e+00 8.412953e-101 4.206476e-101
[236,] 1.000000e+00 0.000000e+00 0.000000e+00
[237,] 1.000000e+00 7.029661e-73 3.514831e-73
[238,] 1.000000e+00 1.133751e-56 5.668757e-57
[239,] 1.000000e+00 3.950626e-47 1.975313e-47
> postscript(file="/var/wessaorg/rcomp/tmp/14ia01356106677.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/281xq1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34i4r1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/476uj1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ab8v1356106677.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
0.1954218383 0.1135610982 0.0705180288 0.2363277668 -0.0763242743
6 7 8 9 10
0.0757211554 0.0979110351 0.0138506581 0.0673588988 0.0853115227
11 12 13 14 15
0.0825134298 0.0799100718 0.0499758314 0.0342163316 -0.1293173962
16 17 18 19 20
-0.0008680448 -0.0289924641 -0.0949345117 0.0576283191 -0.0305722102
21 22 23 24 25
-0.0282197309 -0.0257638926 -0.0632249307 0.0133549462 -0.1517314772
26 27 28 29 30
-0.0984819050 -0.1709548767 -0.1720721298 -0.2547068258 -0.0222979616
31 32 33 34 35
-0.1672843135 -0.0907076157 -0.1317353890 -0.1712405376 -0.2795276438
36 37 38 39 40
-0.0541681489 -0.2274614340 -0.2343097595 -0.2395815045 -0.1759446657
41 42 43 44 45
-0.1947549323 -0.3536054371 -0.2630350629 -0.2189594075 -0.2283502605
46 47 48 49 50
-0.2979586079 -0.2133175034 -0.3144001213 -0.4572653566 -0.3345973542
51 52 53 54 55
-0.4058741132 -0.3608525247 -0.4329747461 -0.2306068094 -0.2469072425
56 57 58 59 60
-0.3174241705 -0.4431336751 -0.3855245575 -0.4070083173 -0.4212247173
61 62 63 64 65
-0.3670915270 -0.5054793321 -0.5362191556 -0.4663812123 -0.4715262099
66 67 68 69 70
0.4920921328 0.4512528976 0.4145379700 0.4764897914 0.4211427889
71 72 73 74 75
0.6164753826 0.4343618864 0.2933660046 0.3481553464 0.3728608697
76 77 78 79 80
0.3799247156 0.3424379963 0.3877245545 0.3228907957 0.4629665181
81 82 83 84 85
0.2753763122 0.3980505810 0.2864136935 0.3216662169 0.3166800050
86 87 88 89 90
0.3210737228 0.2846773463 0.2759353837 0.2077730717 0.3053133883
91 92 93 94 95
0.2091050638 0.1739824524 0.1235103847 0.1568728728 0.1955754306
96 97 98 99 100
0.1772778231 0.2138443950 0.1210881086 0.2006519518 0.0898373979
101 102 103 104 105
0.1475322916 0.0194832841 0.2383469332 0.0248101804 0.0944149710
106 107 108 109 110
0.1050077401 0.0428130400 0.1436643928 0.1897542081 -0.0191493795
111 112 113 114 115
-0.0424605840 0.0442921932 -0.1537853314 0.0540387574 -0.0207837091
116 117 118 119 120
-0.0021714480 0.1319869601 0.0479657356 -0.1003873247 0.0087095437
121 122 123 124 125
-0.1129879669 -0.0864524275 -0.0585408624 -0.0722757257 -0.1788975010
126 127 128 129 130
-0.0547315584 -0.0936667440 -0.0377762964 -0.1077237397 -0.1099035758
131 132 133 134 135
-0.1734257237 -0.0801599609 -0.1717609415 -0.1596831648 -0.2330828590
136 137 138 139 140
-0.1973234340 -0.1115633240 -0.1104385109 -0.0852653101 -0.1781794893
141 142 143 144 145
-0.1115538752 -0.2880903879 -0.3666887794 -0.2925235177 -0.1918893879
146 147 148 149 150
-0.3317647035 -0.2173312972 -0.2912972633 -0.2670612101 -0.2321502418
151 152 153 154 155
-0.4323434566 -0.3257646429 -0.2330489880 -1.2943681119 -0.2916765561
156 157 158 159 160
-0.4267278228 -0.2839709345 -0.3312376287 -0.3904537857 -0.4666333986
161 162 163 164 165
-0.3070679060 0.5415791365 0.5334055751 0.3611084982 0.4932051262
166 167 168 169 170
0.5152761682 0.5077981774 0.4555532103 0.4505918561 0.3307179039
171 172 173 174 175
0.6032126029 0.4395867478 0.5301479508 0.5077617733 0.4252666566
176 177 178 179 180
0.3810189775 0.5386864224 0.3832531035 0.4466960265 0.3835057129
181 182 183 184 185
0.4266386698 0.5574787733 0.2977508784 0.3414325120 0.2888420411
186 187 188 189 190
0.1657167803 0.2516950852 0.3588857080 0.3547034359 0.3976179638
191 192 193 194 195
0.1046381614 0.2412880171 0.3896459565 0.3212311498 0.2407369391
196 197 198 199 200
0.2733269147 0.1598603621 0.1122114925 0.2194968176 0.0722569551
201 202 203 204 205
0.1844931971 0.1301236500 0.1306295240 0.2282963623 0.1907665213
206 207 208 209 210
0.1103749578 0.1604796950 0.0823112398 0.1706130012 0.0503893322
211 212 213 214 215
0.1044606564 0.0885903652 -0.0098008735 0.1286024732 -0.0112298131
216 217 218 219 220
0.0840725643 -0.0200298506 0.0653420728 0.0118719915 -0.0311310760
221 222 223 224 225
-0.0060895894 -0.0532308077 -0.0733437063 0.0386147690 -0.2424095741
226 227 228 229 230
0.0197065706 -0.0287521667 -0.0892482323 -0.0648656113 -0.0217300705
231 232 233 234 235
0.0025206291 -0.0145029363 0.0411048866 -0.1863389588 -0.0896585844
236 237 238 239 240
-0.0959654804 0.0129622610 -0.0625657774 -0.1929642743 -0.0534941219
241 242 243 244 245
-0.1675642273 -0.1525229438 -0.0655497896 -0.1450606562 -0.2017094671
246 247 248 249 250
-0.2685881715 -0.2124392492 -0.2213870752 -0.2319409581 -0.2993085406
251 252 253 254 255
-0.2589959400 -0.2944411046 -0.1659584789 -0.2757145163 -0.2781021774
256 257 258 259 260
-0.3061240182 -0.5183942755 -0.2762925601 -0.3885784735 -0.0993557668
261 262 263 264
-0.4169317168 -0.4236847285 -0.3233512312 -0.4174750184
> postscript(file="/var/wessaorg/rcomp/tmp/6ythl1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 0.1954218383 NA
1 0.1135610982 0.1954218383
2 0.0705180288 0.1135610982
3 0.2363277668 0.0705180288
4 -0.0763242743 0.2363277668
5 0.0757211554 -0.0763242743
6 0.0979110351 0.0757211554
7 0.0138506581 0.0979110351
8 0.0673588988 0.0138506581
9 0.0853115227 0.0673588988
10 0.0825134298 0.0853115227
11 0.0799100718 0.0825134298
12 0.0499758314 0.0799100718
13 0.0342163316 0.0499758314
14 -0.1293173962 0.0342163316
15 -0.0008680448 -0.1293173962
16 -0.0289924641 -0.0008680448
17 -0.0949345117 -0.0289924641
18 0.0576283191 -0.0949345117
19 -0.0305722102 0.0576283191
20 -0.0282197309 -0.0305722102
21 -0.0257638926 -0.0282197309
22 -0.0632249307 -0.0257638926
23 0.0133549462 -0.0632249307
24 -0.1517314772 0.0133549462
25 -0.0984819050 -0.1517314772
26 -0.1709548767 -0.0984819050
27 -0.1720721298 -0.1709548767
28 -0.2547068258 -0.1720721298
29 -0.0222979616 -0.2547068258
30 -0.1672843135 -0.0222979616
31 -0.0907076157 -0.1672843135
32 -0.1317353890 -0.0907076157
33 -0.1712405376 -0.1317353890
34 -0.2795276438 -0.1712405376
35 -0.0541681489 -0.2795276438
36 -0.2274614340 -0.0541681489
37 -0.2343097595 -0.2274614340
38 -0.2395815045 -0.2343097595
39 -0.1759446657 -0.2395815045
40 -0.1947549323 -0.1759446657
41 -0.3536054371 -0.1947549323
42 -0.2630350629 -0.3536054371
43 -0.2189594075 -0.2630350629
44 -0.2283502605 -0.2189594075
45 -0.2979586079 -0.2283502605
46 -0.2133175034 -0.2979586079
47 -0.3144001213 -0.2133175034
48 -0.4572653566 -0.3144001213
49 -0.3345973542 -0.4572653566
50 -0.4058741132 -0.3345973542
51 -0.3608525247 -0.4058741132
52 -0.4329747461 -0.3608525247
53 -0.2306068094 -0.4329747461
54 -0.2469072425 -0.2306068094
55 -0.3174241705 -0.2469072425
56 -0.4431336751 -0.3174241705
57 -0.3855245575 -0.4431336751
58 -0.4070083173 -0.3855245575
59 -0.4212247173 -0.4070083173
60 -0.3670915270 -0.4212247173
61 -0.5054793321 -0.3670915270
62 -0.5362191556 -0.5054793321
63 -0.4663812123 -0.5362191556
64 -0.4715262099 -0.4663812123
65 0.4920921328 -0.4715262099
66 0.4512528976 0.4920921328
67 0.4145379700 0.4512528976
68 0.4764897914 0.4145379700
69 0.4211427889 0.4764897914
70 0.6164753826 0.4211427889
71 0.4343618864 0.6164753826
72 0.2933660046 0.4343618864
73 0.3481553464 0.2933660046
74 0.3728608697 0.3481553464
75 0.3799247156 0.3728608697
76 0.3424379963 0.3799247156
77 0.3877245545 0.3424379963
78 0.3228907957 0.3877245545
79 0.4629665181 0.3228907957
80 0.2753763122 0.4629665181
81 0.3980505810 0.2753763122
82 0.2864136935 0.3980505810
83 0.3216662169 0.2864136935
84 0.3166800050 0.3216662169
85 0.3210737228 0.3166800050
86 0.2846773463 0.3210737228
87 0.2759353837 0.2846773463
88 0.2077730717 0.2759353837
89 0.3053133883 0.2077730717
90 0.2091050638 0.3053133883
91 0.1739824524 0.2091050638
92 0.1235103847 0.1739824524
93 0.1568728728 0.1235103847
94 0.1955754306 0.1568728728
95 0.1772778231 0.1955754306
96 0.2138443950 0.1772778231
97 0.1210881086 0.2138443950
98 0.2006519518 0.1210881086
99 0.0898373979 0.2006519518
100 0.1475322916 0.0898373979
101 0.0194832841 0.1475322916
102 0.2383469332 0.0194832841
103 0.0248101804 0.2383469332
104 0.0944149710 0.0248101804
105 0.1050077401 0.0944149710
106 0.0428130400 0.1050077401
107 0.1436643928 0.0428130400
108 0.1897542081 0.1436643928
109 -0.0191493795 0.1897542081
110 -0.0424605840 -0.0191493795
111 0.0442921932 -0.0424605840
112 -0.1537853314 0.0442921932
113 0.0540387574 -0.1537853314
114 -0.0207837091 0.0540387574
115 -0.0021714480 -0.0207837091
116 0.1319869601 -0.0021714480
117 0.0479657356 0.1319869601
118 -0.1003873247 0.0479657356
119 0.0087095437 -0.1003873247
120 -0.1129879669 0.0087095437
121 -0.0864524275 -0.1129879669
122 -0.0585408624 -0.0864524275
123 -0.0722757257 -0.0585408624
124 -0.1788975010 -0.0722757257
125 -0.0547315584 -0.1788975010
126 -0.0936667440 -0.0547315584
127 -0.0377762964 -0.0936667440
128 -0.1077237397 -0.0377762964
129 -0.1099035758 -0.1077237397
130 -0.1734257237 -0.1099035758
131 -0.0801599609 -0.1734257237
132 -0.1717609415 -0.0801599609
133 -0.1596831648 -0.1717609415
134 -0.2330828590 -0.1596831648
135 -0.1973234340 -0.2330828590
136 -0.1115633240 -0.1973234340
137 -0.1104385109 -0.1115633240
138 -0.0852653101 -0.1104385109
139 -0.1781794893 -0.0852653101
140 -0.1115538752 -0.1781794893
141 -0.2880903879 -0.1115538752
142 -0.3666887794 -0.2880903879
143 -0.2925235177 -0.3666887794
144 -0.1918893879 -0.2925235177
145 -0.3317647035 -0.1918893879
146 -0.2173312972 -0.3317647035
147 -0.2912972633 -0.2173312972
148 -0.2670612101 -0.2912972633
149 -0.2321502418 -0.2670612101
150 -0.4323434566 -0.2321502418
151 -0.3257646429 -0.4323434566
152 -0.2330489880 -0.3257646429
153 -1.2943681119 -0.2330489880
154 -0.2916765561 -1.2943681119
155 -0.4267278228 -0.2916765561
156 -0.2839709345 -0.4267278228
157 -0.3312376287 -0.2839709345
158 -0.3904537857 -0.3312376287
159 -0.4666333986 -0.3904537857
160 -0.3070679060 -0.4666333986
161 0.5415791365 -0.3070679060
162 0.5334055751 0.5415791365
163 0.3611084982 0.5334055751
164 0.4932051262 0.3611084982
165 0.5152761682 0.4932051262
166 0.5077981774 0.5152761682
167 0.4555532103 0.5077981774
168 0.4505918561 0.4555532103
169 0.3307179039 0.4505918561
170 0.6032126029 0.3307179039
171 0.4395867478 0.6032126029
172 0.5301479508 0.4395867478
173 0.5077617733 0.5301479508
174 0.4252666566 0.5077617733
175 0.3810189775 0.4252666566
176 0.5386864224 0.3810189775
177 0.3832531035 0.5386864224
178 0.4466960265 0.3832531035
179 0.3835057129 0.4466960265
180 0.4266386698 0.3835057129
181 0.5574787733 0.4266386698
182 0.2977508784 0.5574787733
183 0.3414325120 0.2977508784
184 0.2888420411 0.3414325120
185 0.1657167803 0.2888420411
186 0.2516950852 0.1657167803
187 0.3588857080 0.2516950852
188 0.3547034359 0.3588857080
189 0.3976179638 0.3547034359
190 0.1046381614 0.3976179638
191 0.2412880171 0.1046381614
192 0.3896459565 0.2412880171
193 0.3212311498 0.3896459565
194 0.2407369391 0.3212311498
195 0.2733269147 0.2407369391
196 0.1598603621 0.2733269147
197 0.1122114925 0.1598603621
198 0.2194968176 0.1122114925
199 0.0722569551 0.2194968176
200 0.1844931971 0.0722569551
201 0.1301236500 0.1844931971
202 0.1306295240 0.1301236500
203 0.2282963623 0.1306295240
204 0.1907665213 0.2282963623
205 0.1103749578 0.1907665213
206 0.1604796950 0.1103749578
207 0.0823112398 0.1604796950
208 0.1706130012 0.0823112398
209 0.0503893322 0.1706130012
210 0.1044606564 0.0503893322
211 0.0885903652 0.1044606564
212 -0.0098008735 0.0885903652
213 0.1286024732 -0.0098008735
214 -0.0112298131 0.1286024732
215 0.0840725643 -0.0112298131
216 -0.0200298506 0.0840725643
217 0.0653420728 -0.0200298506
218 0.0118719915 0.0653420728
219 -0.0311310760 0.0118719915
220 -0.0060895894 -0.0311310760
221 -0.0532308077 -0.0060895894
222 -0.0733437063 -0.0532308077
223 0.0386147690 -0.0733437063
224 -0.2424095741 0.0386147690
225 0.0197065706 -0.2424095741
226 -0.0287521667 0.0197065706
227 -0.0892482323 -0.0287521667
228 -0.0648656113 -0.0892482323
229 -0.0217300705 -0.0648656113
230 0.0025206291 -0.0217300705
231 -0.0145029363 0.0025206291
232 0.0411048866 -0.0145029363
233 -0.1863389588 0.0411048866
234 -0.0896585844 -0.1863389588
235 -0.0959654804 -0.0896585844
236 0.0129622610 -0.0959654804
237 -0.0625657774 0.0129622610
238 -0.1929642743 -0.0625657774
239 -0.0534941219 -0.1929642743
240 -0.1675642273 -0.0534941219
241 -0.1525229438 -0.1675642273
242 -0.0655497896 -0.1525229438
243 -0.1450606562 -0.0655497896
244 -0.2017094671 -0.1450606562
245 -0.2685881715 -0.2017094671
246 -0.2124392492 -0.2685881715
247 -0.2213870752 -0.2124392492
248 -0.2319409581 -0.2213870752
249 -0.2993085406 -0.2319409581
250 -0.2589959400 -0.2993085406
251 -0.2944411046 -0.2589959400
252 -0.1659584789 -0.2944411046
253 -0.2757145163 -0.1659584789
254 -0.2781021774 -0.2757145163
255 -0.3061240182 -0.2781021774
256 -0.5183942755 -0.3061240182
257 -0.2762925601 -0.5183942755
258 -0.3885784735 -0.2762925601
259 -0.0993557668 -0.3885784735
260 -0.4169317168 -0.0993557668
261 -0.4236847285 -0.4169317168
262 -0.3233512312 -0.4236847285
263 -0.4174750184 -0.3233512312
264 NA -0.4174750184
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.1135610982 0.1954218383
[2,] 0.0705180288 0.1135610982
[3,] 0.2363277668 0.0705180288
[4,] -0.0763242743 0.2363277668
[5,] 0.0757211554 -0.0763242743
[6,] 0.0979110351 0.0757211554
[7,] 0.0138506581 0.0979110351
[8,] 0.0673588988 0.0138506581
[9,] 0.0853115227 0.0673588988
[10,] 0.0825134298 0.0853115227
[11,] 0.0799100718 0.0825134298
[12,] 0.0499758314 0.0799100718
[13,] 0.0342163316 0.0499758314
[14,] -0.1293173962 0.0342163316
[15,] -0.0008680448 -0.1293173962
[16,] -0.0289924641 -0.0008680448
[17,] -0.0949345117 -0.0289924641
[18,] 0.0576283191 -0.0949345117
[19,] -0.0305722102 0.0576283191
[20,] -0.0282197309 -0.0305722102
[21,] -0.0257638926 -0.0282197309
[22,] -0.0632249307 -0.0257638926
[23,] 0.0133549462 -0.0632249307
[24,] -0.1517314772 0.0133549462
[25,] -0.0984819050 -0.1517314772
[26,] -0.1709548767 -0.0984819050
[27,] -0.1720721298 -0.1709548767
[28,] -0.2547068258 -0.1720721298
[29,] -0.0222979616 -0.2547068258
[30,] -0.1672843135 -0.0222979616
[31,] -0.0907076157 -0.1672843135
[32,] -0.1317353890 -0.0907076157
[33,] -0.1712405376 -0.1317353890
[34,] -0.2795276438 -0.1712405376
[35,] -0.0541681489 -0.2795276438
[36,] -0.2274614340 -0.0541681489
[37,] -0.2343097595 -0.2274614340
[38,] -0.2395815045 -0.2343097595
[39,] -0.1759446657 -0.2395815045
[40,] -0.1947549323 -0.1759446657
[41,] -0.3536054371 -0.1947549323
[42,] -0.2630350629 -0.3536054371
[43,] -0.2189594075 -0.2630350629
[44,] -0.2283502605 -0.2189594075
[45,] -0.2979586079 -0.2283502605
[46,] -0.2133175034 -0.2979586079
[47,] -0.3144001213 -0.2133175034
[48,] -0.4572653566 -0.3144001213
[49,] -0.3345973542 -0.4572653566
[50,] -0.4058741132 -0.3345973542
[51,] -0.3608525247 -0.4058741132
[52,] -0.4329747461 -0.3608525247
[53,] -0.2306068094 -0.4329747461
[54,] -0.2469072425 -0.2306068094
[55,] -0.3174241705 -0.2469072425
[56,] -0.4431336751 -0.3174241705
[57,] -0.3855245575 -0.4431336751
[58,] -0.4070083173 -0.3855245575
[59,] -0.4212247173 -0.4070083173
[60,] -0.3670915270 -0.4212247173
[61,] -0.5054793321 -0.3670915270
[62,] -0.5362191556 -0.5054793321
[63,] -0.4663812123 -0.5362191556
[64,] -0.4715262099 -0.4663812123
[65,] 0.4920921328 -0.4715262099
[66,] 0.4512528976 0.4920921328
[67,] 0.4145379700 0.4512528976
[68,] 0.4764897914 0.4145379700
[69,] 0.4211427889 0.4764897914
[70,] 0.6164753826 0.4211427889
[71,] 0.4343618864 0.6164753826
[72,] 0.2933660046 0.4343618864
[73,] 0.3481553464 0.2933660046
[74,] 0.3728608697 0.3481553464
[75,] 0.3799247156 0.3728608697
[76,] 0.3424379963 0.3799247156
[77,] 0.3877245545 0.3424379963
[78,] 0.3228907957 0.3877245545
[79,] 0.4629665181 0.3228907957
[80,] 0.2753763122 0.4629665181
[81,] 0.3980505810 0.2753763122
[82,] 0.2864136935 0.3980505810
[83,] 0.3216662169 0.2864136935
[84,] 0.3166800050 0.3216662169
[85,] 0.3210737228 0.3166800050
[86,] 0.2846773463 0.3210737228
[87,] 0.2759353837 0.2846773463
[88,] 0.2077730717 0.2759353837
[89,] 0.3053133883 0.2077730717
[90,] 0.2091050638 0.3053133883
[91,] 0.1739824524 0.2091050638
[92,] 0.1235103847 0.1739824524
[93,] 0.1568728728 0.1235103847
[94,] 0.1955754306 0.1568728728
[95,] 0.1772778231 0.1955754306
[96,] 0.2138443950 0.1772778231
[97,] 0.1210881086 0.2138443950
[98,] 0.2006519518 0.1210881086
[99,] 0.0898373979 0.2006519518
[100,] 0.1475322916 0.0898373979
[101,] 0.0194832841 0.1475322916
[102,] 0.2383469332 0.0194832841
[103,] 0.0248101804 0.2383469332
[104,] 0.0944149710 0.0248101804
[105,] 0.1050077401 0.0944149710
[106,] 0.0428130400 0.1050077401
[107,] 0.1436643928 0.0428130400
[108,] 0.1897542081 0.1436643928
[109,] -0.0191493795 0.1897542081
[110,] -0.0424605840 -0.0191493795
[111,] 0.0442921932 -0.0424605840
[112,] -0.1537853314 0.0442921932
[113,] 0.0540387574 -0.1537853314
[114,] -0.0207837091 0.0540387574
[115,] -0.0021714480 -0.0207837091
[116,] 0.1319869601 -0.0021714480
[117,] 0.0479657356 0.1319869601
[118,] -0.1003873247 0.0479657356
[119,] 0.0087095437 -0.1003873247
[120,] -0.1129879669 0.0087095437
[121,] -0.0864524275 -0.1129879669
[122,] -0.0585408624 -0.0864524275
[123,] -0.0722757257 -0.0585408624
[124,] -0.1788975010 -0.0722757257
[125,] -0.0547315584 -0.1788975010
[126,] -0.0936667440 -0.0547315584
[127,] -0.0377762964 -0.0936667440
[128,] -0.1077237397 -0.0377762964
[129,] -0.1099035758 -0.1077237397
[130,] -0.1734257237 -0.1099035758
[131,] -0.0801599609 -0.1734257237
[132,] -0.1717609415 -0.0801599609
[133,] -0.1596831648 -0.1717609415
[134,] -0.2330828590 -0.1596831648
[135,] -0.1973234340 -0.2330828590
[136,] -0.1115633240 -0.1973234340
[137,] -0.1104385109 -0.1115633240
[138,] -0.0852653101 -0.1104385109
[139,] -0.1781794893 -0.0852653101
[140,] -0.1115538752 -0.1781794893
[141,] -0.2880903879 -0.1115538752
[142,] -0.3666887794 -0.2880903879
[143,] -0.2925235177 -0.3666887794
[144,] -0.1918893879 -0.2925235177
[145,] -0.3317647035 -0.1918893879
[146,] -0.2173312972 -0.3317647035
[147,] -0.2912972633 -0.2173312972
[148,] -0.2670612101 -0.2912972633
[149,] -0.2321502418 -0.2670612101
[150,] -0.4323434566 -0.2321502418
[151,] -0.3257646429 -0.4323434566
[152,] -0.2330489880 -0.3257646429
[153,] -1.2943681119 -0.2330489880
[154,] -0.2916765561 -1.2943681119
[155,] -0.4267278228 -0.2916765561
[156,] -0.2839709345 -0.4267278228
[157,] -0.3312376287 -0.2839709345
[158,] -0.3904537857 -0.3312376287
[159,] -0.4666333986 -0.3904537857
[160,] -0.3070679060 -0.4666333986
[161,] 0.5415791365 -0.3070679060
[162,] 0.5334055751 0.5415791365
[163,] 0.3611084982 0.5334055751
[164,] 0.4932051262 0.3611084982
[165,] 0.5152761682 0.4932051262
[166,] 0.5077981774 0.5152761682
[167,] 0.4555532103 0.5077981774
[168,] 0.4505918561 0.4555532103
[169,] 0.3307179039 0.4505918561
[170,] 0.6032126029 0.3307179039
[171,] 0.4395867478 0.6032126029
[172,] 0.5301479508 0.4395867478
[173,] 0.5077617733 0.5301479508
[174,] 0.4252666566 0.5077617733
[175,] 0.3810189775 0.4252666566
[176,] 0.5386864224 0.3810189775
[177,] 0.3832531035 0.5386864224
[178,] 0.4466960265 0.3832531035
[179,] 0.3835057129 0.4466960265
[180,] 0.4266386698 0.3835057129
[181,] 0.5574787733 0.4266386698
[182,] 0.2977508784 0.5574787733
[183,] 0.3414325120 0.2977508784
[184,] 0.2888420411 0.3414325120
[185,] 0.1657167803 0.2888420411
[186,] 0.2516950852 0.1657167803
[187,] 0.3588857080 0.2516950852
[188,] 0.3547034359 0.3588857080
[189,] 0.3976179638 0.3547034359
[190,] 0.1046381614 0.3976179638
[191,] 0.2412880171 0.1046381614
[192,] 0.3896459565 0.2412880171
[193,] 0.3212311498 0.3896459565
[194,] 0.2407369391 0.3212311498
[195,] 0.2733269147 0.2407369391
[196,] 0.1598603621 0.2733269147
[197,] 0.1122114925 0.1598603621
[198,] 0.2194968176 0.1122114925
[199,] 0.0722569551 0.2194968176
[200,] 0.1844931971 0.0722569551
[201,] 0.1301236500 0.1844931971
[202,] 0.1306295240 0.1301236500
[203,] 0.2282963623 0.1306295240
[204,] 0.1907665213 0.2282963623
[205,] 0.1103749578 0.1907665213
[206,] 0.1604796950 0.1103749578
[207,] 0.0823112398 0.1604796950
[208,] 0.1706130012 0.0823112398
[209,] 0.0503893322 0.1706130012
[210,] 0.1044606564 0.0503893322
[211,] 0.0885903652 0.1044606564
[212,] -0.0098008735 0.0885903652
[213,] 0.1286024732 -0.0098008735
[214,] -0.0112298131 0.1286024732
[215,] 0.0840725643 -0.0112298131
[216,] -0.0200298506 0.0840725643
[217,] 0.0653420728 -0.0200298506
[218,] 0.0118719915 0.0653420728
[219,] -0.0311310760 0.0118719915
[220,] -0.0060895894 -0.0311310760
[221,] -0.0532308077 -0.0060895894
[222,] -0.0733437063 -0.0532308077
[223,] 0.0386147690 -0.0733437063
[224,] -0.2424095741 0.0386147690
[225,] 0.0197065706 -0.2424095741
[226,] -0.0287521667 0.0197065706
[227,] -0.0892482323 -0.0287521667
[228,] -0.0648656113 -0.0892482323
[229,] -0.0217300705 -0.0648656113
[230,] 0.0025206291 -0.0217300705
[231,] -0.0145029363 0.0025206291
[232,] 0.0411048866 -0.0145029363
[233,] -0.1863389588 0.0411048866
[234,] -0.0896585844 -0.1863389588
[235,] -0.0959654804 -0.0896585844
[236,] 0.0129622610 -0.0959654804
[237,] -0.0625657774 0.0129622610
[238,] -0.1929642743 -0.0625657774
[239,] -0.0534941219 -0.1929642743
[240,] -0.1675642273 -0.0534941219
[241,] -0.1525229438 -0.1675642273
[242,] -0.0655497896 -0.1525229438
[243,] -0.1450606562 -0.0655497896
[244,] -0.2017094671 -0.1450606562
[245,] -0.2685881715 -0.2017094671
[246,] -0.2124392492 -0.2685881715
[247,] -0.2213870752 -0.2124392492
[248,] -0.2319409581 -0.2213870752
[249,] -0.2993085406 -0.2319409581
[250,] -0.2589959400 -0.2993085406
[251,] -0.2944411046 -0.2589959400
[252,] -0.1659584789 -0.2944411046
[253,] -0.2757145163 -0.1659584789
[254,] -0.2781021774 -0.2757145163
[255,] -0.3061240182 -0.2781021774
[256,] -0.5183942755 -0.3061240182
[257,] -0.2762925601 -0.5183942755
[258,] -0.3885784735 -0.2762925601
[259,] -0.0993557668 -0.3885784735
[260,] -0.4169317168 -0.0993557668
[261,] -0.4236847285 -0.4169317168
[262,] -0.3233512312 -0.4236847285
[263,] -0.4174750184 -0.3233512312
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.1135610982 0.1954218383
2 0.0705180288 0.1135610982
3 0.2363277668 0.0705180288
4 -0.0763242743 0.2363277668
5 0.0757211554 -0.0763242743
6 0.0979110351 0.0757211554
7 0.0138506581 0.0979110351
8 0.0673588988 0.0138506581
9 0.0853115227 0.0673588988
10 0.0825134298 0.0853115227
11 0.0799100718 0.0825134298
12 0.0499758314 0.0799100718
13 0.0342163316 0.0499758314
14 -0.1293173962 0.0342163316
15 -0.0008680448 -0.1293173962
16 -0.0289924641 -0.0008680448
17 -0.0949345117 -0.0289924641
18 0.0576283191 -0.0949345117
19 -0.0305722102 0.0576283191
20 -0.0282197309 -0.0305722102
21 -0.0257638926 -0.0282197309
22 -0.0632249307 -0.0257638926
23 0.0133549462 -0.0632249307
24 -0.1517314772 0.0133549462
25 -0.0984819050 -0.1517314772
26 -0.1709548767 -0.0984819050
27 -0.1720721298 -0.1709548767
28 -0.2547068258 -0.1720721298
29 -0.0222979616 -0.2547068258
30 -0.1672843135 -0.0222979616
31 -0.0907076157 -0.1672843135
32 -0.1317353890 -0.0907076157
33 -0.1712405376 -0.1317353890
34 -0.2795276438 -0.1712405376
35 -0.0541681489 -0.2795276438
36 -0.2274614340 -0.0541681489
37 -0.2343097595 -0.2274614340
38 -0.2395815045 -0.2343097595
39 -0.1759446657 -0.2395815045
40 -0.1947549323 -0.1759446657
41 -0.3536054371 -0.1947549323
42 -0.2630350629 -0.3536054371
43 -0.2189594075 -0.2630350629
44 -0.2283502605 -0.2189594075
45 -0.2979586079 -0.2283502605
46 -0.2133175034 -0.2979586079
47 -0.3144001213 -0.2133175034
48 -0.4572653566 -0.3144001213
49 -0.3345973542 -0.4572653566
50 -0.4058741132 -0.3345973542
51 -0.3608525247 -0.4058741132
52 -0.4329747461 -0.3608525247
53 -0.2306068094 -0.4329747461
54 -0.2469072425 -0.2306068094
55 -0.3174241705 -0.2469072425
56 -0.4431336751 -0.3174241705
57 -0.3855245575 -0.4431336751
58 -0.4070083173 -0.3855245575
59 -0.4212247173 -0.4070083173
60 -0.3670915270 -0.4212247173
61 -0.5054793321 -0.3670915270
62 -0.5362191556 -0.5054793321
63 -0.4663812123 -0.5362191556
64 -0.4715262099 -0.4663812123
65 0.4920921328 -0.4715262099
66 0.4512528976 0.4920921328
67 0.4145379700 0.4512528976
68 0.4764897914 0.4145379700
69 0.4211427889 0.4764897914
70 0.6164753826 0.4211427889
71 0.4343618864 0.6164753826
72 0.2933660046 0.4343618864
73 0.3481553464 0.2933660046
74 0.3728608697 0.3481553464
75 0.3799247156 0.3728608697
76 0.3424379963 0.3799247156
77 0.3877245545 0.3424379963
78 0.3228907957 0.3877245545
79 0.4629665181 0.3228907957
80 0.2753763122 0.4629665181
81 0.3980505810 0.2753763122
82 0.2864136935 0.3980505810
83 0.3216662169 0.2864136935
84 0.3166800050 0.3216662169
85 0.3210737228 0.3166800050
86 0.2846773463 0.3210737228
87 0.2759353837 0.2846773463
88 0.2077730717 0.2759353837
89 0.3053133883 0.2077730717
90 0.2091050638 0.3053133883
91 0.1739824524 0.2091050638
92 0.1235103847 0.1739824524
93 0.1568728728 0.1235103847
94 0.1955754306 0.1568728728
95 0.1772778231 0.1955754306
96 0.2138443950 0.1772778231
97 0.1210881086 0.2138443950
98 0.2006519518 0.1210881086
99 0.0898373979 0.2006519518
100 0.1475322916 0.0898373979
101 0.0194832841 0.1475322916
102 0.2383469332 0.0194832841
103 0.0248101804 0.2383469332
104 0.0944149710 0.0248101804
105 0.1050077401 0.0944149710
106 0.0428130400 0.1050077401
107 0.1436643928 0.0428130400
108 0.1897542081 0.1436643928
109 -0.0191493795 0.1897542081
110 -0.0424605840 -0.0191493795
111 0.0442921932 -0.0424605840
112 -0.1537853314 0.0442921932
113 0.0540387574 -0.1537853314
114 -0.0207837091 0.0540387574
115 -0.0021714480 -0.0207837091
116 0.1319869601 -0.0021714480
117 0.0479657356 0.1319869601
118 -0.1003873247 0.0479657356
119 0.0087095437 -0.1003873247
120 -0.1129879669 0.0087095437
121 -0.0864524275 -0.1129879669
122 -0.0585408624 -0.0864524275
123 -0.0722757257 -0.0585408624
124 -0.1788975010 -0.0722757257
125 -0.0547315584 -0.1788975010
126 -0.0936667440 -0.0547315584
127 -0.0377762964 -0.0936667440
128 -0.1077237397 -0.0377762964
129 -0.1099035758 -0.1077237397
130 -0.1734257237 -0.1099035758
131 -0.0801599609 -0.1734257237
132 -0.1717609415 -0.0801599609
133 -0.1596831648 -0.1717609415
134 -0.2330828590 -0.1596831648
135 -0.1973234340 -0.2330828590
136 -0.1115633240 -0.1973234340
137 -0.1104385109 -0.1115633240
138 -0.0852653101 -0.1104385109
139 -0.1781794893 -0.0852653101
140 -0.1115538752 -0.1781794893
141 -0.2880903879 -0.1115538752
142 -0.3666887794 -0.2880903879
143 -0.2925235177 -0.3666887794
144 -0.1918893879 -0.2925235177
145 -0.3317647035 -0.1918893879
146 -0.2173312972 -0.3317647035
147 -0.2912972633 -0.2173312972
148 -0.2670612101 -0.2912972633
149 -0.2321502418 -0.2670612101
150 -0.4323434566 -0.2321502418
151 -0.3257646429 -0.4323434566
152 -0.2330489880 -0.3257646429
153 -1.2943681119 -0.2330489880
154 -0.2916765561 -1.2943681119
155 -0.4267278228 -0.2916765561
156 -0.2839709345 -0.4267278228
157 -0.3312376287 -0.2839709345
158 -0.3904537857 -0.3312376287
159 -0.4666333986 -0.3904537857
160 -0.3070679060 -0.4666333986
161 0.5415791365 -0.3070679060
162 0.5334055751 0.5415791365
163 0.3611084982 0.5334055751
164 0.4932051262 0.3611084982
165 0.5152761682 0.4932051262
166 0.5077981774 0.5152761682
167 0.4555532103 0.5077981774
168 0.4505918561 0.4555532103
169 0.3307179039 0.4505918561
170 0.6032126029 0.3307179039
171 0.4395867478 0.6032126029
172 0.5301479508 0.4395867478
173 0.5077617733 0.5301479508
174 0.4252666566 0.5077617733
175 0.3810189775 0.4252666566
176 0.5386864224 0.3810189775
177 0.3832531035 0.5386864224
178 0.4466960265 0.3832531035
179 0.3835057129 0.4466960265
180 0.4266386698 0.3835057129
181 0.5574787733 0.4266386698
182 0.2977508784 0.5574787733
183 0.3414325120 0.2977508784
184 0.2888420411 0.3414325120
185 0.1657167803 0.2888420411
186 0.2516950852 0.1657167803
187 0.3588857080 0.2516950852
188 0.3547034359 0.3588857080
189 0.3976179638 0.3547034359
190 0.1046381614 0.3976179638
191 0.2412880171 0.1046381614
192 0.3896459565 0.2412880171
193 0.3212311498 0.3896459565
194 0.2407369391 0.3212311498
195 0.2733269147 0.2407369391
196 0.1598603621 0.2733269147
197 0.1122114925 0.1598603621
198 0.2194968176 0.1122114925
199 0.0722569551 0.2194968176
200 0.1844931971 0.0722569551
201 0.1301236500 0.1844931971
202 0.1306295240 0.1301236500
203 0.2282963623 0.1306295240
204 0.1907665213 0.2282963623
205 0.1103749578 0.1907665213
206 0.1604796950 0.1103749578
207 0.0823112398 0.1604796950
208 0.1706130012 0.0823112398
209 0.0503893322 0.1706130012
210 0.1044606564 0.0503893322
211 0.0885903652 0.1044606564
212 -0.0098008735 0.0885903652
213 0.1286024732 -0.0098008735
214 -0.0112298131 0.1286024732
215 0.0840725643 -0.0112298131
216 -0.0200298506 0.0840725643
217 0.0653420728 -0.0200298506
218 0.0118719915 0.0653420728
219 -0.0311310760 0.0118719915
220 -0.0060895894 -0.0311310760
221 -0.0532308077 -0.0060895894
222 -0.0733437063 -0.0532308077
223 0.0386147690 -0.0733437063
224 -0.2424095741 0.0386147690
225 0.0197065706 -0.2424095741
226 -0.0287521667 0.0197065706
227 -0.0892482323 -0.0287521667
228 -0.0648656113 -0.0892482323
229 -0.0217300705 -0.0648656113
230 0.0025206291 -0.0217300705
231 -0.0145029363 0.0025206291
232 0.0411048866 -0.0145029363
233 -0.1863389588 0.0411048866
234 -0.0896585844 -0.1863389588
235 -0.0959654804 -0.0896585844
236 0.0129622610 -0.0959654804
237 -0.0625657774 0.0129622610
238 -0.1929642743 -0.0625657774
239 -0.0534941219 -0.1929642743
240 -0.1675642273 -0.0534941219
241 -0.1525229438 -0.1675642273
242 -0.0655497896 -0.1525229438
243 -0.1450606562 -0.0655497896
244 -0.2017094671 -0.1450606562
245 -0.2685881715 -0.2017094671
246 -0.2124392492 -0.2685881715
247 -0.2213870752 -0.2124392492
248 -0.2319409581 -0.2213870752
249 -0.2993085406 -0.2319409581
250 -0.2589959400 -0.2993085406
251 -0.2944411046 -0.2589959400
252 -0.1659584789 -0.2944411046
253 -0.2757145163 -0.1659584789
254 -0.2781021774 -0.2757145163
255 -0.3061240182 -0.2781021774
256 -0.5183942755 -0.3061240182
257 -0.2762925601 -0.5183942755
258 -0.3885784735 -0.2762925601
259 -0.0993557668 -0.3885784735
260 -0.4169317168 -0.0993557668
261 -0.4236847285 -0.4169317168
262 -0.3233512312 -0.4236847285
263 -0.4174750184 -0.3233512312
> 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/7apnd1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/806ur1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9b0ya1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10tpzm1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11euhb1356106677.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/12dhon1356106677.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/13moiq1356106677.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/14yovd1356106678.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/155eqr1356106678.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/16l3pa1356106678.tab")
+ }
>
> try(system("convert tmp/14ia01356106677.ps tmp/14ia01356106677.png",intern=TRUE))
character(0)
> try(system("convert tmp/281xq1356106677.ps tmp/281xq1356106677.png",intern=TRUE))
character(0)
> try(system("convert tmp/34i4r1356106677.ps tmp/34i4r1356106677.png",intern=TRUE))
character(0)
> try(system("convert tmp/476uj1356106677.ps tmp/476uj1356106677.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ab8v1356106677.ps tmp/5ab8v1356106677.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ythl1356106677.ps tmp/6ythl1356106677.png",intern=TRUE))
character(0)
> try(system("convert tmp/7apnd1356106677.ps tmp/7apnd1356106677.png",intern=TRUE))
character(0)
> try(system("convert tmp/806ur1356106677.ps tmp/806ur1356106677.png",intern=TRUE))
character(0)
> try(system("convert tmp/9b0ya1356106677.ps tmp/9b0ya1356106677.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tpzm1356106677.ps tmp/10tpzm1356106677.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.842 1.642 17.409