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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+ ,69
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+ ,35
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+ ,16
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+ ,15
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+ ,36
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+ ,11
+ ,7
+ ,15
+ ,14
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+ ,42
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+ ,32
+ ,10
+ ,7
+ ,13
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+ ,78
+ ,47
+ ,36
+ ,34
+ ,12
+ ,6
+ ,13
+ ,11
+ ,71
+ ,44
+ ,33
+ ,32
+ ,16
+ ,9
+ ,13
+ ,13
+ ,72
+ ,45
+ ,37
+ ,33
+ ,12
+ ,10
+ ,12
+ ,17
+ ,68
+ ,44
+ ,34
+ ,33
+ ,14
+ ,11
+ ,12
+ ,15
+ ,67
+ ,43
+ ,35
+ ,37
+ ,16
+ ,12
+ ,9
+ ,21
+ ,75
+ ,43
+ ,31
+ ,32
+ ,14
+ ,8
+ ,9
+ ,18
+ ,62
+ ,40
+ ,37
+ ,34
+ ,13
+ ,11
+ ,15
+ ,15
+ ,67
+ ,41
+ ,35
+ ,30
+ ,4
+ ,3
+ ,10
+ ,8
+ ,83
+ ,52
+ ,27
+ ,30
+ ,15
+ ,11
+ ,14
+ ,12
+ ,64
+ ,38
+ ,34
+ ,38
+ ,11
+ ,12
+ ,15
+ ,12
+ ,68
+ ,41
+ ,40
+ ,36
+ ,11
+ ,7
+ ,7
+ ,22
+ ,62
+ ,39
+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '3'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Learning Connected Separate Software Happiness Depression Belonging
1 13 41 38 12 14 12.0 53
2 16 39 32 11 18 11.0 83
3 19 30 35 15 11 14.0 66
4 15 31 33 6 12 12.0 67
5 14 34 37 13 16 21.0 76
6 13 35 29 10 18 12.0 78
7 19 39 31 12 14 22.0 53
8 15 34 36 14 14 11.0 80
9 14 36 35 12 15 10.0 74
10 15 37 38 9 15 13.0 76
11 16 38 31 10 17 10.0 79
12 16 36 34 12 19 8.0 54
13 16 38 35 12 10 15.0 67
14 16 39 38 11 16 14.0 54
15 17 33 37 15 18 10.0 87
16 15 32 33 12 14 14.0 58
17 15 36 32 10 14 14.0 75
18 20 38 38 12 17 11.0 88
19 18 39 38 11 14 10.0 64
20 16 32 32 12 16 13.0 57
21 16 32 33 11 18 9.5 66
22 16 31 31 12 11 14.0 68
23 19 39 38 13 14 12.0 54
24 16 37 39 11 12 14.0 56
25 17 39 32 12 17 11.0 86
26 17 41 32 13 9 9.0 80
27 16 36 35 10 16 11.0 76
28 15 33 37 14 14 15.0 69
29 16 33 33 12 15 14.0 78
30 14 34 33 10 11 13.0 67
31 15 31 31 12 16 9.0 80
32 12 27 32 8 13 15.0 54
33 14 37 31 10 17 10.0 71
34 16 34 37 12 15 11.0 84
35 14 34 30 12 14 13.0 74
36 10 32 33 7 16 8.0 71
37 10 29 31 9 9 20.0 63
38 14 36 33 12 15 12.0 71
39 16 29 31 10 17 10.0 76
40 16 35 33 10 13 10.0 69
41 16 37 32 10 15 9.0 74
42 14 34 33 12 16 14.0 75
43 20 38 32 15 16 8.0 54
44 14 35 33 10 12 14.0 52
45 14 38 28 10 15 11.0 69
46 11 37 35 12 11 13.0 68
47 14 38 39 13 15 9.0 65
48 15 33 34 11 15 11.0 75
49 16 36 38 11 17 15.0 74
50 14 38 32 12 13 11.0 75
51 16 32 38 14 16 10.0 72
52 14 32 30 10 14 14.0 67
53 12 32 33 12 11 18.0 63
54 16 34 38 13 12 14.0 62
55 9 32 32 5 12 11.0 63
56 14 37 35 6 15 14.5 76
57 16 39 34 12 16 13.0 74
58 16 29 34 12 15 9.0 67
59 15 37 36 11 12 10.0 73
60 16 35 34 10 12 15.0 70
61 12 30 28 7 8 20.0 53
62 16 38 34 12 13 12.0 77
63 16 34 35 14 11 12.0 80
64 14 31 35 11 14 14.0 52
65 16 34 31 12 15 13.0 54
66 17 35 37 13 10 11.0 80
67 18 36 35 14 11 17.0 66
68 18 30 27 11 12 12.0 73
69 12 39 40 12 15 13.0 63
70 16 35 37 12 15 14.0 69
71 10 38 36 8 14 13.0 67
72 14 31 38 11 16 15.0 54
73 18 34 39 14 15 13.0 81
74 18 38 41 14 15 10.0 69
75 16 34 27 12 13 11.0 84
76 17 39 30 9 12 19.0 80
77 16 37 37 13 17 13.0 70
78 16 34 31 11 13 17.0 69
79 13 28 31 12 15 13.0 77
80 16 37 27 12 13 9.0 54
81 16 33 36 12 15 11.0 79
82 16 35 37 12 15 9.0 71
83 15 37 33 12 16 12.0 73
84 15 32 34 11 15 12.0 72
85 16 33 31 10 14 13.0 77
86 14 38 39 9 15 13.0 75
87 16 33 34 12 14 12.0 69
88 16 29 32 12 13 15.0 54
89 15 33 33 12 7 22.0 70
90 12 31 36 9 17 13.0 73
91 17 36 32 15 13 15.0 54
92 16 35 41 12 15 13.0 77
93 15 32 28 12 14 15.0 82
94 13 29 30 12 13 12.5 80
95 16 39 36 10 16 11.0 80
96 16 37 35 13 12 16.0 69
97 16 35 31 9 14 11.0 78
98 16 37 34 12 17 11.0 81
99 14 32 36 10 15 10.0 76
100 16 38 36 14 17 10.0 76
101 16 37 35 11 12 16.0 73
102 20 36 37 15 16 12.0 85
103 15 32 28 11 11 11.0 66
104 16 33 39 11 15 16.0 79
105 13 40 32 12 9 19.0 68
106 17 38 35 12 16 11.0 76
107 16 41 39 12 15 16.0 71
108 16 36 35 11 10 15.0 54
109 12 43 42 7 10 24.0 46
110 16 30 34 12 15 14.0 85
111 16 31 33 14 11 15.0 74
112 17 32 41 11 13 11.0 88
113 13 32 33 11 14 15.0 38
114 12 37 34 10 18 12.0 76
115 18 37 32 13 16 10.0 86
116 14 33 40 13 14 14.0 54
117 14 34 40 8 14 13.0 67
118 13 33 35 11 14 9.0 69
119 16 38 36 12 14 15.0 90
120 13 33 37 11 12 15.0 54
121 16 31 27 13 14 14.0 76
122 13 38 39 12 15 11.0 89
123 16 37 38 14 15 8.0 76
124 15 36 31 13 15 11.0 73
125 16 31 33 15 13 11.0 79
126 15 39 32 10 17 8.0 90
127 17 44 39 11 17 10.0 74
128 15 33 36 9 19 11.0 81
129 12 35 33 11 15 13.0 72
130 16 32 33 10 13 11.0 71
131 10 28 32 11 9 20.0 66
132 16 40 37 8 15 10.0 77
133 12 27 30 11 15 15.0 65
134 14 37 38 12 15 12.0 74
135 15 32 29 12 16 14.0 85
136 13 28 22 9 11 23.0 54
137 15 34 35 11 14 14.0 63
138 11 30 35 10 11 16.0 54
139 12 35 34 8 15 11.0 64
140 11 31 35 9 13 12.0 69
141 16 32 34 8 15 10.0 54
142 15 30 37 9 16 14.0 84
143 17 30 35 15 14 12.0 86
144 16 31 23 11 15 12.0 77
145 10 40 31 8 16 11.0 89
146 18 32 27 13 16 12.0 76
147 13 36 36 12 11 13.0 60
148 16 32 31 12 12 11.0 75
149 13 35 32 9 9 19.0 73
150 10 38 39 7 16 12.0 85
151 15 42 37 13 13 17.0 79
152 16 34 38 9 16 9.0 71
153 16 35 39 6 12 12.0 72
154 14 38 34 8 9 19.0 69
155 10 33 31 8 13 18.0 78
156 17 36 32 15 13 15.0 54
157 13 32 37 6 14 14.0 69
158 15 33 36 9 19 11.0 81
159 16 34 32 11 13 9.0 84
160 12 32 38 8 12 18.0 84
161 13 34 36 8 13 16.0 69
162 13 27 26 10 10 24.0 66
163 12 31 26 8 14 14.0 81
164 17 38 33 14 16 20.0 82
165 15 34 39 10 10 18.0 72
166 10 24 30 8 11 23.0 54
167 14 30 33 11 14 12.0 78
168 11 26 25 12 12 14.0 74
169 13 34 38 12 9 16.0 82
170 16 27 37 12 9 18.0 73
171 12 37 31 5 11 20.0 55
172 16 36 37 12 16 12.0 72
173 12 41 35 10 9 12.0 78
174 9 29 25 7 13 17.0 59
175 12 36 28 12 16 13.0 72
176 15 32 35 11 13 9.0 78
177 12 37 33 8 9 16.0 68
178 12 30 30 9 12 18.0 69
179 14 31 31 10 16 10.0 67
180 12 38 37 9 11 14.0 74
181 16 36 36 12 14 11.0 54
182 11 35 30 6 13 9.0 67
183 19 31 36 15 15 11.0 70
184 15 38 32 12 14 10.0 80
185 8 22 28 12 16 11.0 89
186 16 32 36 12 13 19.0 76
187 17 36 34 11 14 14.0 74
188 12 39 31 7 15 12.0 87
189 11 28 28 7 13 14.0 54
190 11 32 36 5 11 21.0 61
191 14 32 36 12 11 13.0 38
192 16 38 40 12 14 10.0 75
193 12 32 33 3 15 15.0 69
194 16 35 37 11 11 16.0 62
195 13 32 32 10 15 14.0 72
196 15 37 38 12 12 12.0 70
197 16 34 31 9 14 19.0 79
198 16 33 37 12 14 15.0 87
199 14 33 33 9 8 19.0 62
200 16 26 32 12 13 13.0 77
201 16 30 30 12 9 17.0 69
202 14 24 30 10 15 12.0 69
203 11 34 31 9 17 11.0 75
204 12 34 32 12 13 14.0 54
205 15 33 34 8 15 11.0 72
206 15 34 36 11 15 13.0 74
207 16 35 37 11 14 12.0 85
208 16 35 36 12 16 15.0 52
209 11 36 33 10 13 14.0 70
210 15 34 33 10 16 12.0 84
211 12 34 33 12 9 17.0 64
212 12 41 44 12 16 11.0 84
213 15 32 39 11 11 18.0 87
214 15 30 32 8 10 13.0 79
215 16 35 35 12 11 17.0 67
216 14 28 25 10 15 13.0 65
217 17 33 35 11 17 11.0 85
218 14 39 34 10 14 12.0 83
219 13 36 35 8 8 22.0 61
220 15 36 39 12 15 14.0 82
221 13 35 33 12 11 12.0 76
222 14 38 36 10 16 12.0 58
223 15 33 32 12 10 17.0 72
224 12 31 32 9 15 9.0 72
225 13 34 36 9 9 21.0 38
226 8 32 36 6 16 10.0 78
227 14 31 32 10 19 11.0 54
228 14 33 34 9 12 12.0 63
229 11 34 33 9 8 23.0 66
230 12 34 35 9 11 13.0 70
231 13 34 30 6 14 12.0 71
232 10 33 38 10 9 16.0 67
233 16 32 34 6 15 9.0 58
234 18 41 33 14 13 17.0 72
235 13 34 32 10 16 9.0 72
236 11 36 31 10 11 14.0 70
237 4 37 30 6 12 17.0 76
238 13 36 27 12 13 13.0 50
239 16 29 31 12 10 11.0 72
240 10 37 30 7 11 12.0 72
241 12 27 32 8 12 10.0 88
242 12 35 35 11 8 19.0 53
243 10 28 28 3 12 16.0 58
244 13 35 33 6 12 16.0 66
245 15 37 31 10 15 14.0 82
246 12 29 35 8 11 20.0 69
247 14 32 35 9 13 15.0 68
248 10 36 32 9 14 23.0 44
249 12 19 21 8 10 20.0 56
250 12 21 20 9 12 16.0 53
251 11 31 34 7 15 14.0 70
252 10 33 32 7 13 17.0 78
253 12 36 34 6 13 11.0 71
254 16 33 32 9 13 13.0 72
255 12 37 33 10 12 17.0 68
256 14 34 33 11 12 15.0 67
257 16 35 37 12 9 21.0 75
258 14 31 32 8 9 18.0 62
259 13 37 34 11 15 15.0 67
260 4 35 30 3 10 8.0 83
261 15 27 30 11 14 12.0 64
262 11 34 38 12 15 12.0 68
263 11 40 36 7 7 22.0 62
264 14 29 32 9 14 12.0 72
Belonging_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 Software
5.334627 0.033432 0.042910 0.557382
Happiness Depression Belonging Belonging_Final
0.070714 -0.028988 0.023564 -0.025260
t
-0.004938
> (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 **
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
t -0.004938 0.001684 -2.933 0.00367 **
---
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/1fsje1352115409.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2n8qy1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3e22a1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/44wxh1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/54n6w1352115409.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/6dbob1352115409.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/7se8o1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8pi2i1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9n4pl1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10wbqx1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xo4n1352115409.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/123etl1352115409.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/13c28b1352115409.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/146jlp1352115409.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/15dxwn1352115409.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/16awnb1352115409.tab")
+ }
>
> try(system("convert tmp/1fsje1352115409.ps tmp/1fsje1352115409.png",intern=TRUE))
character(0)
> try(system("convert tmp/2n8qy1352115409.ps tmp/2n8qy1352115409.png",intern=TRUE))
character(0)
> try(system("convert tmp/3e22a1352115409.ps tmp/3e22a1352115409.png",intern=TRUE))
character(0)
> try(system("convert tmp/44wxh1352115409.ps tmp/44wxh1352115409.png",intern=TRUE))
character(0)
> try(system("convert tmp/54n6w1352115409.ps tmp/54n6w1352115409.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dbob1352115409.ps tmp/6dbob1352115409.png",intern=TRUE))
character(0)
> try(system("convert tmp/7se8o1352115409.ps tmp/7se8o1352115409.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pi2i1352115409.ps tmp/8pi2i1352115409.png",intern=TRUE))
character(0)
> try(system("convert tmp/9n4pl1352115409.ps tmp/9n4pl1352115409.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wbqx1352115409.ps tmp/10wbqx1352115409.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.071 1.224 12.336