R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(12.42
+ ,10.4
+ ,12.37
+ ,10.5
+ ,12.53
+ ,10.6
+ ,12.02
+ ,10.6
+ ,11.70
+ ,10.7
+ ,11.67
+ ,10.7
+ ,11.51
+ ,10.8
+ ,11.50
+ ,10.9
+ ,11.77
+ ,10.9
+ ,11.75
+ ,11.0
+ ,11.87
+ ,11.0
+ ,12.18
+ ,10.9
+ ,12.29
+ ,10.9
+ ,12.41
+ ,10.8
+ ,12.55
+ ,10.8
+ ,12.39
+ ,10.8
+ ,12.39
+ ,10.8
+ ,12.40
+ ,10.8
+ ,12.33
+ ,10.8
+ ,12.16
+ ,10.9
+ ,12.12
+ ,10.9
+ ,12.13
+ ,10.8
+ ,11.90
+ ,10.7
+ ,11.84
+ ,10.6
+ ,11.75
+ ,10.6
+ ,11.73
+ ,10.6
+ ,11.73
+ ,10.4
+ ,11.64
+ ,10.2
+ ,11.45
+ ,10.1
+ ,10.78
+ ,10.0
+ ,10.67
+ ,9.9
+ ,10.80
+ ,9.9
+ ,10.76
+ ,9.9
+ ,10.38
+ ,9.9
+ ,9.99
+ ,9.9
+ ,9.94
+ ,10.0
+ ,10.05
+ ,10.0
+ ,9.99
+ ,10.1
+ ,9.48
+ ,10.1
+ ,8.29
+ ,10.1
+ ,8.48
+ ,10.1
+ ,8.58
+ ,10.1
+ ,8.23
+ ,10.0
+ ,7.92
+ ,10.0
+ ,8.04
+ ,10.0
+ ,8.09
+ ,10.0
+ ,8.09
+ ,10.1
+ ,8.27
+ ,10.1
+ ,8.26
+ ,10.1
+ ,8.20
+ ,10.0
+ ,8.11
+ ,10.0
+ ,8.02
+ ,10.0
+ ,8.05
+ ,9.9
+ ,8.10
+ ,9.9
+ ,8.07
+ ,9.8
+ ,7.96
+ ,9.7
+ ,8.18
+ ,9.7
+ ,8.64
+ ,9.6
+ ,8.35
+ ,9.6
+ ,8.27
+ ,9.5
+ ,8.16
+ ,9.4
+ ,7.78
+ ,9.4
+ ,7.67
+ ,9.3
+ ,7.79
+ ,9.2
+ ,7.93
+ ,9.1
+ ,7.95
+ ,8.9
+ ,8.09
+ ,8.8
+ ,8.20
+ ,8.7
+ ,8.24
+ ,8.5
+ ,8.09
+ ,8.3
+ ,8.05
+ ,8.2
+ ,8.14
+ ,8.1
+ ,8.17
+ ,7.9
+ ,8.30
+ ,7.8
+ ,8.51
+ ,7.7
+ ,8.42
+ ,7.6
+ ,8.36
+ ,7.5
+ ,8.35
+ ,7.4
+ ,8.39
+ ,7.3
+ ,8.36
+ ,7.3
+ ,8.43
+ ,7.2
+ ,8.68
+ ,7.1
+ ,9.10
+ ,7.0
+ ,9.40
+ ,6.9
+ ,9.80
+ ,6.8
+ ,10.40
+ ,6.7
+ ,10.26
+ ,6.7
+ ,10.00
+ ,6.6
+ ,9.82
+ ,6.6
+ ,9.75
+ ,6.6
+ ,9.56
+ ,6.5
+ ,10.01
+ ,6.5
+ ,10.30
+ ,6.4
+ ,10.22
+ ,6.4
+ ,10.01
+ ,6.4
+ ,9.95
+ ,6.4
+ ,9.87
+ ,6.4
+ ,9.25
+ ,6.4
+ ,9.23
+ ,6.4
+ ,9.17
+ ,6.3
+ ,9.14
+ ,6.4
+ ,9.26
+ ,6.4
+ ,9.47
+ ,6.4
+ ,9.41
+ ,6.4
+ ,9.22
+ ,6.5
+ ,9.13
+ ,6.5
+ ,9.15
+ ,6.6
+ ,9.13
+ ,6.6
+ ,8.72
+ ,6.7
+ ,8.72
+ ,6.7
+ ,8.81
+ ,6.8
+ ,8.86
+ ,6.9
+ ,8.83
+ ,7.0
+ ,8.92
+ ,7.0
+ ,8.91
+ ,7.1
+ ,9.03
+ ,7.2
+ ,8.77
+ ,7.2
+ ,8.28
+ ,7.4
+ ,8.04
+ ,7.5
+ ,7.95
+ ,7.6
+ ,7.57
+ ,7.8
+ ,7.65
+ ,7.9
+ ,7.37
+ ,8.1
+ ,7.44
+ ,8.2
+ ,7.43
+ ,8.4
+ ,7.23
+ ,8.5
+ ,7.05
+ ,8.7
+ ,7.08
+ ,8.9
+ ,7.22
+ ,9.0
+ ,7.19
+ ,9.2
+ ,6.92
+ ,9.3
+ ,6.59
+ ,9.5
+ ,6.52
+ ,9.6
+ ,6.70
+ ,9.6
+ ,7.14
+ ,9.7
+ ,7.29
+ ,9.8
+ ,7.54
+ ,9.9
+ ,7.98
+ ,9.9
+ ,7.95
+ ,9.8
+ ,8.21
+ ,9.8
+ ,8.58
+ ,9.8
+ ,8.45
+ ,9.8
+ ,8.35
+ ,9.7
+ ,8.30
+ ,9.7
+ ,8.45
+ ,9.7
+ ,8.27
+ ,9.7
+ ,8.16
+ ,9.6
+ ,7.85
+ ,9.6
+ ,7.59
+ ,9.6
+ ,7.33
+ ,9.6
+ ,7.33
+ ,9.6
+ ,7.19
+ ,9.7
+ ,7.04
+ ,9.7
+ ,7.06
+ ,9.8
+ ,6.80
+ ,9.8
+ ,6.70
+ ,9.9
+ ,6.44
+ ,9.9
+ ,6.64
+ ,9.9
+ ,6.84
+ ,9.8
+ ,6.67
+ ,9.7
+ ,6.69
+ ,9.7
+ ,6.78
+ ,9.6
+ ,6.78
+ ,9.5
+ ,6.62
+ ,9.4
+ ,6.45
+ ,9.4
+ ,6.10
+ ,9.3
+ ,6.00
+ ,9.2
+ ,5.90
+ ,9.2
+ ,5.89
+ ,9.2
+ ,5.65
+ ,9.1
+ ,5.85
+ ,9.1
+ ,6.02
+ ,9.1
+ ,5.90
+ ,9.1
+ ,5.83
+ ,9.2
+ ,5.64
+ ,9.3
+ ,5.75
+ ,9.3
+ ,5.69
+ ,9.3
+ ,5.69
+ ,9.3
+ ,5.68
+ ,9.3
+ ,5.45
+ ,9.4
+ ,5.22
+ ,9.4
+ ,5.11
+ ,9.4
+ ,5.03
+ ,9.5
+ ,5.03
+ ,9.5
+ ,5.09
+ ,9.4
+ ,4.96
+ ,9.4
+ ,4.88
+ ,9.3
+ ,4.66
+ ,9.4
+ ,4.34
+ ,9.4
+ ,4.28
+ ,9.2
+ ,4.33
+ ,9.1
+ ,4.09
+ ,9.1
+ ,3.90
+ ,9.1
+ ,4.04
+ ,9.0
+ ,4.26
+ ,9.0
+ ,4.11
+ ,8.9
+ ,4.29
+ ,8.8
+ ,4.64
+ ,8.7
+ ,4.94
+ ,8.5
+ ,5.18
+ ,8.3
+ ,5.34
+ ,8.1
+ ,5.58
+ ,7.9
+ ,5.30
+ ,7.8
+ ,5.41
+ ,7.6
+ ,5.79
+ ,7.4
+ ,5.79
+ ,7.2
+ ,5.62
+ ,7.0
+ ,5.52
+ ,7.0
+ ,5.69
+ ,6.8
+ ,5.53
+ ,6.8
+ ,5.60
+ ,6.7
+ ,5.56
+ ,6.8
+ ,5.63
+ ,6.7
+ ,5.58
+ ,6.7
+ ,5.52
+ ,6.7
+ ,5.28
+ ,6.5
+ ,5.16
+ ,6.3
+ ,5.16
+ ,6.3
+ ,5.08
+ ,6.3
+ ,5.21
+ ,6.5
+ ,5.38
+ ,6.6
+ ,5.33
+ ,6.5
+ ,5.35
+ ,6.3
+ ,5.15
+ ,6.3
+ ,5.14
+ ,6.5
+ ,4.89
+ ,7.0
+ ,4.75
+ ,7.1
+ ,4.97
+ ,7.3
+ ,5.08
+ ,7.3
+ ,5.15
+ ,7.4
+ ,5.37
+ ,7.4
+ ,5.37
+ ,7.3
+ ,5.38
+ ,7.4
+ ,5.24
+ ,7.5
+ ,5.09
+ ,7.7
+ ,4.80
+ ,7.7
+ ,4.60
+ ,7.7
+ ,4.66
+ ,7.7
+ ,4.64
+ ,7.7
+ ,4.46
+ ,7.8
+ ,4.28
+ ,8.0
+ ,4.11
+ ,8.1
+ ,4.15
+ ,8.1
+ ,4.29
+ ,8.2
+ ,3.95
+ ,8.2
+ ,3.74
+ ,8.2
+ ,4.06
+ ,8.1
+ ,4.22
+ ,8.1
+ ,4.25
+ ,8.2
+ ,4.31
+ ,8.3
+ ,4.43
+ ,8.3
+ ,4.38
+ ,8.4
+ ,4.26
+ ,8.5
+ ,4.26
+ ,8.5
+ ,4.07
+ ,8.4
+ ,4.26
+ ,8.0
+ ,4.40
+ ,7.9
+ ,4.46
+ ,8.1
+ ,4.34
+ ,8.5
+ ,4.18
+ ,8.8
+ ,4.11
+ ,8.8
+ ,3.98
+ ,8.6
+ ,3.85
+ ,8.3
+ ,3.66
+ ,8.3
+ ,3.59
+ ,8.3
+ ,3.57
+ ,8.4
+ ,3.76
+ ,8.4
+ ,3.60
+ ,8.5
+ ,3.43
+ ,8.6
+ ,3.26
+ ,8.6
+ ,3.30
+ ,8.6
+ ,3.31
+ ,8.6
+ ,3.14
+ ,8.6
+ ,3.30
+ ,8.5
+ ,3.49
+ ,8.4
+ ,3.39
+ ,8.4
+ ,3.37
+ ,8.4
+ ,3.54
+ ,8.5
+ ,3.70
+ ,8.5
+ ,3.96
+ ,8.6
+ ,4.03
+ ,8.6
+ ,4.02
+ ,8.4
+ ,4.04
+ ,8.2
+ ,3.92
+ ,8.0
+ ,3.79
+ ,8.0
+ ,3.83
+ ,8.0
+ ,3.76
+ ,8.0
+ ,3.82
+ ,7.9
+ ,4.06
+ ,7.9
+ ,4.11
+ ,7.8
+ ,4.01
+ ,7.8
+ ,4.22
+ ,8.0)
+ ,dim=c(2
+ ,292)
+ ,dimnames=list(c('Rente'
+ ,'werkloosheid')
+ ,1:292))
> y <- array(NA,dim=c(2,292),dimnames=list(c('Rente','werkloosheid'),1:292))
> 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 = '2'
> #'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.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
werkloosheid Rente t
1 10.4 12.42 1
2 10.5 12.37 2
3 10.6 12.53 3
4 10.6 12.02 4
5 10.7 11.70 5
6 10.7 11.67 6
7 10.8 11.51 7
8 10.9 11.50 8
9 10.9 11.77 9
10 11.0 11.75 10
11 11.0 11.87 11
12 10.9 12.18 12
13 10.9 12.29 13
14 10.8 12.41 14
15 10.8 12.55 15
16 10.8 12.39 16
17 10.8 12.39 17
18 10.8 12.40 18
19 10.8 12.33 19
20 10.9 12.16 20
21 10.9 12.12 21
22 10.8 12.13 22
23 10.7 11.90 23
24 10.6 11.84 24
25 10.6 11.75 25
26 10.6 11.73 26
27 10.4 11.73 27
28 10.2 11.64 28
29 10.1 11.45 29
30 10.0 10.78 30
31 9.9 10.67 31
32 9.9 10.80 32
33 9.9 10.76 33
34 9.9 10.38 34
35 9.9 9.99 35
36 10.0 9.94 36
37 10.0 10.05 37
38 10.1 9.99 38
39 10.1 9.48 39
40 10.1 8.29 40
41 10.1 8.48 41
42 10.1 8.58 42
43 10.0 8.23 43
44 10.0 7.92 44
45 10.0 8.04 45
46 10.0 8.09 46
47 10.1 8.09 47
48 10.1 8.27 48
49 10.1 8.26 49
50 10.0 8.20 50
51 10.0 8.11 51
52 10.0 8.02 52
53 9.9 8.05 53
54 9.9 8.10 54
55 9.8 8.07 55
56 9.7 7.96 56
57 9.7 8.18 57
58 9.6 8.64 58
59 9.6 8.35 59
60 9.5 8.27 60
61 9.4 8.16 61
62 9.4 7.78 62
63 9.3 7.67 63
64 9.2 7.79 64
65 9.1 7.93 65
66 8.9 7.95 66
67 8.8 8.09 67
68 8.7 8.20 68
69 8.5 8.24 69
70 8.3 8.09 70
71 8.2 8.05 71
72 8.1 8.14 72
73 7.9 8.17 73
74 7.8 8.30 74
75 7.7 8.51 75
76 7.6 8.42 76
77 7.5 8.36 77
78 7.4 8.35 78
79 7.3 8.39 79
80 7.3 8.36 80
81 7.2 8.43 81
82 7.1 8.68 82
83 7.0 9.10 83
84 6.9 9.40 84
85 6.8 9.80 85
86 6.7 10.40 86
87 6.7 10.26 87
88 6.6 10.00 88
89 6.6 9.82 89
90 6.6 9.75 90
91 6.5 9.56 91
92 6.5 10.01 92
93 6.4 10.30 93
94 6.4 10.22 94
95 6.4 10.01 95
96 6.4 9.95 96
97 6.4 9.87 97
98 6.4 9.25 98
99 6.4 9.23 99
100 6.3 9.17 100
101 6.4 9.14 101
102 6.4 9.26 102
103 6.4 9.47 103
104 6.4 9.41 104
105 6.5 9.22 105
106 6.5 9.13 106
107 6.6 9.15 107
108 6.6 9.13 108
109 6.7 8.72 109
110 6.7 8.72 110
111 6.8 8.81 111
112 6.9 8.86 112
113 7.0 8.83 113
114 7.0 8.92 114
115 7.1 8.91 115
116 7.2 9.03 116
117 7.2 8.77 117
118 7.4 8.28 118
119 7.5 8.04 119
120 7.6 7.95 120
121 7.8 7.57 121
122 7.9 7.65 122
123 8.1 7.37 123
124 8.2 7.44 124
125 8.4 7.43 125
126 8.5 7.23 126
127 8.7 7.05 127
128 8.9 7.08 128
129 9.0 7.22 129
130 9.2 7.19 130
131 9.3 6.92 131
132 9.5 6.59 132
133 9.6 6.52 133
134 9.6 6.70 134
135 9.7 7.14 135
136 9.8 7.29 136
137 9.9 7.54 137
138 9.9 7.98 138
139 9.8 7.95 139
140 9.8 8.21 140
141 9.8 8.58 141
142 9.8 8.45 142
143 9.7 8.35 143
144 9.7 8.30 144
145 9.7 8.45 145
146 9.7 8.27 146
147 9.6 8.16 147
148 9.6 7.85 148
149 9.6 7.59 149
150 9.6 7.33 150
151 9.6 7.33 151
152 9.7 7.19 152
153 9.7 7.04 153
154 9.8 7.06 154
155 9.8 6.80 155
156 9.9 6.70 156
157 9.9 6.44 157
158 9.9 6.64 158
159 9.8 6.84 159
160 9.7 6.67 160
161 9.7 6.69 161
162 9.6 6.78 162
163 9.5 6.78 163
164 9.4 6.62 164
165 9.4 6.45 165
166 9.3 6.10 166
167 9.2 6.00 167
168 9.2 5.90 168
169 9.2 5.89 169
170 9.1 5.65 170
171 9.1 5.85 171
172 9.1 6.02 172
173 9.1 5.90 173
174 9.2 5.83 174
175 9.3 5.64 175
176 9.3 5.75 176
177 9.3 5.69 177
178 9.3 5.69 178
179 9.3 5.68 179
180 9.4 5.45 180
181 9.4 5.22 181
182 9.4 5.11 182
183 9.5 5.03 183
184 9.5 5.03 184
185 9.4 5.09 185
186 9.4 4.96 186
187 9.3 4.88 187
188 9.4 4.66 188
189 9.4 4.34 189
190 9.2 4.28 190
191 9.1 4.33 191
192 9.1 4.09 192
193 9.1 3.90 193
194 9.0 4.04 194
195 9.0 4.26 195
196 8.9 4.11 196
197 8.8 4.29 197
198 8.7 4.64 198
199 8.5 4.94 199
200 8.3 5.18 200
201 8.1 5.34 201
202 7.9 5.58 202
203 7.8 5.30 203
204 7.6 5.41 204
205 7.4 5.79 205
206 7.2 5.79 206
207 7.0 5.62 207
208 7.0 5.52 208
209 6.8 5.69 209
210 6.8 5.53 210
211 6.7 5.60 211
212 6.8 5.56 212
213 6.7 5.63 213
214 6.7 5.58 214
215 6.7 5.52 215
216 6.5 5.28 216
217 6.3 5.16 217
218 6.3 5.16 218
219 6.3 5.08 219
220 6.5 5.21 220
221 6.6 5.38 221
222 6.5 5.33 222
223 6.3 5.35 223
224 6.3 5.15 224
225 6.5 5.14 225
226 7.0 4.89 226
227 7.1 4.75 227
228 7.3 4.97 228
229 7.3 5.08 229
230 7.4 5.15 230
231 7.4 5.37 231
232 7.3 5.37 232
233 7.4 5.38 233
234 7.5 5.24 234
235 7.7 5.09 235
236 7.7 4.80 236
237 7.7 4.60 237
238 7.7 4.66 238
239 7.7 4.64 239
240 7.8 4.46 240
241 8.0 4.28 241
242 8.1 4.11 242
243 8.1 4.15 243
244 8.2 4.29 244
245 8.2 3.95 245
246 8.2 3.74 246
247 8.1 4.06 247
248 8.1 4.22 248
249 8.2 4.25 249
250 8.3 4.31 250
251 8.3 4.43 251
252 8.4 4.38 252
253 8.5 4.26 253
254 8.5 4.26 254
255 8.4 4.07 255
256 8.0 4.26 256
257 7.9 4.40 257
258 8.1 4.46 258
259 8.5 4.34 259
260 8.8 4.18 260
261 8.8 4.11 261
262 8.6 3.98 262
263 8.3 3.85 263
264 8.3 3.66 264
265 8.3 3.59 265
266 8.4 3.57 266
267 8.4 3.76 267
268 8.5 3.60 268
269 8.6 3.43 269
270 8.6 3.26 270
271 8.6 3.30 271
272 8.6 3.31 272
273 8.6 3.14 273
274 8.5 3.30 274
275 8.4 3.49 275
276 8.4 3.39 276
277 8.4 3.37 277
278 8.5 3.54 278
279 8.5 3.70 279
280 8.6 3.96 280
281 8.6 4.03 281
282 8.4 4.02 282
283 8.2 4.04 283
284 8.0 3.92 284
285 8.0 3.79 285
286 8.0 3.83 286
287 8.0 3.76 287
288 7.9 3.82 288
289 7.9 4.06 289
290 7.8 4.11 290
291 7.8 4.01 291
292 8.0 4.22 292
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Rente t
12.89188 -0.29215 -0.01504
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.4088 -0.9575 0.2986 0.7677 1.8648
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.891877 0.860742 14.978 < 2e-16 ***
Rente -0.292148 0.075362 -3.877 0.000131 ***
t -0.015041 0.002286 -6.581 2.20e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.172 on 289 degrees of freedom
Multiple R-squared: 0.2254, Adjusted R-squared: 0.22
F-statistic: 42.04 on 2 and 289 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,] 3.640228e-05 7.280456e-05 9.999636e-01
[2,] 8.949849e-07 1.789970e-06 9.999991e-01
[3,] 2.423000e-08 4.845999e-08 1.000000e+00
[4,] 3.275781e-09 6.551562e-09 1.000000e+00
[5,] 9.074776e-11 1.814955e-10 1.000000e+00
[6,] 6.678296e-12 1.335659e-11 1.000000e+00
[7,] 1.589718e-11 3.179436e-11 1.000000e+00
[8,] 2.748895e-12 5.497791e-12 1.000000e+00
[9,] 2.234321e-12 4.468642e-12 1.000000e+00
[10,] 3.755298e-13 7.510596e-13 1.000000e+00
[11,] 1.115232e-13 2.230463e-13 1.000000e+00
[12,] 2.787814e-14 5.575628e-14 1.000000e+00
[13,] 6.232519e-15 1.246504e-14 1.000000e+00
[14,] 1.788932e-15 3.577863e-15 1.000000e+00
[15,] 2.555029e-16 5.110058e-16 1.000000e+00
[16,] 4.423282e-17 8.846564e-17 1.000000e+00
[17,] 3.299751e-17 6.599501e-17 1.000000e+00
[18,] 2.366073e-16 4.732146e-16 1.000000e+00
[19,] 1.716488e-15 3.432976e-15 1.000000e+00
[20,] 2.787856e-15 5.575713e-15 1.000000e+00
[21,] 2.138503e-15 4.277006e-15 1.000000e+00
[22,] 7.869196e-15 1.573839e-14 1.000000e+00
[23,] 7.949180e-14 1.589836e-13 1.000000e+00
[24,] 3.519695e-13 7.039389e-13 1.000000e+00
[25,] 3.720798e-13 7.441597e-13 1.000000e+00
[26,] 2.476472e-13 4.952944e-13 1.000000e+00
[27,] 1.303388e-13 2.606777e-13 1.000000e+00
[28,] 5.267873e-14 1.053575e-13 1.000000e+00
[29,] 1.462590e-14 2.925179e-14 1.000000e+00
[30,] 4.206138e-15 8.412277e-15 1.000000e+00
[31,] 1.592172e-15 3.184345e-15 1.000000e+00
[32,] 5.231018e-16 1.046204e-15 1.000000e+00
[33,] 2.536065e-16 5.072129e-16 1.000000e+00
[34,] 1.964531e-16 3.929062e-16 1.000000e+00
[35,] 4.252355e-16 8.504711e-16 1.000000e+00
[36,] 2.807562e-16 5.615124e-16 1.000000e+00
[37,] 1.339835e-16 2.679670e-16 1.000000e+00
[38,] 4.489165e-17 8.978331e-17 1.000000e+00
[39,] 1.537349e-17 3.074698e-17 1.000000e+00
[40,] 4.851430e-18 9.702859e-18 1.000000e+00
[41,] 1.485284e-18 2.970568e-18 1.000000e+00
[42,] 6.322885e-19 1.264577e-18 1.000000e+00
[43,] 2.512902e-19 5.025803e-19 1.000000e+00
[44,] 9.879186e-20 1.975837e-19 1.000000e+00
[45,] 2.866930e-20 5.733861e-20 1.000000e+00
[46,] 8.437003e-21 1.687401e-20 1.000000e+00
[47,] 2.519737e-21 5.039474e-21 1.000000e+00
[48,] 6.295227e-22 1.259045e-21 1.000000e+00
[49,] 1.585808e-22 3.171615e-22 1.000000e+00
[50,] 3.822537e-23 7.645074e-23 1.000000e+00
[51,] 9.831959e-24 1.966392e-23 1.000000e+00
[52,] 2.540826e-24 5.081652e-24 1.000000e+00
[53,] 9.170718e-25 1.834144e-24 1.000000e+00
[54,] 2.723030e-25 5.446059e-25 1.000000e+00
[55,] 9.966869e-26 1.993374e-25 1.000000e+00
[56,] 4.722084e-26 9.444168e-26 1.000000e+00
[57,] 1.696916e-26 3.393831e-26 1.000000e+00
[58,] 8.098352e-27 1.619670e-26 1.000000e+00
[59,] 5.648564e-27 1.129713e-26 1.000000e+00
[60,] 5.766992e-27 1.153398e-26 1.000000e+00
[61,] 1.814851e-26 3.629702e-26 1.000000e+00
[62,] 6.583782e-26 1.316756e-25 1.000000e+00
[63,] 2.448964e-25 4.897929e-25 1.000000e+00
[64,] 2.002593e-24 4.005185e-24 1.000000e+00
[65,] 3.253611e-23 6.507223e-23 1.000000e+00
[66,] 3.593563e-22 7.187125e-22 1.000000e+00
[67,] 2.630170e-21 5.260340e-21 1.000000e+00
[68,] 2.802327e-20 5.604653e-20 1.000000e+00
[69,] 1.722599e-19 3.445198e-19 1.000000e+00
[70,] 6.044756e-19 1.208951e-18 1.000000e+00
[71,] 1.865639e-18 3.731278e-18 1.000000e+00
[72,] 5.220062e-18 1.044012e-17 1.000000e+00
[73,] 1.297354e-17 2.594708e-17 1.000000e+00
[74,] 2.767808e-17 5.535616e-17 1.000000e+00
[75,] 3.939439e-17 7.878878e-17 1.000000e+00
[76,] 5.157900e-17 1.031580e-16 1.000000e+00
[77,] 5.051312e-17 1.010262e-16 1.000000e+00
[78,] 3.296534e-17 6.593067e-17 1.000000e+00
[79,] 1.754226e-17 3.508452e-17 1.000000e+00
[80,] 7.755357e-18 1.551071e-17 1.000000e+00
[81,] 3.113127e-18 6.226254e-18 1.000000e+00
[82,] 1.260186e-18 2.520372e-18 1.000000e+00
[83,] 5.360813e-19 1.072163e-18 1.000000e+00
[84,] 2.361073e-19 4.722147e-19 1.000000e+00
[85,] 1.049490e-19 2.098980e-19 1.000000e+00
[86,] 5.296197e-20 1.059239e-19 1.000000e+00
[87,] 2.376177e-20 4.752354e-20 1.000000e+00
[88,] 1.074865e-20 2.149731e-20 1.000000e+00
[89,] 5.061124e-21 1.012225e-20 1.000000e+00
[90,] 2.464042e-21 4.928084e-21 1.000000e+00
[91,] 1.271521e-21 2.543041e-21 1.000000e+00
[92,] 6.995351e-22 1.399070e-21 1.000000e+00
[93,] 4.340748e-22 8.681497e-22 1.000000e+00
[94,] 2.876019e-22 5.752038e-22 1.000000e+00
[95,] 2.109339e-22 4.218679e-22 1.000000e+00
[96,] 1.708418e-22 3.416837e-22 1.000000e+00
[97,] 1.568465e-22 3.136930e-22 1.000000e+00
[98,] 1.754723e-22 3.509446e-22 1.000000e+00
[99,] 2.232408e-22 4.464817e-22 1.000000e+00
[100,] 3.764408e-22 7.528815e-22 1.000000e+00
[101,] 7.156158e-22 1.431232e-21 1.000000e+00
[102,] 2.110931e-21 4.221862e-21 1.000000e+00
[103,] 7.111267e-21 1.422253e-20 1.000000e+00
[104,] 2.783465e-20 5.566930e-20 1.000000e+00
[105,] 1.267733e-19 2.535467e-19 1.000000e+00
[106,] 9.512602e-19 1.902520e-18 1.000000e+00
[107,] 1.112509e-17 2.225018e-17 1.000000e+00
[108,] 1.758682e-16 3.517364e-16 1.000000e+00
[109,] 2.733668e-15 5.467335e-15 1.000000e+00
[110,] 5.206914e-14 1.041383e-13 1.000000e+00
[111,] 1.239378e-12 2.478756e-12 1.000000e+00
[112,] 1.876309e-11 3.752619e-11 1.000000e+00
[113,] 2.858852e-10 5.717704e-10 1.000000e+00
[114,] 3.806453e-09 7.612906e-09 1.000000e+00
[115,] 4.686968e-08 9.373936e-08 1.000000e+00
[116,] 5.292703e-07 1.058541e-06 9.999995e-01
[117,] 5.386780e-06 1.077356e-05 9.999946e-01
[118,] 4.667884e-05 9.335768e-05 9.999533e-01
[119,] 3.280922e-04 6.561845e-04 9.996719e-01
[120,] 1.999338e-03 3.998677e-03 9.980007e-01
[121,] 8.502680e-03 1.700536e-02 9.914973e-01
[122,] 2.907138e-02 5.814275e-02 9.709286e-01
[123,] 8.305403e-02 1.661081e-01 9.169460e-01
[124,] 1.876891e-01 3.753782e-01 8.123109e-01
[125,] 3.506466e-01 7.012933e-01 6.493534e-01
[126,] 5.234606e-01 9.530789e-01 4.765394e-01
[127,] 6.822662e-01 6.354675e-01 3.177338e-01
[128,] 8.048215e-01 3.903571e-01 1.951785e-01
[129,] 8.876381e-01 2.247238e-01 1.123619e-01
[130,] 9.456246e-01 1.087508e-01 5.437539e-02
[131,] 9.769687e-01 4.606270e-02 2.303135e-02
[132,] 9.917860e-01 1.642792e-02 8.213960e-03
[133,] 9.974706e-01 5.058810e-03 2.529405e-03
[134,] 9.991148e-01 1.770422e-03 8.852108e-04
[135,] 9.997071e-01 5.857664e-04 2.928832e-04
[136,] 9.999120e-01 1.759226e-04 8.796128e-05
[137,] 9.999717e-01 5.669866e-05 2.834933e-05
[138,] 9.999891e-01 2.184311e-05 1.092156e-05
[139,] 9.999957e-01 8.634120e-06 4.317060e-06
[140,] 9.999984e-01 3.159952e-06 1.579976e-06
[141,] 9.999994e-01 1.191926e-06 5.959629e-07
[142,] 9.999997e-01 5.126888e-07 2.563444e-07
[143,] 9.999999e-01 2.419067e-07 1.209533e-07
[144,] 9.999999e-01 1.239917e-07 6.199587e-08
[145,] 1.000000e+00 7.002253e-08 3.501127e-08
[146,] 1.000000e+00 3.785425e-08 1.892713e-08
[147,] 1.000000e+00 1.829084e-08 9.145418e-09
[148,] 1.000000e+00 9.212847e-09 4.606423e-09
[149,] 1.000000e+00 3.577799e-09 1.788900e-09
[150,] 1.000000e+00 1.596542e-09 7.982708e-10
[151,] 1.000000e+00 6.032444e-10 3.016222e-10
[152,] 1.000000e+00 2.726039e-10 1.363019e-10
[153,] 1.000000e+00 8.851269e-11 4.425634e-11
[154,] 1.000000e+00 2.226026e-11 1.113013e-11
[155,] 1.000000e+00 7.217356e-12 3.608678e-12
[156,] 1.000000e+00 1.810721e-12 9.053603e-13
[157,] 1.000000e+00 3.582607e-13 1.791303e-13
[158,] 1.000000e+00 6.220935e-14 3.110468e-14
[159,] 1.000000e+00 1.405254e-14 7.026268e-15
[160,] 1.000000e+00 3.486734e-15 1.743367e-15
[161,] 1.000000e+00 2.001702e-15 1.000851e-15
[162,] 1.000000e+00 1.513141e-15 7.565705e-16
[163,] 1.000000e+00 1.245523e-15 6.227617e-16
[164,] 1.000000e+00 9.436845e-16 4.718423e-16
[165,] 1.000000e+00 1.170682e-15 5.853409e-16
[166,] 1.000000e+00 9.628607e-16 4.814304e-16
[167,] 1.000000e+00 4.679405e-16 2.339703e-16
[168,] 1.000000e+00 2.568703e-16 1.284352e-16
[169,] 1.000000e+00 1.105272e-16 5.526359e-17
[170,] 1.000000e+00 5.202165e-17 2.601083e-17
[171,] 1.000000e+00 1.366513e-17 6.832566e-18
[172,] 1.000000e+00 3.267925e-18 1.633963e-18
[173,] 1.000000e+00 5.448230e-19 2.724115e-19
[174,] 1.000000e+00 6.029323e-20 3.014661e-20
[175,] 1.000000e+00 8.480454e-21 4.240227e-21
[176,] 1.000000e+00 2.389768e-21 1.194884e-21
[177,] 1.000000e+00 8.117180e-22 4.058590e-22
[178,] 1.000000e+00 2.002620e-22 1.001310e-22
[179,] 1.000000e+00 3.374600e-23 1.687300e-23
[180,] 1.000000e+00 4.063754e-24 2.031877e-24
[181,] 1.000000e+00 6.252471e-25 3.126236e-25
[182,] 1.000000e+00 1.538682e-25 7.693411e-26
[183,] 1.000000e+00 5.166400e-26 2.583200e-26
[184,] 1.000000e+00 5.110936e-26 2.555468e-26
[185,] 1.000000e+00 8.840720e-26 4.420360e-26
[186,] 1.000000e+00 1.506235e-25 7.531174e-26
[187,] 1.000000e+00 3.719900e-25 1.859950e-25
[188,] 1.000000e+00 1.063101e-24 5.315503e-25
[189,] 1.000000e+00 2.859465e-24 1.429733e-24
[190,] 1.000000e+00 4.973882e-24 2.486941e-24
[191,] 1.000000e+00 1.184758e-23 5.923788e-24
[192,] 1.000000e+00 2.184057e-23 1.092029e-23
[193,] 1.000000e+00 1.652448e-23 8.262238e-24
[194,] 1.000000e+00 6.395588e-24 3.197794e-24
[195,] 1.000000e+00 1.578060e-24 7.890302e-25
[196,] 1.000000e+00 3.630766e-25 1.815383e-25
[197,] 1.000000e+00 6.022619e-26 3.011309e-26
[198,] 1.000000e+00 3.355145e-26 1.677573e-26
[199,] 1.000000e+00 2.541938e-26 1.270969e-26
[200,] 1.000000e+00 1.152724e-26 5.763619e-27
[201,] 1.000000e+00 1.024151e-26 5.120755e-27
[202,] 1.000000e+00 2.204986e-26 1.102493e-26
[203,] 1.000000e+00 5.382151e-26 2.691076e-26
[204,] 1.000000e+00 1.410868e-25 7.054338e-26
[205,] 1.000000e+00 4.083493e-25 2.041746e-25
[206,] 1.000000e+00 1.187655e-24 5.938276e-25
[207,] 1.000000e+00 3.449083e-24 1.724541e-24
[208,] 1.000000e+00 1.011161e-23 5.055803e-24
[209,] 1.000000e+00 2.988215e-23 1.494108e-23
[210,] 1.000000e+00 8.780972e-23 4.390486e-23
[211,] 1.000000e+00 1.404505e-22 7.022525e-23
[212,] 1.000000e+00 6.644818e-23 3.322409e-23
[213,] 1.000000e+00 2.366777e-23 1.183388e-23
[214,] 1.000000e+00 3.958753e-24 1.979376e-24
[215,] 1.000000e+00 2.514677e-24 1.257338e-24
[216,] 1.000000e+00 3.569843e-24 1.784922e-24
[217,] 1.000000e+00 2.293628e-24 1.146814e-24
[218,] 1.000000e+00 2.771122e-25 1.385561e-25
[219,] 1.000000e+00 3.073261e-27 1.536631e-27
[220,] 1.000000e+00 5.263057e-29 2.631528e-29
[221,] 1.000000e+00 1.108555e-29 5.542773e-30
[222,] 1.000000e+00 1.346485e-30 6.732424e-31
[223,] 1.000000e+00 1.802858e-30 9.014290e-31
[224,] 1.000000e+00 3.063637e-30 1.531818e-30
[225,] 1.000000e+00 9.443442e-30 4.721721e-30
[226,] 1.000000e+00 4.130415e-29 2.065207e-29
[227,] 1.000000e+00 1.205960e-28 6.029802e-29
[228,] 1.000000e+00 4.595584e-28 2.297792e-28
[229,] 1.000000e+00 1.737219e-27 8.686095e-28
[230,] 1.000000e+00 8.176126e-27 4.088063e-27
[231,] 1.000000e+00 2.569324e-26 1.284662e-26
[232,] 1.000000e+00 4.576475e-26 2.288237e-26
[233,] 1.000000e+00 7.844149e-26 3.922074e-26
[234,] 1.000000e+00 9.841279e-26 4.920640e-26
[235,] 1.000000e+00 1.119360e-25 5.596799e-26
[236,] 1.000000e+00 2.446688e-25 1.223344e-25
[237,] 1.000000e+00 5.900506e-25 2.950253e-25
[238,] 1.000000e+00 1.397578e-24 6.987890e-25
[239,] 1.000000e+00 5.829254e-24 2.914627e-24
[240,] 1.000000e+00 1.416112e-23 7.080561e-24
[241,] 1.000000e+00 1.745273e-23 8.726367e-24
[242,] 1.000000e+00 1.991129e-23 9.955643e-24
[243,] 1.000000e+00 2.978591e-23 1.489296e-23
[244,] 1.000000e+00 8.108220e-23 4.054110e-23
[245,] 1.000000e+00 3.612718e-22 1.806359e-22
[246,] 1.000000e+00 1.791895e-21 8.959473e-22
[247,] 1.000000e+00 9.617659e-21 4.808830e-21
[248,] 1.000000e+00 4.870165e-20 2.435083e-20
[249,] 1.000000e+00 2.359967e-19 1.179983e-19
[250,] 1.000000e+00 1.182237e-18 5.911187e-19
[251,] 1.000000e+00 1.139828e-18 5.699138e-19
[252,] 1.000000e+00 2.358348e-19 1.179174e-19
[253,] 1.000000e+00 1.279297e-19 6.396484e-20
[254,] 1.000000e+00 6.701885e-19 3.350943e-19
[255,] 1.000000e+00 2.317186e-18 1.158593e-18
[256,] 1.000000e+00 4.607944e-18 2.303972e-18
[257,] 1.000000e+00 2.261603e-17 1.130802e-17
[258,] 1.000000e+00 8.436685e-17 4.218342e-17
[259,] 1.000000e+00 1.570530e-16 7.852648e-17
[260,] 1.000000e+00 1.367588e-16 6.837938e-17
[261,] 1.000000e+00 1.805109e-16 9.025543e-17
[262,] 1.000000e+00 5.002323e-17 2.501161e-17
[263,] 1.000000e+00 8.206173e-18 4.103086e-18
[264,] 1.000000e+00 9.527488e-18 4.763744e-18
[265,] 1.000000e+00 5.251272e-17 2.625636e-17
[266,] 1.000000e+00 3.576045e-16 1.788022e-16
[267,] 1.000000e+00 3.352222e-15 1.676111e-15
[268,] 1.000000e+00 2.384022e-14 1.192011e-14
[269,] 1.000000e+00 2.600292e-13 1.300146e-13
[270,] 1.000000e+00 8.610868e-13 4.305434e-13
[271,] 1.000000e+00 7.457759e-12 3.728880e-12
[272,] 1.000000e+00 8.100350e-11 4.050175e-11
[273,] 1.000000e+00 6.908850e-10 3.454425e-10
[274,] 1.000000e+00 5.299331e-09 2.649666e-09
[275,] 1.000000e+00 3.011698e-08 1.505849e-08
[276,] 1.000000e+00 4.628825e-08 2.314412e-08
[277,] 9.999999e-01 1.124478e-07 5.622388e-08
[278,] 9.999995e-01 9.268615e-07 4.634308e-07
[279,] 9.999933e-01 1.334373e-05 6.671865e-06
[280,] 9.999054e-01 1.891797e-04 9.458985e-05
[281,] 9.988354e-01 2.329234e-03 1.164617e-03
> postscript(file="/var/www/html/rcomp/tmp/13h0d1292972401.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23h0d1292972401.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3v80g1292972401.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4v80g1292972401.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5v80g1292972401.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 = 292
Frequency = 1
1 2 3 4 5 6
1.151637044 1.252070769 1.413855484 1.279901322 1.301455200 1.307731876
7 8 9 10 11 12
1.376029366 1.488148993 1.582069943 1.691268095 1.741366907 1.746973760
13 14 15 16 17 18
1.794151097 1.744249910 1.800191674 1.768489165 1.783530268 1.801492847
19 20 21 22 23 24
1.796083619 1.861459634 1.864814834 1.782777413 1.630624573 1.528136821
25 26 27 28 29 30
1.516884642 1.526082794 1.341123897 1.129871718 0.989404781 0.708707006
31 32 33 34 35 36
0.591611875 0.644632164 0.647987364 0.552012387 0.453115934 0.553549658
37 38 39 40 41 42
0.600726995 0.698239243 0.564285081 0.231670565 0.302219708 0.346475569
43 44 45 46 47 48
0.159265019 0.083740373 0.133839186 0.163487668 0.278528771 0.346156438
49 50 51 52 53 54
0.358276066 0.255788314 0.244536135 0.233283956 0.157089487 0.186737969
55 56 57 58 59 60
0.093014645 -0.024080486 0.055233085 0.104662075 0.034980380 -0.073350324
61 62 63 64 65 66
-0.190445454 -0.286420431 -0.403515562 -0.453416749 -0.497474985 -0.676590930
67 68 69 70 71 72
-0.720649166 -0.773471829 -0.946744822 -1.175525856 -1.272170656 -1.330836271
73 74 75 76 77 78
-1.507030740 -1.554010452 -1.577618357 -1.688870536 -1.791358287 -1.879238660
79 80 81 82 83 84
-1.952511654 -1.946234978 -2.010743544 -2.022665546 -1.984922460 -1.982237082
85 86 87 88 89 90
-1.950336947 -1.860007297 -1.885866855 -2.046784122 -2.084329583 -2.089738811
91 92 93 94 95 96
-2.230205748 -2.083698234 -2.083934332 -2.092265036 -2.138574924 -2.141062676
97 98 99 100 101 102
-2.149393379 -2.315483775 -2.306285623 -2.408773375 -2.302496699 -2.252397887
103 104 105 106 107 108
-2.176005792 -2.178493543 -2.118960480 -2.130212659 -2.009328604 -2.000130453
109 110 111 112 113 114
-2.004869857 -1.989828754 -1.848494369 -1.718845887 -1.612569211 -1.571234825
115 116 117 118 119 120
-1.459115198 -1.309016385 -1.369933653 -1.298044864 -1.253119180 -1.164371359
121 122 123 124 125 126
-1.060346336 -0.921933426 -0.788693645 -0.653202211 -0.441082584 -0.384470997
127 128 129 130 131 132
-0.222016458 0.001789073 0.157730837 0.364007513 0.400168769 0.518801171
133 134 135 136 137 138
0.613391944 0.681019611 0.924605650 1.083468890 1.271546888 1.415132926
139 140 141 142 143 144
1.321409602 1.412409076 1.535544783 1.512606701 1.398433046 1.398866770
145 146 147 148 149 150
1.457730011 1.420184549 1.303089419 1.227564772 1.166647505 1.105730237
151 152 153 154 155 156
1.120771340 1.194911782 1.166130749 1.287014804 1.226097536 1.311923881
157 158 159 160 161 162
1.251006614 1.324477233 1.297947852 1.163323867 1.184207921 1.125542307
163 164 165 166 167 168
1.040583410 0.908880900 0.874256915 0.687046365 0.572872710 0.558699056
169 170 171 172 173 174
0.570818683 0.415744367 0.489214986 0.553921178 0.533904572 0.628495344
175 176 177 178 179 180
0.688028407 0.735205744 0.732717993 0.747759096 0.759878723 0.807725883
181 182 183 184 185 186
0.755573043 0.738477912 0.830147209 0.845188312 0.777758270 0.754820188
187 188 189 190 191 192
0.646489485 0.697258120 0.618811998 0.416324247 0.345972729 0.290898413
193 194 195 196 197 198
0.250431476 0.206373240 0.285686811 0.156905777 0.124533445 0.141826201
199 200 201 202 203 204
0.044511578 -0.070331900 -0.208547184 -0.323390662 -0.490150881 -0.642973544
205 206 207 208 209 210
-0.716916361 -0.901875258 -1.136499243 -1.150672898 -1.285966706 -1.317669216
211 212 213 214 215 216
-1.382177782 -1.278822582 -1.343331148 -1.342897424 -1.345385175 -1.600459491
217 218 219 220 221 222
-1.820476098 -1.805434995 -1.813765698 -1.560745409 -1.396039218 -1.495605493
223 224 225 226 227 228
-1.674721439 -1.718109851 -1.505990224 -1.063986016 -0.989845574 -0.710532003
229 230 231 232 233 234
-0.663354666 -0.527863232 -0.448549662 -0.533508558 -0.415545979 -0.341405537
235 236 237 238 239 240
-0.170186571 -0.239868266 -0.283256679 -0.250686721 -0.241488569 -0.179034030
241 242 243 244 245 246
-0.016579492 0.048796523 0.075523529 0.231465294 0.147176220 0.100866331
247 248 249 250 251 252
0.109394660 0.171179376 0.294984906 0.427554864 0.477653677 0.578087401
253 254 255 256 257 258
0.658070795 0.673111898 0.532644961 0.203194104 0.159135869 0.391705827
259 260 261 262 263 264
0.771689220 1.039986711 1.034577483 0.811639401 0.488701319 0.448234382
265 266 267 268 269 270
0.442825155 0.552023306 0.622572450 0.690869940 0.756245955 0.721621969
271 272 273 274 275 276
0.748348976 0.766311555 0.731687569 0.693472285 0.664021428 0.649847774
277 278 279 280 281 282
0.659045925 0.823752117 0.885536833 1.076536307 1.112027740 0.924147368
283 284 285 286 287 288
0.745031423 0.525014816 0.502076734 0.528803740 0.523394513 0.455964471
289 290 291 292
0.541120993 0.470769475 0.456595821 0.732987915
> postscript(file="/var/www/html/rcomp/tmp/66hh11292972401.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 = 292
Frequency = 1
lag(myerror, k = 1) myerror
0 1.151637044 NA
1 1.252070769 1.151637044
2 1.413855484 1.252070769
3 1.279901322 1.413855484
4 1.301455200 1.279901322
5 1.307731876 1.301455200
6 1.376029366 1.307731876
7 1.488148993 1.376029366
8 1.582069943 1.488148993
9 1.691268095 1.582069943
10 1.741366907 1.691268095
11 1.746973760 1.741366907
12 1.794151097 1.746973760
13 1.744249910 1.794151097
14 1.800191674 1.744249910
15 1.768489165 1.800191674
16 1.783530268 1.768489165
17 1.801492847 1.783530268
18 1.796083619 1.801492847
19 1.861459634 1.796083619
20 1.864814834 1.861459634
21 1.782777413 1.864814834
22 1.630624573 1.782777413
23 1.528136821 1.630624573
24 1.516884642 1.528136821
25 1.526082794 1.516884642
26 1.341123897 1.526082794
27 1.129871718 1.341123897
28 0.989404781 1.129871718
29 0.708707006 0.989404781
30 0.591611875 0.708707006
31 0.644632164 0.591611875
32 0.647987364 0.644632164
33 0.552012387 0.647987364
34 0.453115934 0.552012387
35 0.553549658 0.453115934
36 0.600726995 0.553549658
37 0.698239243 0.600726995
38 0.564285081 0.698239243
39 0.231670565 0.564285081
40 0.302219708 0.231670565
41 0.346475569 0.302219708
42 0.159265019 0.346475569
43 0.083740373 0.159265019
44 0.133839186 0.083740373
45 0.163487668 0.133839186
46 0.278528771 0.163487668
47 0.346156438 0.278528771
48 0.358276066 0.346156438
49 0.255788314 0.358276066
50 0.244536135 0.255788314
51 0.233283956 0.244536135
52 0.157089487 0.233283956
53 0.186737969 0.157089487
54 0.093014645 0.186737969
55 -0.024080486 0.093014645
56 0.055233085 -0.024080486
57 0.104662075 0.055233085
58 0.034980380 0.104662075
59 -0.073350324 0.034980380
60 -0.190445454 -0.073350324
61 -0.286420431 -0.190445454
62 -0.403515562 -0.286420431
63 -0.453416749 -0.403515562
64 -0.497474985 -0.453416749
65 -0.676590930 -0.497474985
66 -0.720649166 -0.676590930
67 -0.773471829 -0.720649166
68 -0.946744822 -0.773471829
69 -1.175525856 -0.946744822
70 -1.272170656 -1.175525856
71 -1.330836271 -1.272170656
72 -1.507030740 -1.330836271
73 -1.554010452 -1.507030740
74 -1.577618357 -1.554010452
75 -1.688870536 -1.577618357
76 -1.791358287 -1.688870536
77 -1.879238660 -1.791358287
78 -1.952511654 -1.879238660
79 -1.946234978 -1.952511654
80 -2.010743544 -1.946234978
81 -2.022665546 -2.010743544
82 -1.984922460 -2.022665546
83 -1.982237082 -1.984922460
84 -1.950336947 -1.982237082
85 -1.860007297 -1.950336947
86 -1.885866855 -1.860007297
87 -2.046784122 -1.885866855
88 -2.084329583 -2.046784122
89 -2.089738811 -2.084329583
90 -2.230205748 -2.089738811
91 -2.083698234 -2.230205748
92 -2.083934332 -2.083698234
93 -2.092265036 -2.083934332
94 -2.138574924 -2.092265036
95 -2.141062676 -2.138574924
96 -2.149393379 -2.141062676
97 -2.315483775 -2.149393379
98 -2.306285623 -2.315483775
99 -2.408773375 -2.306285623
100 -2.302496699 -2.408773375
101 -2.252397887 -2.302496699
102 -2.176005792 -2.252397887
103 -2.178493543 -2.176005792
104 -2.118960480 -2.178493543
105 -2.130212659 -2.118960480
106 -2.009328604 -2.130212659
107 -2.000130453 -2.009328604
108 -2.004869857 -2.000130453
109 -1.989828754 -2.004869857
110 -1.848494369 -1.989828754
111 -1.718845887 -1.848494369
112 -1.612569211 -1.718845887
113 -1.571234825 -1.612569211
114 -1.459115198 -1.571234825
115 -1.309016385 -1.459115198
116 -1.369933653 -1.309016385
117 -1.298044864 -1.369933653
118 -1.253119180 -1.298044864
119 -1.164371359 -1.253119180
120 -1.060346336 -1.164371359
121 -0.921933426 -1.060346336
122 -0.788693645 -0.921933426
123 -0.653202211 -0.788693645
124 -0.441082584 -0.653202211
125 -0.384470997 -0.441082584
126 -0.222016458 -0.384470997
127 0.001789073 -0.222016458
128 0.157730837 0.001789073
129 0.364007513 0.157730837
130 0.400168769 0.364007513
131 0.518801171 0.400168769
132 0.613391944 0.518801171
133 0.681019611 0.613391944
134 0.924605650 0.681019611
135 1.083468890 0.924605650
136 1.271546888 1.083468890
137 1.415132926 1.271546888
138 1.321409602 1.415132926
139 1.412409076 1.321409602
140 1.535544783 1.412409076
141 1.512606701 1.535544783
142 1.398433046 1.512606701
143 1.398866770 1.398433046
144 1.457730011 1.398866770
145 1.420184549 1.457730011
146 1.303089419 1.420184549
147 1.227564772 1.303089419
148 1.166647505 1.227564772
149 1.105730237 1.166647505
150 1.120771340 1.105730237
151 1.194911782 1.120771340
152 1.166130749 1.194911782
153 1.287014804 1.166130749
154 1.226097536 1.287014804
155 1.311923881 1.226097536
156 1.251006614 1.311923881
157 1.324477233 1.251006614
158 1.297947852 1.324477233
159 1.163323867 1.297947852
160 1.184207921 1.163323867
161 1.125542307 1.184207921
162 1.040583410 1.125542307
163 0.908880900 1.040583410
164 0.874256915 0.908880900
165 0.687046365 0.874256915
166 0.572872710 0.687046365
167 0.558699056 0.572872710
168 0.570818683 0.558699056
169 0.415744367 0.570818683
170 0.489214986 0.415744367
171 0.553921178 0.489214986
172 0.533904572 0.553921178
173 0.628495344 0.533904572
174 0.688028407 0.628495344
175 0.735205744 0.688028407
176 0.732717993 0.735205744
177 0.747759096 0.732717993
178 0.759878723 0.747759096
179 0.807725883 0.759878723
180 0.755573043 0.807725883
181 0.738477912 0.755573043
182 0.830147209 0.738477912
183 0.845188312 0.830147209
184 0.777758270 0.845188312
185 0.754820188 0.777758270
186 0.646489485 0.754820188
187 0.697258120 0.646489485
188 0.618811998 0.697258120
189 0.416324247 0.618811998
190 0.345972729 0.416324247
191 0.290898413 0.345972729
192 0.250431476 0.290898413
193 0.206373240 0.250431476
194 0.285686811 0.206373240
195 0.156905777 0.285686811
196 0.124533445 0.156905777
197 0.141826201 0.124533445
198 0.044511578 0.141826201
199 -0.070331900 0.044511578
200 -0.208547184 -0.070331900
201 -0.323390662 -0.208547184
202 -0.490150881 -0.323390662
203 -0.642973544 -0.490150881
204 -0.716916361 -0.642973544
205 -0.901875258 -0.716916361
206 -1.136499243 -0.901875258
207 -1.150672898 -1.136499243
208 -1.285966706 -1.150672898
209 -1.317669216 -1.285966706
210 -1.382177782 -1.317669216
211 -1.278822582 -1.382177782
212 -1.343331148 -1.278822582
213 -1.342897424 -1.343331148
214 -1.345385175 -1.342897424
215 -1.600459491 -1.345385175
216 -1.820476098 -1.600459491
217 -1.805434995 -1.820476098
218 -1.813765698 -1.805434995
219 -1.560745409 -1.813765698
220 -1.396039218 -1.560745409
221 -1.495605493 -1.396039218
222 -1.674721439 -1.495605493
223 -1.718109851 -1.674721439
224 -1.505990224 -1.718109851
225 -1.063986016 -1.505990224
226 -0.989845574 -1.063986016
227 -0.710532003 -0.989845574
228 -0.663354666 -0.710532003
229 -0.527863232 -0.663354666
230 -0.448549662 -0.527863232
231 -0.533508558 -0.448549662
232 -0.415545979 -0.533508558
233 -0.341405537 -0.415545979
234 -0.170186571 -0.341405537
235 -0.239868266 -0.170186571
236 -0.283256679 -0.239868266
237 -0.250686721 -0.283256679
238 -0.241488569 -0.250686721
239 -0.179034030 -0.241488569
240 -0.016579492 -0.179034030
241 0.048796523 -0.016579492
242 0.075523529 0.048796523
243 0.231465294 0.075523529
244 0.147176220 0.231465294
245 0.100866331 0.147176220
246 0.109394660 0.100866331
247 0.171179376 0.109394660
248 0.294984906 0.171179376
249 0.427554864 0.294984906
250 0.477653677 0.427554864
251 0.578087401 0.477653677
252 0.658070795 0.578087401
253 0.673111898 0.658070795
254 0.532644961 0.673111898
255 0.203194104 0.532644961
256 0.159135869 0.203194104
257 0.391705827 0.159135869
258 0.771689220 0.391705827
259 1.039986711 0.771689220
260 1.034577483 1.039986711
261 0.811639401 1.034577483
262 0.488701319 0.811639401
263 0.448234382 0.488701319
264 0.442825155 0.448234382
265 0.552023306 0.442825155
266 0.622572450 0.552023306
267 0.690869940 0.622572450
268 0.756245955 0.690869940
269 0.721621969 0.756245955
270 0.748348976 0.721621969
271 0.766311555 0.748348976
272 0.731687569 0.766311555
273 0.693472285 0.731687569
274 0.664021428 0.693472285
275 0.649847774 0.664021428
276 0.659045925 0.649847774
277 0.823752117 0.659045925
278 0.885536833 0.823752117
279 1.076536307 0.885536833
280 1.112027740 1.076536307
281 0.924147368 1.112027740
282 0.745031423 0.924147368
283 0.525014816 0.745031423
284 0.502076734 0.525014816
285 0.528803740 0.502076734
286 0.523394513 0.528803740
287 0.455964471 0.523394513
288 0.541120993 0.455964471
289 0.470769475 0.541120993
290 0.456595821 0.470769475
291 0.732987915 0.456595821
292 NA 0.732987915
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.252070769 1.151637044
[2,] 1.413855484 1.252070769
[3,] 1.279901322 1.413855484
[4,] 1.301455200 1.279901322
[5,] 1.307731876 1.301455200
[6,] 1.376029366 1.307731876
[7,] 1.488148993 1.376029366
[8,] 1.582069943 1.488148993
[9,] 1.691268095 1.582069943
[10,] 1.741366907 1.691268095
[11,] 1.746973760 1.741366907
[12,] 1.794151097 1.746973760
[13,] 1.744249910 1.794151097
[14,] 1.800191674 1.744249910
[15,] 1.768489165 1.800191674
[16,] 1.783530268 1.768489165
[17,] 1.801492847 1.783530268
[18,] 1.796083619 1.801492847
[19,] 1.861459634 1.796083619
[20,] 1.864814834 1.861459634
[21,] 1.782777413 1.864814834
[22,] 1.630624573 1.782777413
[23,] 1.528136821 1.630624573
[24,] 1.516884642 1.528136821
[25,] 1.526082794 1.516884642
[26,] 1.341123897 1.526082794
[27,] 1.129871718 1.341123897
[28,] 0.989404781 1.129871718
[29,] 0.708707006 0.989404781
[30,] 0.591611875 0.708707006
[31,] 0.644632164 0.591611875
[32,] 0.647987364 0.644632164
[33,] 0.552012387 0.647987364
[34,] 0.453115934 0.552012387
[35,] 0.553549658 0.453115934
[36,] 0.600726995 0.553549658
[37,] 0.698239243 0.600726995
[38,] 0.564285081 0.698239243
[39,] 0.231670565 0.564285081
[40,] 0.302219708 0.231670565
[41,] 0.346475569 0.302219708
[42,] 0.159265019 0.346475569
[43,] 0.083740373 0.159265019
[44,] 0.133839186 0.083740373
[45,] 0.163487668 0.133839186
[46,] 0.278528771 0.163487668
[47,] 0.346156438 0.278528771
[48,] 0.358276066 0.346156438
[49,] 0.255788314 0.358276066
[50,] 0.244536135 0.255788314
[51,] 0.233283956 0.244536135
[52,] 0.157089487 0.233283956
[53,] 0.186737969 0.157089487
[54,] 0.093014645 0.186737969
[55,] -0.024080486 0.093014645
[56,] 0.055233085 -0.024080486
[57,] 0.104662075 0.055233085
[58,] 0.034980380 0.104662075
[59,] -0.073350324 0.034980380
[60,] -0.190445454 -0.073350324
[61,] -0.286420431 -0.190445454
[62,] -0.403515562 -0.286420431
[63,] -0.453416749 -0.403515562
[64,] -0.497474985 -0.453416749
[65,] -0.676590930 -0.497474985
[66,] -0.720649166 -0.676590930
[67,] -0.773471829 -0.720649166
[68,] -0.946744822 -0.773471829
[69,] -1.175525856 -0.946744822
[70,] -1.272170656 -1.175525856
[71,] -1.330836271 -1.272170656
[72,] -1.507030740 -1.330836271
[73,] -1.554010452 -1.507030740
[74,] -1.577618357 -1.554010452
[75,] -1.688870536 -1.577618357
[76,] -1.791358287 -1.688870536
[77,] -1.879238660 -1.791358287
[78,] -1.952511654 -1.879238660
[79,] -1.946234978 -1.952511654
[80,] -2.010743544 -1.946234978
[81,] -2.022665546 -2.010743544
[82,] -1.984922460 -2.022665546
[83,] -1.982237082 -1.984922460
[84,] -1.950336947 -1.982237082
[85,] -1.860007297 -1.950336947
[86,] -1.885866855 -1.860007297
[87,] -2.046784122 -1.885866855
[88,] -2.084329583 -2.046784122
[89,] -2.089738811 -2.084329583
[90,] -2.230205748 -2.089738811
[91,] -2.083698234 -2.230205748
[92,] -2.083934332 -2.083698234
[93,] -2.092265036 -2.083934332
[94,] -2.138574924 -2.092265036
[95,] -2.141062676 -2.138574924
[96,] -2.149393379 -2.141062676
[97,] -2.315483775 -2.149393379
[98,] -2.306285623 -2.315483775
[99,] -2.408773375 -2.306285623
[100,] -2.302496699 -2.408773375
[101,] -2.252397887 -2.302496699
[102,] -2.176005792 -2.252397887
[103,] -2.178493543 -2.176005792
[104,] -2.118960480 -2.178493543
[105,] -2.130212659 -2.118960480
[106,] -2.009328604 -2.130212659
[107,] -2.000130453 -2.009328604
[108,] -2.004869857 -2.000130453
[109,] -1.989828754 -2.004869857
[110,] -1.848494369 -1.989828754
[111,] -1.718845887 -1.848494369
[112,] -1.612569211 -1.718845887
[113,] -1.571234825 -1.612569211
[114,] -1.459115198 -1.571234825
[115,] -1.309016385 -1.459115198
[116,] -1.369933653 -1.309016385
[117,] -1.298044864 -1.369933653
[118,] -1.253119180 -1.298044864
[119,] -1.164371359 -1.253119180
[120,] -1.060346336 -1.164371359
[121,] -0.921933426 -1.060346336
[122,] -0.788693645 -0.921933426
[123,] -0.653202211 -0.788693645
[124,] -0.441082584 -0.653202211
[125,] -0.384470997 -0.441082584
[126,] -0.222016458 -0.384470997
[127,] 0.001789073 -0.222016458
[128,] 0.157730837 0.001789073
[129,] 0.364007513 0.157730837
[130,] 0.400168769 0.364007513
[131,] 0.518801171 0.400168769
[132,] 0.613391944 0.518801171
[133,] 0.681019611 0.613391944
[134,] 0.924605650 0.681019611
[135,] 1.083468890 0.924605650
[136,] 1.271546888 1.083468890
[137,] 1.415132926 1.271546888
[138,] 1.321409602 1.415132926
[139,] 1.412409076 1.321409602
[140,] 1.535544783 1.412409076
[141,] 1.512606701 1.535544783
[142,] 1.398433046 1.512606701
[143,] 1.398866770 1.398433046
[144,] 1.457730011 1.398866770
[145,] 1.420184549 1.457730011
[146,] 1.303089419 1.420184549
[147,] 1.227564772 1.303089419
[148,] 1.166647505 1.227564772
[149,] 1.105730237 1.166647505
[150,] 1.120771340 1.105730237
[151,] 1.194911782 1.120771340
[152,] 1.166130749 1.194911782
[153,] 1.287014804 1.166130749
[154,] 1.226097536 1.287014804
[155,] 1.311923881 1.226097536
[156,] 1.251006614 1.311923881
[157,] 1.324477233 1.251006614
[158,] 1.297947852 1.324477233
[159,] 1.163323867 1.297947852
[160,] 1.184207921 1.163323867
[161,] 1.125542307 1.184207921
[162,] 1.040583410 1.125542307
[163,] 0.908880900 1.040583410
[164,] 0.874256915 0.908880900
[165,] 0.687046365 0.874256915
[166,] 0.572872710 0.687046365
[167,] 0.558699056 0.572872710
[168,] 0.570818683 0.558699056
[169,] 0.415744367 0.570818683
[170,] 0.489214986 0.415744367
[171,] 0.553921178 0.489214986
[172,] 0.533904572 0.553921178
[173,] 0.628495344 0.533904572
[174,] 0.688028407 0.628495344
[175,] 0.735205744 0.688028407
[176,] 0.732717993 0.735205744
[177,] 0.747759096 0.732717993
[178,] 0.759878723 0.747759096
[179,] 0.807725883 0.759878723
[180,] 0.755573043 0.807725883
[181,] 0.738477912 0.755573043
[182,] 0.830147209 0.738477912
[183,] 0.845188312 0.830147209
[184,] 0.777758270 0.845188312
[185,] 0.754820188 0.777758270
[186,] 0.646489485 0.754820188
[187,] 0.697258120 0.646489485
[188,] 0.618811998 0.697258120
[189,] 0.416324247 0.618811998
[190,] 0.345972729 0.416324247
[191,] 0.290898413 0.345972729
[192,] 0.250431476 0.290898413
[193,] 0.206373240 0.250431476
[194,] 0.285686811 0.206373240
[195,] 0.156905777 0.285686811
[196,] 0.124533445 0.156905777
[197,] 0.141826201 0.124533445
[198,] 0.044511578 0.141826201
[199,] -0.070331900 0.044511578
[200,] -0.208547184 -0.070331900
[201,] -0.323390662 -0.208547184
[202,] -0.490150881 -0.323390662
[203,] -0.642973544 -0.490150881
[204,] -0.716916361 -0.642973544
[205,] -0.901875258 -0.716916361
[206,] -1.136499243 -0.901875258
[207,] -1.150672898 -1.136499243
[208,] -1.285966706 -1.150672898
[209,] -1.317669216 -1.285966706
[210,] -1.382177782 -1.317669216
[211,] -1.278822582 -1.382177782
[212,] -1.343331148 -1.278822582
[213,] -1.342897424 -1.343331148
[214,] -1.345385175 -1.342897424
[215,] -1.600459491 -1.345385175
[216,] -1.820476098 -1.600459491
[217,] -1.805434995 -1.820476098
[218,] -1.813765698 -1.805434995
[219,] -1.560745409 -1.813765698
[220,] -1.396039218 -1.560745409
[221,] -1.495605493 -1.396039218
[222,] -1.674721439 -1.495605493
[223,] -1.718109851 -1.674721439
[224,] -1.505990224 -1.718109851
[225,] -1.063986016 -1.505990224
[226,] -0.989845574 -1.063986016
[227,] -0.710532003 -0.989845574
[228,] -0.663354666 -0.710532003
[229,] -0.527863232 -0.663354666
[230,] -0.448549662 -0.527863232
[231,] -0.533508558 -0.448549662
[232,] -0.415545979 -0.533508558
[233,] -0.341405537 -0.415545979
[234,] -0.170186571 -0.341405537
[235,] -0.239868266 -0.170186571
[236,] -0.283256679 -0.239868266
[237,] -0.250686721 -0.283256679
[238,] -0.241488569 -0.250686721
[239,] -0.179034030 -0.241488569
[240,] -0.016579492 -0.179034030
[241,] 0.048796523 -0.016579492
[242,] 0.075523529 0.048796523
[243,] 0.231465294 0.075523529
[244,] 0.147176220 0.231465294
[245,] 0.100866331 0.147176220
[246,] 0.109394660 0.100866331
[247,] 0.171179376 0.109394660
[248,] 0.294984906 0.171179376
[249,] 0.427554864 0.294984906
[250,] 0.477653677 0.427554864
[251,] 0.578087401 0.477653677
[252,] 0.658070795 0.578087401
[253,] 0.673111898 0.658070795
[254,] 0.532644961 0.673111898
[255,] 0.203194104 0.532644961
[256,] 0.159135869 0.203194104
[257,] 0.391705827 0.159135869
[258,] 0.771689220 0.391705827
[259,] 1.039986711 0.771689220
[260,] 1.034577483 1.039986711
[261,] 0.811639401 1.034577483
[262,] 0.488701319 0.811639401
[263,] 0.448234382 0.488701319
[264,] 0.442825155 0.448234382
[265,] 0.552023306 0.442825155
[266,] 0.622572450 0.552023306
[267,] 0.690869940 0.622572450
[268,] 0.756245955 0.690869940
[269,] 0.721621969 0.756245955
[270,] 0.748348976 0.721621969
[271,] 0.766311555 0.748348976
[272,] 0.731687569 0.766311555
[273,] 0.693472285 0.731687569
[274,] 0.664021428 0.693472285
[275,] 0.649847774 0.664021428
[276,] 0.659045925 0.649847774
[277,] 0.823752117 0.659045925
[278,] 0.885536833 0.823752117
[279,] 1.076536307 0.885536833
[280,] 1.112027740 1.076536307
[281,] 0.924147368 1.112027740
[282,] 0.745031423 0.924147368
[283,] 0.525014816 0.745031423
[284,] 0.502076734 0.525014816
[285,] 0.528803740 0.502076734
[286,] 0.523394513 0.528803740
[287,] 0.455964471 0.523394513
[288,] 0.541120993 0.455964471
[289,] 0.470769475 0.541120993
[290,] 0.456595821 0.470769475
[291,] 0.732987915 0.456595821
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.252070769 1.151637044
2 1.413855484 1.252070769
3 1.279901322 1.413855484
4 1.301455200 1.279901322
5 1.307731876 1.301455200
6 1.376029366 1.307731876
7 1.488148993 1.376029366
8 1.582069943 1.488148993
9 1.691268095 1.582069943
10 1.741366907 1.691268095
11 1.746973760 1.741366907
12 1.794151097 1.746973760
13 1.744249910 1.794151097
14 1.800191674 1.744249910
15 1.768489165 1.800191674
16 1.783530268 1.768489165
17 1.801492847 1.783530268
18 1.796083619 1.801492847
19 1.861459634 1.796083619
20 1.864814834 1.861459634
21 1.782777413 1.864814834
22 1.630624573 1.782777413
23 1.528136821 1.630624573
24 1.516884642 1.528136821
25 1.526082794 1.516884642
26 1.341123897 1.526082794
27 1.129871718 1.341123897
28 0.989404781 1.129871718
29 0.708707006 0.989404781
30 0.591611875 0.708707006
31 0.644632164 0.591611875
32 0.647987364 0.644632164
33 0.552012387 0.647987364
34 0.453115934 0.552012387
35 0.553549658 0.453115934
36 0.600726995 0.553549658
37 0.698239243 0.600726995
38 0.564285081 0.698239243
39 0.231670565 0.564285081
40 0.302219708 0.231670565
41 0.346475569 0.302219708
42 0.159265019 0.346475569
43 0.083740373 0.159265019
44 0.133839186 0.083740373
45 0.163487668 0.133839186
46 0.278528771 0.163487668
47 0.346156438 0.278528771
48 0.358276066 0.346156438
49 0.255788314 0.358276066
50 0.244536135 0.255788314
51 0.233283956 0.244536135
52 0.157089487 0.233283956
53 0.186737969 0.157089487
54 0.093014645 0.186737969
55 -0.024080486 0.093014645
56 0.055233085 -0.024080486
57 0.104662075 0.055233085
58 0.034980380 0.104662075
59 -0.073350324 0.034980380
60 -0.190445454 -0.073350324
61 -0.286420431 -0.190445454
62 -0.403515562 -0.286420431
63 -0.453416749 -0.403515562
64 -0.497474985 -0.453416749
65 -0.676590930 -0.497474985
66 -0.720649166 -0.676590930
67 -0.773471829 -0.720649166
68 -0.946744822 -0.773471829
69 -1.175525856 -0.946744822
70 -1.272170656 -1.175525856
71 -1.330836271 -1.272170656
72 -1.507030740 -1.330836271
73 -1.554010452 -1.507030740
74 -1.577618357 -1.554010452
75 -1.688870536 -1.577618357
76 -1.791358287 -1.688870536
77 -1.879238660 -1.791358287
78 -1.952511654 -1.879238660
79 -1.946234978 -1.952511654
80 -2.010743544 -1.946234978
81 -2.022665546 -2.010743544
82 -1.984922460 -2.022665546
83 -1.982237082 -1.984922460
84 -1.950336947 -1.982237082
85 -1.860007297 -1.950336947
86 -1.885866855 -1.860007297
87 -2.046784122 -1.885866855
88 -2.084329583 -2.046784122
89 -2.089738811 -2.084329583
90 -2.230205748 -2.089738811
91 -2.083698234 -2.230205748
92 -2.083934332 -2.083698234
93 -2.092265036 -2.083934332
94 -2.138574924 -2.092265036
95 -2.141062676 -2.138574924
96 -2.149393379 -2.141062676
97 -2.315483775 -2.149393379
98 -2.306285623 -2.315483775
99 -2.408773375 -2.306285623
100 -2.302496699 -2.408773375
101 -2.252397887 -2.302496699
102 -2.176005792 -2.252397887
103 -2.178493543 -2.176005792
104 -2.118960480 -2.178493543
105 -2.130212659 -2.118960480
106 -2.009328604 -2.130212659
107 -2.000130453 -2.009328604
108 -2.004869857 -2.000130453
109 -1.989828754 -2.004869857
110 -1.848494369 -1.989828754
111 -1.718845887 -1.848494369
112 -1.612569211 -1.718845887
113 -1.571234825 -1.612569211
114 -1.459115198 -1.571234825
115 -1.309016385 -1.459115198
116 -1.369933653 -1.309016385
117 -1.298044864 -1.369933653
118 -1.253119180 -1.298044864
119 -1.164371359 -1.253119180
120 -1.060346336 -1.164371359
121 -0.921933426 -1.060346336
122 -0.788693645 -0.921933426
123 -0.653202211 -0.788693645
124 -0.441082584 -0.653202211
125 -0.384470997 -0.441082584
126 -0.222016458 -0.384470997
127 0.001789073 -0.222016458
128 0.157730837 0.001789073
129 0.364007513 0.157730837
130 0.400168769 0.364007513
131 0.518801171 0.400168769
132 0.613391944 0.518801171
133 0.681019611 0.613391944
134 0.924605650 0.681019611
135 1.083468890 0.924605650
136 1.271546888 1.083468890
137 1.415132926 1.271546888
138 1.321409602 1.415132926
139 1.412409076 1.321409602
140 1.535544783 1.412409076
141 1.512606701 1.535544783
142 1.398433046 1.512606701
143 1.398866770 1.398433046
144 1.457730011 1.398866770
145 1.420184549 1.457730011
146 1.303089419 1.420184549
147 1.227564772 1.303089419
148 1.166647505 1.227564772
149 1.105730237 1.166647505
150 1.120771340 1.105730237
151 1.194911782 1.120771340
152 1.166130749 1.194911782
153 1.287014804 1.166130749
154 1.226097536 1.287014804
155 1.311923881 1.226097536
156 1.251006614 1.311923881
157 1.324477233 1.251006614
158 1.297947852 1.324477233
159 1.163323867 1.297947852
160 1.184207921 1.163323867
161 1.125542307 1.184207921
162 1.040583410 1.125542307
163 0.908880900 1.040583410
164 0.874256915 0.908880900
165 0.687046365 0.874256915
166 0.572872710 0.687046365
167 0.558699056 0.572872710
168 0.570818683 0.558699056
169 0.415744367 0.570818683
170 0.489214986 0.415744367
171 0.553921178 0.489214986
172 0.533904572 0.553921178
173 0.628495344 0.533904572
174 0.688028407 0.628495344
175 0.735205744 0.688028407
176 0.732717993 0.735205744
177 0.747759096 0.732717993
178 0.759878723 0.747759096
179 0.807725883 0.759878723
180 0.755573043 0.807725883
181 0.738477912 0.755573043
182 0.830147209 0.738477912
183 0.845188312 0.830147209
184 0.777758270 0.845188312
185 0.754820188 0.777758270
186 0.646489485 0.754820188
187 0.697258120 0.646489485
188 0.618811998 0.697258120
189 0.416324247 0.618811998
190 0.345972729 0.416324247
191 0.290898413 0.345972729
192 0.250431476 0.290898413
193 0.206373240 0.250431476
194 0.285686811 0.206373240
195 0.156905777 0.285686811
196 0.124533445 0.156905777
197 0.141826201 0.124533445
198 0.044511578 0.141826201
199 -0.070331900 0.044511578
200 -0.208547184 -0.070331900
201 -0.323390662 -0.208547184
202 -0.490150881 -0.323390662
203 -0.642973544 -0.490150881
204 -0.716916361 -0.642973544
205 -0.901875258 -0.716916361
206 -1.136499243 -0.901875258
207 -1.150672898 -1.136499243
208 -1.285966706 -1.150672898
209 -1.317669216 -1.285966706
210 -1.382177782 -1.317669216
211 -1.278822582 -1.382177782
212 -1.343331148 -1.278822582
213 -1.342897424 -1.343331148
214 -1.345385175 -1.342897424
215 -1.600459491 -1.345385175
216 -1.820476098 -1.600459491
217 -1.805434995 -1.820476098
218 -1.813765698 -1.805434995
219 -1.560745409 -1.813765698
220 -1.396039218 -1.560745409
221 -1.495605493 -1.396039218
222 -1.674721439 -1.495605493
223 -1.718109851 -1.674721439
224 -1.505990224 -1.718109851
225 -1.063986016 -1.505990224
226 -0.989845574 -1.063986016
227 -0.710532003 -0.989845574
228 -0.663354666 -0.710532003
229 -0.527863232 -0.663354666
230 -0.448549662 -0.527863232
231 -0.533508558 -0.448549662
232 -0.415545979 -0.533508558
233 -0.341405537 -0.415545979
234 -0.170186571 -0.341405537
235 -0.239868266 -0.170186571
236 -0.283256679 -0.239868266
237 -0.250686721 -0.283256679
238 -0.241488569 -0.250686721
239 -0.179034030 -0.241488569
240 -0.016579492 -0.179034030
241 0.048796523 -0.016579492
242 0.075523529 0.048796523
243 0.231465294 0.075523529
244 0.147176220 0.231465294
245 0.100866331 0.147176220
246 0.109394660 0.100866331
247 0.171179376 0.109394660
248 0.294984906 0.171179376
249 0.427554864 0.294984906
250 0.477653677 0.427554864
251 0.578087401 0.477653677
252 0.658070795 0.578087401
253 0.673111898 0.658070795
254 0.532644961 0.673111898
255 0.203194104 0.532644961
256 0.159135869 0.203194104
257 0.391705827 0.159135869
258 0.771689220 0.391705827
259 1.039986711 0.771689220
260 1.034577483 1.039986711
261 0.811639401 1.034577483
262 0.488701319 0.811639401
263 0.448234382 0.488701319
264 0.442825155 0.448234382
265 0.552023306 0.442825155
266 0.622572450 0.552023306
267 0.690869940 0.622572450
268 0.756245955 0.690869940
269 0.721621969 0.756245955
270 0.748348976 0.721621969
271 0.766311555 0.748348976
272 0.731687569 0.766311555
273 0.693472285 0.731687569
274 0.664021428 0.693472285
275 0.649847774 0.664021428
276 0.659045925 0.649847774
277 0.823752117 0.659045925
278 0.885536833 0.823752117
279 1.076536307 0.885536833
280 1.112027740 1.076536307
281 0.924147368 1.112027740
282 0.745031423 0.924147368
283 0.525014816 0.745031423
284 0.502076734 0.525014816
285 0.528803740 0.502076734
286 0.523394513 0.528803740
287 0.455964471 0.523394513
288 0.541120993 0.455964471
289 0.470769475 0.541120993
290 0.456595821 0.470769475
291 0.732987915 0.456595821
> 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/www/html/rcomp/tmp/7zrg41292972401.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8zrg41292972401.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/990fo1292972401.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1090fo1292972401.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/html/rcomp/tmp/11utsr1292972401.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/www/html/rcomp/tmp/12f45u1292972401.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/www/html/rcomp/tmp/13ayve1292972401.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/www/html/rcomp/tmp/14l0rd1292972401.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/www/html/rcomp/tmp/15vjmv1292972401.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/www/html/rcomp/tmp/16h75t1292972401.tab")
+ }
>
> try(system("convert tmp/13h0d1292972401.ps tmp/13h0d1292972401.png",intern=TRUE))
character(0)
> try(system("convert tmp/23h0d1292972401.ps tmp/23h0d1292972401.png",intern=TRUE))
character(0)
> try(system("convert tmp/3v80g1292972401.ps tmp/3v80g1292972401.png",intern=TRUE))
character(0)
> try(system("convert tmp/4v80g1292972401.ps tmp/4v80g1292972401.png",intern=TRUE))
character(0)
> try(system("convert tmp/5v80g1292972401.ps tmp/5v80g1292972401.png",intern=TRUE))
character(0)
> try(system("convert tmp/66hh11292972401.ps tmp/66hh11292972401.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zrg41292972401.ps tmp/7zrg41292972401.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zrg41292972401.ps tmp/8zrg41292972401.png",intern=TRUE))
character(0)
> try(system("convert tmp/990fo1292972401.ps tmp/990fo1292972401.png",intern=TRUE))
character(0)
> try(system("convert tmp/1090fo1292972401.ps tmp/1090fo1292972401.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.134 1.940 16.489