R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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+ ,1
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+ ,10
+ ,-2
+ ,3
+ ,23
+ ,3
+ ,7
+ ,11
+ ,0
+ ,8
+ ,17
+ ,2
+ ,6
+ ,12
+ ,-2
+ ,3
+ ,16
+ ,0
+ ,6
+ ,1
+ ,-3
+ ,-3
+ ,15
+ ,0
+ ,6
+ ,2
+ ,1
+ ,4
+ ,8
+ ,3
+ ,6
+ ,3
+ ,-2
+ ,-5
+ ,5
+ ,-2
+ ,2
+ ,4
+ ,-1
+ ,-1
+ ,6
+ ,0
+ ,2
+ ,5
+ ,1
+ ,5
+ ,5
+ ,1
+ ,2
+ ,6
+ ,-3
+ ,0
+ ,12
+ ,-1
+ ,3
+ ,7
+ ,-4
+ ,-6
+ ,8
+ ,-2
+ ,-1
+ ,8
+ ,-9
+ ,-13
+ ,17
+ ,-1
+ ,-4
+ ,9
+ ,-9
+ ,-15
+ ,22
+ ,-1
+ ,4
+ ,10
+ ,-7
+ ,-8
+ ,24
+ ,1
+ ,5
+ ,11
+ ,-14
+ ,-20
+ ,36
+ ,-2
+ ,3)
+ ,dim=c(6
+ ,323)
+ ,dimnames=list(c('maand'
+ ,'consumentenvertrouwen'
+ ,'economischesituatie'
+ ,'werkloosheid'
+ ,'financielesituatie'
+ ,'spaarvermogen')
+ ,1:323))
> y <- array(NA,dim=c(6,323),dimnames=list(c('maand','consumentenvertrouwen','economischesituatie','werkloosheid','financielesituatie','spaarvermogen'),1:323))
> 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
> 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
consumentenvertrouwen maand economischesituatie werkloosheid
1 -28 1 -25 37
2 -26 2 -23 33
3 -27 3 -24 36
4 -26 4 -24 37
5 -27 5 -25 39
6 -27 6 -25 39
7 -27 7 -24 37
8 -28 8 -24 37
9 -26 9 -22 36
10 -13 10 1 23
11 -13 11 -5 21
12 -14 12 -10 24
13 -12 1 -10 25
14 -16 2 -15 29
15 -16 3 -13 24
16 -12 4 -11 22
17 -15 5 -15 28
18 -18 6 -15 39
19 -17 7 -16 36
20 -10 8 -4 32
21 -9 9 -5 27
22 -13 10 -9 33
23 -15 11 -14 36
24 -12 12 -11 34
25 -13 1 -7 34
26 -10 2 -7 31
27 -13 3 -9 37
28 -11 4 -5 36
29 -12 5 -10 35
30 -10 6 -9 32
31 -13 7 -10 35
32 -12 8 -8 36
33 -11 9 -9 35
34 -11 10 -10 32
35 -11 11 -10 28
36 -8 12 -5 24
37 -7 1 -6 25
38 -10 2 -10 29
39 -8 3 -10 28
40 -8 4 -9 25
41 -7 5 -10 22
42 -7 6 -8 22
43 -6 7 -8 22
44 -8 8 -8 23
45 -6 9 -4 22
46 -3 10 2 14
47 1 11 3 7
48 0 12 2 9
49 -3 1 -3 12
50 0 2 -1 9
51 0 3 1 6
52 -1 4 2 8
53 -1 5 -4 10
54 0 6 0 8
55 1 7 5 9
56 0 8 -1 11
57 2 9 3 6
58 3 10 6 6
59 2 11 7 9
60 4 12 7 7
61 3 1 3 8
62 4 2 8 2
63 3 3 3 2
64 1 4 0 7
65 2 5 1 6
66 4 6 4 4
67 3 7 4 8
68 2 8 1 9
69 -4 9 -17 11
70 -5 10 -16 14
71 -5 11 -13 18
72 -7 12 -15 23
73 -13 1 -31 25
74 -11 2 -26 31
75 -3 3 -5 18
76 -3 4 -5 19
77 -5 5 -6 23
78 -4 6 -5 24
79 -4 7 -5 25
80 -4 8 -7 26
81 -5 9 -6 27
82 -4 10 -8 23
83 -5 11 -6 27
84 -6 12 -12 34
85 -9 1 -15 34
86 -10 2 -15 37
87 -11 3 -16 41
88 -13 4 -19 43
89 -13 5 -23 38
90 -13 6 -23 39
91 -11 7 -21 35
92 -12 8 -21 38
93 -14 9 -25 40
94 -20 10 -34 49
95 -17 11 -30 51
96 -16 12 -27 48
97 -24 1 -40 54
98 -24 2 -40 56
99 -22 3 -34 56
100 -25 4 -43 61
101 -24 5 -39 57
102 -25 6 -40 57
103 -24 7 -40 52
104 -25 8 -40 58
105 -24 9 -35 60
106 -26 10 -43 62
107 -25 11 -44 48
108 -24 12 -38 50
109 -22 1 -37 50
110 -20 2 -31 48
111 -14 3 -20 40
112 -13 4 -22 35
113 -10 5 -9 33
114 -10 6 -11 34
115 -11 7 -8 34
116 -6 8 -3 28
117 -2 9 3 26
118 -3 10 6 23
119 -2 11 -3 20
120 -4 12 -8 20
121 -7 1 -8 26
122 -8 2 -10 28
123 -7 3 -9 29
124 -4 4 -7 25
125 -7 5 -12 27
126 -5 6 -9 24
127 -6 7 -8 26
128 -12 8 -19 38
129 -12 9 -21 38
130 -16 10 -24 45
131 -20 11 -30 53
132 -16 12 -28 44
133 -16 1 -27 43
134 -18 2 -26 47
135 -15 3 -27 40
136 -12 4 -23 34
137 -13 5 -26 38
138 -13 6 -23 39
139 -12 7 -21 35
140 -11 8 -20 35
141 -9 9 -14 36
142 -9 10 -16 25
143 -8 11 -17 24
144 -8 12 -18 29
145 -15 1 -25 44
146 -16 2 -26 43
147 -21 3 -36 57
148 -21 4 -35 56
149 -16 5 -27 47
150 -13 6 -22 41
151 -12 7 -25 38
152 -8 8 -17 33
153 -9 9 -14 36
154 -1 10 -7 22
155 -5 11 -12 27
156 -9 12 -17 32
157 -1 1 -8 21
158 3 2 -2 14
159 2 3 -1 10
160 3 4 1 14
161 5 5 0 12
162 5 6 -2 10
163 3 7 -5 12
164 2 8 -4 9
165 1 9 -9 14
166 -4 10 -16 23
167 1 11 -7 17
168 1 12 -7 16
169 6 1 3 7
170 3 2 -2 9
171 2 3 -3 9
172 2 4 -6 14
173 2 5 -7 12
174 -8 6 -24 23
175 0 7 -13 12
176 -2 8 -14 15
177 3 9 -7 6
178 5 10 -1 6
179 8 11 5 1
180 8 12 6 3
181 9 1 5 -1
182 11 2 5 -4
183 13 3 9 -6
184 12 4 10 -9
185 13 5 14 -13
186 15 6 19 -13
187 13 7 18 -10
188 16 8 16 -12
189 10 9 8 -9
190 14 10 10 -15
191 14 11 12 -14
192 15 12 13 -18
193 13 1 15 -13
194 8 2 3 -2
195 7 3 2 -1
196 3 4 -2 5
197 3 5 1 8
198 4 6 1 6
199 4 7 -1 7
200 0 8 -6 15
201 -4 9 -13 23
202 -14 10 -25 43
203 -18 11 -26 60
204 -8 12 -9 36
205 -1 1 1 28
206 1 2 3 23
207 2 3 6 23
208 0 4 2 22
209 1 5 5 22
210 0 6 5 24
211 -1 7 0 32
212 -3 8 -5 27
213 -3 9 -4 27
214 -3 10 -2 27
215 -4 11 -1 29
216 -8 12 -8 38
217 -9 1 -16 40
218 -13 2 -19 45
219 -18 3 -28 50
220 -11 4 -11 43
221 -9 5 -4 44
222 -10 6 -9 44
223 -13 7 -12 49
224 -11 8 -10 42
225 -5 9 -2 36
226 -15 10 -13 57
227 -6 11 0 42
228 -6 12 0 39
229 -3 1 4 33
230 -1 2 7 32
231 -3 3 5 34
232 -4 4 2 37
233 -6 5 -2 38
234 0 6 6 28
235 -4 7 -3 31
236 -2 8 1 28
237 -2 9 0 30
238 -6 10 -7 39
239 -7 11 -6 38
240 -6 12 -4 39
241 -6 1 -4 38
242 -3 2 -2 37
243 -2 3 2 32
244 -5 4 -5 32
245 -11 5 -15 44
246 -11 6 -16 43
247 -11 7 -18 42
248 -10 8 -13 38
249 -14 9 -23 37
250 -8 10 -10 35
251 -9 11 -10 37
252 -5 12 -6 33
253 -1 1 -3 24
254 -2 2 -4 24
255 -5 3 -7 31
256 -4 4 -7 25
257 -6 5 -7 28
258 -2 6 -3 24
259 -2 7 0 25
260 -2 8 -5 16
261 -2 9 -3 17
262 2 10 3 11
263 1 11 2 12
264 -8 12 -7 39
265 -1 1 -1 19
266 1 2 0 14
267 -1 3 -3 15
268 2 4 4 7
269 2 5 2 12
270 1 6 3 12
271 -1 7 0 14
272 -2 8 -10 9
273 -2 9 -10 8
274 -1 10 -9 4
275 -8 11 -22 7
276 -4 12 -16 3
277 -6 1 -18 5
278 -3 2 -14 0
279 -3 3 -12 -2
280 -7 4 -17 6
281 -9 5 -23 11
282 -11 6 -28 9
283 -13 7 -31 17
284 -11 8 -21 21
285 -9 9 -19 21
286 -17 10 -22 41
287 -22 11 -22 57
288 -25 12 -25 65
289 -20 1 -16 68
290 -24 2 -22 73
291 -24 3 -21 71
292 -22 4 -10 71
293 -19 5 -7 70
294 -18 6 -5 69
295 -17 7 -4 65
296 -11 8 7 57
297 -11 9 6 57
298 -12 10 3 57
299 -10 11 10 55
300 -15 12 0 65
301 -15 1 -2 65
302 -15 2 -1 64
303 -13 3 2 60
304 -8 4 8 43
305 -13 5 -6 47
306 -9 6 -4 40
307 -7 7 4 31
308 -4 8 7 27
309 -4 9 3 24
310 -2 10 3 23
311 0 11 8 17
312 -2 12 3 16
313 -3 1 -3 15
314 1 2 4 8
315 -2 3 -5 5
316 -1 4 -1 6
317 1 5 5 5
318 -3 6 0 12
319 -4 7 -6 8
320 -9 8 -13 17
321 -9 9 -15 22
322 -7 10 -8 24
323 -14 11 -20 36
financielesituatie spaarvermogen t
1 -16 -33 1
2 -15 -32 2
3 -16 -32 3
4 -14 -31 4
5 -14 -31 5
6 -14 -32 6
7 -16 -32 7
8 -17 -33 8
9 -15 -31 9
10 -9 -21 10
11 -9 -17 11
12 -7 -14 12
13 -4 -10 13
14 -9 -13 14
15 -8 -19 15
16 -6 -10 16
17 -5 -13 17
18 -7 -11 18
19 -6 -9 19
20 -1 -1 20
21 -2 -3 21
22 -1 -7 22
23 -3 -6 23
24 -2 -1 24
25 -2 -11 25
26 -1 -3 26
27 -2 -1 27
28 -1 -2 28
29 0 -2 29
30 1 -2 30
31 -1 -4 31
32 -1 -1 32
33 0 0 33
34 0 -3 34
35 1 -4 35
36 1 -4 36
37 2 -2 37
38 1 -3 38
39 2 4 39
40 1 3 40
41 0 3 41
42 2 -1 42
43 1 5 43
44 0 -2 44
45 1 2 45
46 3 -1 46
47 2 6 47
48 4 4 48
49 1 -2 49
50 4 4 50
51 2 3 51
52 3 0 52
53 2 7 53
54 3 5 54
55 5 3 55
56 5 9 56
57 3 7 57
58 4 8 58
59 5 8 59
60 5 10 60
61 4 11 61
62 6 5 62
63 5 9 63
64 4 7 64
65 4 8 65
66 7 12 66
67 8 10 67
68 5 10 68
69 4 8 69
70 1 11 70
71 2 10 71
72 0 8 72
73 -2 5 73
74 -1 12 74
75 2 10 75
76 3 8 76
77 2 8 77
78 2 10 78
79 5 12 79
80 4 13 80
81 5 7 81
82 2 13 82
83 6 11 83
84 7 13 84
85 1 11 85
86 1 10 86
87 0 15 87
88 -2 11 88
89 -1 10 89
90 -1 12 90
91 1 14 91
92 0 11 92
93 0 8 93
94 -1 3 94
95 -1 15 95
96 -1 11 96
97 -4 0 97
98 -6 4 98
99 -3 7 99
100 -7 12 100
101 -4 5 101
102 -5 2 102
103 -3 0 103
104 -5 5 104
105 -6 4 105
106 -7 7 106
107 -6 0 107
108 -8 -1 108
109 -5 3 109
110 -5 2 110
111 -3 7 111
112 -2 6 112
113 -1 3 113
114 1 3 114
115 -1 1 115
116 -1 8 116
117 3 10 117
118 2 6 118
119 4 11 119
120 3 6 120
121 1 6 121
122 0 3 122
123 2 10 123
124 2 12 124
125 2 9 125
126 3 12 126
127 2 10 127
128 1 6 128
129 0 8 129
130 -4 11 130
131 -9 11 131
132 -6 11 132
133 -7 14 133
134 -6 8 134
135 -6 12 135
136 -3 11 136
137 -3 14 137
138 -4 15 138
139 -5 15 139
140 -4 14 140
141 -3 16 141
142 -5 9 142
143 -3 13 143
144 -2 15 144
145 -3 14 145
146 -5 11 146
147 -3 14 147
148 -3 10 148
149 -4 13 149
150 -2 15 150
151 -3 20 151
152 -2 19 152
153 -3 16 153
154 2 22 154
155 1 19 155
156 -1 16 156
157 2 23 157
158 5 23 158
159 3 16 159
160 3 23 160
161 3 30 161
162 1 31 162
163 3 24 163
164 1 20 164
165 2 24 165
166 2 23 166
167 1 25 167
168 2 25 168
169 4 23 169
170 3 21 170
171 3 16 171
172 3 26 172
173 2 23 173
174 -1 15 174
175 1 23 175
176 3 20 176
177 4 22 177
178 4 24 178
179 6 22 179
180 4 24 180
181 6 24 181
182 6 29 182
183 8 29 183
184 4 25 184
185 8 16 185
186 10 18 186
187 9 13 187
188 12 22 188
189 9 15 189
190 11 20 190
191 11 19 191
192 11 18 192
193 11 13 193
194 11 17 194
195 9 17 195
196 8 13 196
197 6 14 197
198 7 13 198
199 8 17 199
200 6 17 200
201 5 15 201
202 2 9 202
203 3 10 203
204 3 9 204
205 7 14 205
206 8 18 206
207 7 18 207
208 7 12 208
209 6 16 209
210 6 12 210
211 7 19 211
212 5 13 212
213 5 12 213
214 5 13 214
215 4 11 215
216 4 10 216
217 4 16 217
218 1 12 218
219 -1 6 219
220 3 8 220
221 4 6 221
222 3 8 222
223 2 8 223
224 1 9 224
225 4 13 225
226 3 8 226
227 5 11 227
228 6 8 228
229 6 10 229
230 6 15 230
231 6 12 231
232 6 13 232
233 5 12 233
234 6 15 234
235 5 13 235
236 6 13 236
237 5 16 237
238 7 14 238
239 4 12 239
240 5 15 240
241 6 14 241
242 6 19 242
243 5 16 243
244 3 16 244
245 2 11 245
246 3 13 246
247 3 12 247
248 2 11 248
249 0 6 249
250 4 9 250
251 4 6 251
252 5 15 252
253 6 17 253
254 6 13 254
255 5 12 255
256 5 13 256
257 3 10 257
258 5 14 258
259 5 13 259
260 5 10 260
261 3 11 261
262 6 12 262
263 6 7 263
264 4 11 264
265 6 9 265
266 5 13 266
267 4 12 267
268 5 5 268
269 5 13 269
270 4 11 270
271 3 8 271
272 2 8 272
273 3 8 273
274 2 8 274
275 -1 0 275
276 0 3 276
277 -2 0 277
278 1 -1 278
279 -2 -1 279
280 -2 -4 280
281 -2 1 281
282 -6 -1 282
283 -4 0 283
284 -2 -1 284
285 0 6 285
286 -5 0 286
287 -4 -3 287
288 -5 -3 288
289 -1 4 289
290 -2 1 290
291 -4 0 291
292 -1 -4 292
293 1 -2 293
294 1 3 294
295 -2 2 295
296 1 5 296
297 1 6 297
298 3 6 298
299 3 3 299
300 1 4 300
301 1 7 301
302 0 5 302
303 2 6 303
304 2 1 304
305 -1 3 305
306 1 6 306
307 0 0 307
308 1 3 308
309 1 4 309
310 3 7 310
311 2 6 311
312 0 6 312
313 0 6 313
314 3 6 314
315 -2 2 315
316 0 2 316
317 1 2 317
318 -1 3 318
319 -2 -1 319
320 -1 -4 320
321 -1 4 321
322 1 5 322
323 -2 3 323
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) maand economischesituatie
0.1174474 -0.0115480 0.2494050
werkloosheid financielesituatie spaarvermogen
-0.2512233 0.2481153 0.2488992
t
-0.0001324
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.92655 -0.25424 0.01552 0.26474 0.91317
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1174474 0.0628466 1.869 0.0626 .
maand -0.0115480 0.0057411 -2.011 0.0451 *
economischesituatie 0.2494050 0.0027968 89.176 <2e-16 ***
werkloosheid -0.2512233 0.0014271 -176.035 <2e-16 ***
financielesituatie 0.2481153 0.0085798 28.918 <2e-16 ***
spaarvermogen 0.2488992 0.0029089 85.565 <2e-16 ***
t -0.0001324 0.0002384 -0.556 0.5789
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.355 on 316 degrees of freedom
Multiple R-squared: 0.9985, Adjusted R-squared: 0.9984
F-statistic: 3.432e+04 on 6 and 316 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,] 0.32358771 0.647175418 0.676412291
[2,] 0.20615708 0.412314157 0.793842921
[3,] 0.33172386 0.663447719 0.668276141
[4,] 0.21691723 0.433834452 0.783082774
[5,] 0.21506502 0.430130033 0.784934983
[6,] 0.14003109 0.280062173 0.859968914
[7,] 0.10236125 0.204722509 0.897638745
[8,] 0.07815184 0.156303681 0.921848160
[9,] 0.21188125 0.423762509 0.788118746
[10,] 0.24579963 0.491599251 0.754200374
[11,] 0.28518098 0.570361958 0.714819021
[12,] 0.27075585 0.541511694 0.729244153
[13,] 0.31809909 0.636198177 0.681900912
[14,] 0.25582068 0.511641359 0.744179321
[15,] 0.21612039 0.432240772 0.783879614
[16,] 0.19944298 0.398885954 0.800557023
[17,] 0.17122636 0.342452712 0.828773644
[18,] 0.43182418 0.863648354 0.568175823
[19,] 0.36931814 0.738636275 0.630681862
[20,] 0.35273380 0.705467596 0.647266202
[21,] 0.35593695 0.711873906 0.644063047
[22,] 0.43762762 0.875255250 0.562372375
[23,] 0.42458616 0.849172325 0.575413837
[24,] 0.38076340 0.761526797 0.619236601
[25,] 0.34661506 0.693230121 0.653384939
[26,] 0.57603734 0.847925318 0.423962659
[27,] 0.52486740 0.950265196 0.475132598
[28,] 0.55186209 0.896275812 0.448137906
[29,] 0.50045520 0.999089601 0.499544800
[30,] 0.45647541 0.912950828 0.543524586
[31,] 0.52844148 0.943117031 0.471558515
[32,] 0.50813594 0.983728113 0.491864057
[33,] 0.46067390 0.921347807 0.539326097
[34,] 0.41127029 0.822540574 0.588729713
[35,] 0.37771599 0.755431976 0.622284012
[36,] 0.34886836 0.697736726 0.651131637
[37,] 0.42775397 0.855507931 0.572246035
[38,] 0.38443758 0.768875156 0.615562422
[39,] 0.35375453 0.707509052 0.646245474
[40,] 0.45731026 0.914620521 0.542689739
[41,] 0.42116983 0.842339657 0.578830171
[42,] 0.42075276 0.841505527 0.579247237
[43,] 0.47516177 0.950323548 0.524838226
[44,] 0.43775615 0.875512291 0.562243855
[45,] 0.39754623 0.795092455 0.602453772
[46,] 0.35653577 0.713071533 0.643464234
[47,] 0.39489835 0.789796697 0.605101651
[48,] 0.38953289 0.779065775 0.610467112
[49,] 0.35178449 0.703568982 0.648215509
[50,] 0.42514001 0.850280020 0.574859990
[51,] 0.46009777 0.920195543 0.539902228
[52,] 0.44704935 0.894098709 0.552950646
[53,] 0.49290138 0.985802761 0.507098619
[54,] 0.71348307 0.573033862 0.286516931
[55,] 0.67851915 0.642961700 0.321480850
[56,] 0.65493391 0.690132176 0.345066088
[57,] 0.77178528 0.456429443 0.228214721
[58,] 0.78650486 0.426990284 0.213495142
[59,] 0.78894244 0.422115118 0.211057559
[60,] 0.75957226 0.480855489 0.240427745
[61,] 0.76279484 0.474410320 0.237205160
[62,] 0.73848883 0.523022348 0.261511174
[63,] 0.82095320 0.358093601 0.179046800
[64,] 0.79580963 0.408380743 0.204190372
[65,] 0.78938016 0.421239680 0.210619840
[66,] 0.78923391 0.421532189 0.210766094
[67,] 0.76870081 0.462598379 0.231299190
[68,] 0.75732420 0.485351598 0.242675799
[69,] 0.74331324 0.513373517 0.256686759
[70,] 0.81809068 0.363818648 0.181909324
[71,] 0.79694622 0.406107552 0.203053776
[72,] 0.79993105 0.400137908 0.200068954
[73,] 0.77597622 0.448047557 0.224023778
[74,] 0.88400609 0.231987816 0.115993908
[75,] 0.92546563 0.149068746 0.074534373
[76,] 0.91286107 0.174277858 0.087138929
[77,] 0.89919492 0.201610154 0.100805077
[78,] 0.91940138 0.161197246 0.080598623
[79,] 0.91054774 0.178904513 0.089452257
[80,] 0.89573133 0.208537334 0.104268667
[81,] 0.88868368 0.222632647 0.111316324
[82,] 0.93243179 0.135136424 0.067568212
[83,] 0.92126912 0.157461763 0.078730882
[84,] 0.91687865 0.166242695 0.083121348
[85,] 0.90861291 0.182774170 0.091387085
[86,] 0.89595392 0.208092167 0.104046084
[87,] 0.89620273 0.207594541 0.103797270
[88,] 0.88969566 0.220608681 0.110304341
[89,] 0.88770495 0.224590108 0.112295054
[90,] 0.91831320 0.163373608 0.081686804
[91,] 0.91286241 0.174275176 0.087137588
[92,] 0.91040243 0.179195147 0.089597574
[93,] 0.89615610 0.207687793 0.103843897
[94,] 0.89455632 0.210887358 0.105443679
[95,] 0.90582607 0.188347862 0.094173931
[96,] 0.90472247 0.190555063 0.095277531
[97,] 0.90256174 0.194876521 0.097438260
[98,] 0.91679678 0.166406432 0.083203216
[99,] 0.91320583 0.173588334 0.086794167
[100,] 0.90042797 0.199144069 0.099572035
[101,] 0.90118259 0.197634814 0.098817407
[102,] 0.88667737 0.226645265 0.113322632
[103,] 0.87283167 0.254336655 0.127168328
[104,] 0.85589294 0.288214129 0.144107064
[105,] 0.84069622 0.318607553 0.159303776
[106,] 0.87061881 0.258762386 0.129381193
[107,] 0.85551124 0.288977512 0.144488756
[108,] 0.87667032 0.246659368 0.123329684
[109,] 0.93360149 0.132797018 0.066398509
[110,] 0.92550672 0.148986553 0.074493277
[111,] 0.95523309 0.089533825 0.044766913
[112,] 0.95998883 0.080022350 0.040011175
[113,] 0.96820701 0.063585974 0.031792987
[114,] 0.97907159 0.041856824 0.020928412
[115,] 0.98001029 0.039979420 0.019989710
[116,] 0.97634033 0.047319335 0.023659668
[117,] 0.98365827 0.032683468 0.016341734
[118,] 0.98931523 0.021369542 0.010684771
[119,] 0.98996298 0.020074050 0.010037025
[120,] 0.99458389 0.010832222 0.005416111
[121,] 0.99563627 0.008727468 0.004363734
[122,] 0.99554382 0.008912364 0.004456182
[123,] 0.99827525 0.003449490 0.001724745
[124,] 0.99822280 0.003554397 0.001777198
[125,] 0.99825600 0.003488007 0.001744003
[126,] 0.99788232 0.004235369 0.002117685
[127,] 0.99739657 0.005206851 0.002603426
[128,] 0.99695682 0.006086351 0.003043175
[129,] 0.99660164 0.006796725 0.003398362
[130,] 0.99740105 0.005197902 0.002598951
[131,] 0.99703808 0.005923842 0.002961921
[132,] 0.99675350 0.006493007 0.003246504
[133,] 0.99609538 0.007809236 0.003904618
[134,] 0.99576344 0.008473123 0.004236562
[135,] 0.99678248 0.006435044 0.003217522
[136,] 0.99784537 0.004309251 0.002154625
[137,] 0.99783581 0.004328379 0.002164190
[138,] 0.99840352 0.003192967 0.001596484
[139,] 0.99795206 0.004095886 0.002047943
[140,] 0.99758449 0.004831030 0.002415515
[141,] 0.99828383 0.003432331 0.001716166
[142,] 0.99863892 0.002722168 0.001361084
[143,] 0.99846257 0.003074868 0.001537434
[144,] 0.99824277 0.003514467 0.001757233
[145,] 0.99807071 0.003858588 0.001929294
[146,] 0.99778868 0.004422634 0.002211317
[147,] 0.99836409 0.003271823 0.001635912
[148,] 0.99790516 0.004189678 0.002094839
[149,] 0.99733688 0.005326237 0.002663118
[150,] 0.99682325 0.006353509 0.003176754
[151,] 0.99673503 0.006529940 0.003264970
[152,] 0.99625587 0.007488262 0.003744131
[153,] 0.99530418 0.009391635 0.004695817
[154,] 0.99619929 0.007601428 0.003800714
[155,] 0.99527183 0.009456349 0.004728174
[156,] 0.99473421 0.010531575 0.005265787
[157,] 0.99563655 0.008726902 0.004363451
[158,] 0.99695240 0.006095196 0.003047598
[159,] 0.99614967 0.007700657 0.003850329
[160,] 0.99531673 0.009366537 0.004683269
[161,] 0.99543774 0.009124522 0.004562261
[162,] 0.99429926 0.011401474 0.005700737
[163,] 0.99368849 0.012623015 0.006311507
[164,] 0.99474303 0.010513940 0.005256970
[165,] 0.99381470 0.012370593 0.006185297
[166,] 0.99343900 0.013121994 0.006560997
[167,] 0.99491972 0.010160567 0.005080284
[168,] 0.99428472 0.011430551 0.005715275
[169,] 0.99340195 0.013196091 0.006598046
[170,] 0.99172280 0.016554398 0.008277199
[171,] 0.99127896 0.017442072 0.008721036
[172,] 0.98923379 0.021532419 0.010766210
[173,] 0.98678239 0.026435214 0.013217607
[174,] 0.98384588 0.032308243 0.016154121
[175,] 0.98028692 0.039426162 0.019713081
[176,] 0.97634128 0.047317431 0.023658715
[177,] 0.97207533 0.055849337 0.027924668
[178,] 0.97131907 0.057361864 0.028680932
[179,] 0.97561923 0.048761545 0.024380773
[180,] 0.97600623 0.047987531 0.023993766
[181,] 0.97117247 0.057655064 0.028827532
[182,] 0.96543204 0.069135915 0.034567958
[183,] 0.95879104 0.082417915 0.041208958
[184,] 0.95485310 0.090293794 0.045146897
[185,] 0.95691919 0.086161623 0.043080812
[186,] 0.95799387 0.084012260 0.042006130
[187,] 0.97518552 0.049628952 0.024814476
[188,] 0.97627244 0.047455113 0.023727557
[189,] 0.97187694 0.056246119 0.028123059
[190,] 0.97110007 0.057799857 0.028899928
[191,] 0.97728512 0.045429765 0.022714883
[192,] 0.97233176 0.055336478 0.027668239
[193,] 0.96985203 0.060295940 0.030147970
[194,] 0.96951036 0.060979274 0.030489637
[195,] 0.96608931 0.067821389 0.033910694
[196,] 0.96614330 0.067713393 0.033856696
[197,] 0.97773593 0.044528138 0.022264069
[198,] 0.97336715 0.053265696 0.026632848
[199,] 0.96824910 0.063501808 0.031750904
[200,] 0.96894801 0.062103989 0.031051994
[201,] 0.96330239 0.073395210 0.036697605
[202,] 0.97171071 0.056578581 0.028289290
[203,] 0.97590780 0.048184405 0.024092203
[204,] 0.97980514 0.040389720 0.020194860
[205,] 0.97795805 0.044083909 0.022041955
[206,] 0.97646829 0.047063410 0.023531705
[207,] 0.97119182 0.057616365 0.028808183
[208,] 0.96510953 0.069780937 0.034890468
[209,] 0.96256625 0.074867504 0.037433752
[210,] 0.95783076 0.084338480 0.042169240
[211,] 0.95877075 0.082458506 0.041229253
[212,] 0.95850637 0.082987253 0.041493627
[213,] 0.96322511 0.073549779 0.036774890
[214,] 0.96048555 0.079028896 0.039514448
[215,] 0.96998637 0.060027253 0.030013626
[216,] 0.96720949 0.065581012 0.032790506
[217,] 0.96298316 0.074033678 0.037016839
[218,] 0.97219724 0.055605511 0.027802755
[219,] 0.96982500 0.060349994 0.030174997
[220,] 0.96340547 0.073189069 0.036594534
[221,] 0.95597877 0.088042459 0.044021229
[222,] 0.95752997 0.084940065 0.042470032
[223,] 0.94886386 0.102272272 0.051136136
[224,] 0.94680430 0.106391398 0.053195699
[225,] 0.94018920 0.119621609 0.059810804
[226,] 0.92838097 0.143238056 0.071619028
[227,] 0.91475549 0.170489015 0.085244508
[228,] 0.91669011 0.166619771 0.083309886
[229,] 0.92437966 0.151240670 0.075620335
[230,] 0.91488090 0.170238208 0.085119104
[231,] 0.90156464 0.196870715 0.098435357
[232,] 0.92533667 0.149326665 0.074663332
[233,] 0.94875749 0.102485023 0.051242511
[234,] 0.94738916 0.105221683 0.052610841
[235,] 0.95105458 0.097890834 0.048945417
[236,] 0.97183324 0.056333518 0.028166759
[237,] 0.96604956 0.067900887 0.033950443
[238,] 0.97526372 0.049472557 0.024736279
[239,] 0.97455940 0.050881208 0.025440604
[240,] 0.97520077 0.049598454 0.024799227
[241,] 0.97122903 0.057541933 0.028770967
[242,] 0.97558603 0.048827930 0.024413965
[243,] 0.97204355 0.055912897 0.027956448
[244,] 0.96613305 0.067733906 0.033866953
[245,] 0.96667916 0.066641671 0.033320835
[246,] 0.97146124 0.057077525 0.028538763
[247,] 0.97123610 0.057527802 0.028763901
[248,] 0.97383756 0.052324879 0.026162439
[249,] 0.97079202 0.058415951 0.029207976
[250,] 0.96369242 0.072615150 0.036307575
[251,] 0.96422533 0.071549336 0.035774668
[252,] 0.96472489 0.070550221 0.035275111
[253,] 0.96602308 0.067953845 0.033976922
[254,] 0.96664560 0.066708810 0.033354405
[255,] 0.96003486 0.079930271 0.039965135
[256,] 0.95558218 0.088835647 0.044417824
[257,] 0.94465444 0.110691117 0.055345559
[258,] 0.95360664 0.092786727 0.046393364
[259,] 0.94378797 0.112424060 0.056212030
[260,] 0.93076420 0.138471608 0.069235804
[261,] 0.94657030 0.106859395 0.053429698
[262,] 0.94368323 0.112633540 0.056316770
[263,] 0.94252783 0.114944332 0.057472166
[264,] 0.92908391 0.141832171 0.070916086
[265,] 0.91552790 0.168944193 0.084472096
[266,] 0.92706012 0.145879751 0.072939876
[267,] 0.90840736 0.183185280 0.091592640
[268,] 0.88700269 0.225994612 0.112997306
[269,] 0.88386023 0.232279544 0.116139772
[270,] 0.85767158 0.284656839 0.142328420
[271,] 0.82904817 0.341903660 0.170951830
[272,] 0.81850150 0.362996999 0.181498500
[273,] 0.78991133 0.420177337 0.210088668
[274,] 0.74919839 0.501603210 0.250801605
[275,] 0.73846707 0.523065863 0.261532931
[276,] 0.75978416 0.480431673 0.240215836
[277,] 0.71449436 0.571011279 0.285505640
[278,] 0.73978696 0.520426080 0.260213040
[279,] 0.81488248 0.370235047 0.185117524
[280,] 0.78178027 0.436439452 0.218219726
[281,] 0.73516177 0.529676463 0.264838232
[282,] 0.68807770 0.623844602 0.311922301
[283,] 0.78949563 0.421008747 0.210504373
[284,] 0.85624474 0.287510517 0.143755259
[285,] 0.86958633 0.260827331 0.130413666
[286,] 0.84000734 0.319985313 0.159992657
[287,] 0.79478372 0.410432561 0.205216280
[288,] 0.74335836 0.513283272 0.256641636
[289,] 0.90518511 0.189629786 0.094814893
[290,] 0.88601733 0.227965335 0.113982668
[291,] 0.84803040 0.303939193 0.151969596
[292,] 0.79756203 0.404875932 0.202437966
[293,] 0.82242212 0.355155754 0.177577877
[294,] 0.76727898 0.465442032 0.232721016
[295,] 0.73188469 0.536230614 0.268115307
[296,] 0.66648715 0.667025703 0.333512852
[297,] 0.62188930 0.756221410 0.378110705
[298,] 0.56918490 0.861630207 0.430815103
[299,] 0.47089776 0.941795517 0.529102242
[300,] 0.42842096 0.856841912 0.571579044
[301,] 0.47639916 0.952798323 0.523600838
[302,] 0.54079784 0.918404327 0.459202163
[303,] 0.39750605 0.795012107 0.602493946
[304,] 0.25443428 0.508868552 0.745565724
> postscript(file="/var/www/rcomp/tmp/1fb161321948814.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2hcpa1321948814.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3rf541321948814.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/49q4r1321948814.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5abfe1321948814.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 = 323
Frequency = 1
1 2 3 4 5 6
-0.391858222 -0.380895697 -0.118024976 0.399748851 0.163280935 0.423860597
7 8 9 10 11 12
0.179920015 -0.311384993 -0.043767117 -0.011990185 -0.001923055 -0.232475855
13 14 15 16 17 18
0.151908940 0.402782202 -0.095183892 0.178915989 0.194138839 -0.032292183
19 20 21 22 23 24
-0.270790448 -0.488634410 0.262248335 -0.473629574 -0.213922572 0.054484554
25 26 27 28 29 30
0.418961215 0.437662456 -0.794190336 -0.030569503 -0.271202479 0.489287643
31 32 33 34 35 36
-0.501927825 -0.484531916 0.028315675 0.282428946 -0.709999950 0.049762001
37 38 39 40 41 42
0.677580986 0.188789399 -0.041163505 -0.535543483 0.219987394 0.232224025
43 44 45 46 47 48
-0.001375672 0.251938075 -0.228937298 -0.473006559 0.036526305 -0.198373789
49 50 51 52 53 54
0.913167251 0.434626218 -0.061043478 -0.297739097 0.218638868 -0.020064365
55 56 57 58 59 60
-0.002618054 -0.485456177 0.266516658 0.032967364 -0.699202671 0.312232653
61 62 63 64 65 66
0.433396682 -0.312123193 -0.800899166 -0.038973253 0.223179572 -0.755744720
67 68 69 70 71 72
-0.489487917 0.265976944 0.015308657 -0.471097783 -0.201955365 0.558680830
73 74 75 76 77 78
0.167641038 0.449226044 -0.289050045 0.223536836 -0.262369137 0.253331061
79 80 81 82 83 84
-0.725909682 0.035020231 0.293799029 0.050346859 -0.926552424 0.594207624
85 86 87 88 89 90
0.202017670 0.216267254 -0.514134924 0.240034880 -0.005997131 -0.240891886
91 92 93 94 95 96
-0.726943931 0.033219465 0.291664416 0.301611570 -0.168672481 0.336719839
97 98 99 100 101 102
0.441667391 0.456428121 -0.519365459 -0.258957956 -0.251842257 0.004056265
103 104 105 106 107 108
-0.238812027 -0.468057309 0.296059057 0.306844083 -0.455017390 0.307809258
109 110 111 112 113 114
0.191565697 0.453268457 -0.029019974 0.226137918 -0.008311506 0.257171659
115 116 117 118 119 120
-0.485333922 0.029686727 0.552230444 -0.694261907 0.067667160 0.818984357
121 122 123 124 125 126
-0.304340699 0.703409486 -0.521617181 0.488561432 -0.003588568 -0.488606266
127 128 129 130 131 132
-0.477970467 0.535557464 0.796364829 -0.439412860 0.319060955 0.826575526
133 134 135 136 137 138
-0.299530782 -0.287082062 0.219843292 0.231116929 0.249208000 -0.236887328
139 140 141 142 143 144
-0.480794902 0.282264383 0.302824010 0.288383496 -0.193581966 0.577706227
145 146 147 148 149 150
-0.537989816 -0.285199271 -0.505270451 0.001378579 0.258226457 -0.478487348
151 152 153 154 155 156
-0.468642580 0.292464832 0.304413083 0.319159771 -0.171204978 -0.413454397
157 158 159 160 161 162
-0.035092445 -0.022751452 -0.026843961 -0.251375095 -0.235030929 0.020344392
163 164 165 166 167 168
0.528750601 0.029183676 0.300293579 -0.432281657 0.577730336 0.090072120
169 170 171 172 173 174
0.209684139 -0.283249774 0.222331902 -0.250648409 0.502803499 0.253366035
175 176 177 178 179 180
0.270709959 -0.464067586 -0.205146078 -0.187694428 0.073006992 0.336161156
181 182 183 184 185 186
-0.042453243 -0.028938949 -0.013555994 -0.016892260 0.239906592 0.010532631
187 188 189 190 191 192
0.517899558 0.541504301 -0.211264295 0.053539322 0.066532195 0.072813580
193 194 195 196 197 198
-0.052279332 -0.279878920 -0.271339484 -0.510986733 -0.246520116 0.263497602
199 200 201 202 203 204
-0.218500868 -0.453778090 0.059437938 0.326186516 0.361053637 0.352388109
205 206 207 208 209 210
0.484698087 -0.502260419 0.009320188 0.260792941 -0.223223417 0.286500583
211 212 213 214 215 216
0.564582684 0.556797916 0.567972530 -0.168056386 -0.157420586 0.110004177
217 218 219 220 221 222
-0.012599829 -0.256644774 0.245423722 -0.231604626 0.535146919 0.544169428
223 224 225 226 227 228
-0.191703141 -0.438179879 0.330977361 -0.145585702 0.612551103 0.369143996
229 230 231 232 233 234
0.239489920 0.007235683 -0.233129462 0.031536784 -0.210924724 0.298469187
235 236 237 238 239 240
0.054378774 0.066653752 0.331603441 0.351696787 0.104893324 -0.125826097
241 242 243 244 245 246
-0.503161051 0.513989760 0.266746506 -0.479507077 0.533515047 -0.202536603
247 248 249 250 251 252
0.305629848 -0.437593629 -0.442359169 0.085449970 0.346274729 -0.132766771
253 254 255 256 257 258
-0.014801051 0.241881383 0.257354670 -0.487203994 -0.478925268 0.038414108
259 260 261 262 263 264
-0.197998067 -0.453604447 -0.442179397 -0.427514338 0.329290645 -0.130720512
265 266 267 268 269 270
0.223055319 -0.018267479 -0.510134035 0.240103950 0.015517074 -0.476293741
271 272 273 274 275 276
-0.219138508 0.278591187 -0.209067029 -0.203569555 -0.460413669 0.055130182
277 278 279 280 281 282
0.172419717 0.434916662 0.189686361 0.204876204 -0.275392756 -0.028873908
283 284 285 286 287 288
-0.004321786 0.270869965 -0.454785058 0.063548747 -0.406615522 -0.388818169
289 290 291 292 293 294
0.258554754 0.017595053 0.022553712 -0.457970420 0.560242408 -0.422606781
295 296 297 298 299 300
0.328020594 0.095415316 0.107601544 -0.628733550 -0.118637357 0.146658035
301 302 303 304 305 306
-0.228125151 0.028840727 -0.457717125 0.031232991 -0.213975180 0.297403612
307 308 309 310 311 312
-0.205655340 0.058103655 0.064835105 0.582363836 0.336693744 -0.159593242
313 314 315 316 317 318
-0.041281819 -0.278345874 0.460483671 0.229536548 0.245448043 -0.489951727
319 320 321 322 323
0.256978048 -0.225914035 -0.450500911 -0.427339113 -0.165973957
> postscript(file="/var/www/rcomp/tmp/62wt51321948814.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 = 323
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.391858222 NA
1 -0.380895697 -0.391858222
2 -0.118024976 -0.380895697
3 0.399748851 -0.118024976
4 0.163280935 0.399748851
5 0.423860597 0.163280935
6 0.179920015 0.423860597
7 -0.311384993 0.179920015
8 -0.043767117 -0.311384993
9 -0.011990185 -0.043767117
10 -0.001923055 -0.011990185
11 -0.232475855 -0.001923055
12 0.151908940 -0.232475855
13 0.402782202 0.151908940
14 -0.095183892 0.402782202
15 0.178915989 -0.095183892
16 0.194138839 0.178915989
17 -0.032292183 0.194138839
18 -0.270790448 -0.032292183
19 -0.488634410 -0.270790448
20 0.262248335 -0.488634410
21 -0.473629574 0.262248335
22 -0.213922572 -0.473629574
23 0.054484554 -0.213922572
24 0.418961215 0.054484554
25 0.437662456 0.418961215
26 -0.794190336 0.437662456
27 -0.030569503 -0.794190336
28 -0.271202479 -0.030569503
29 0.489287643 -0.271202479
30 -0.501927825 0.489287643
31 -0.484531916 -0.501927825
32 0.028315675 -0.484531916
33 0.282428946 0.028315675
34 -0.709999950 0.282428946
35 0.049762001 -0.709999950
36 0.677580986 0.049762001
37 0.188789399 0.677580986
38 -0.041163505 0.188789399
39 -0.535543483 -0.041163505
40 0.219987394 -0.535543483
41 0.232224025 0.219987394
42 -0.001375672 0.232224025
43 0.251938075 -0.001375672
44 -0.228937298 0.251938075
45 -0.473006559 -0.228937298
46 0.036526305 -0.473006559
47 -0.198373789 0.036526305
48 0.913167251 -0.198373789
49 0.434626218 0.913167251
50 -0.061043478 0.434626218
51 -0.297739097 -0.061043478
52 0.218638868 -0.297739097
53 -0.020064365 0.218638868
54 -0.002618054 -0.020064365
55 -0.485456177 -0.002618054
56 0.266516658 -0.485456177
57 0.032967364 0.266516658
58 -0.699202671 0.032967364
59 0.312232653 -0.699202671
60 0.433396682 0.312232653
61 -0.312123193 0.433396682
62 -0.800899166 -0.312123193
63 -0.038973253 -0.800899166
64 0.223179572 -0.038973253
65 -0.755744720 0.223179572
66 -0.489487917 -0.755744720
67 0.265976944 -0.489487917
68 0.015308657 0.265976944
69 -0.471097783 0.015308657
70 -0.201955365 -0.471097783
71 0.558680830 -0.201955365
72 0.167641038 0.558680830
73 0.449226044 0.167641038
74 -0.289050045 0.449226044
75 0.223536836 -0.289050045
76 -0.262369137 0.223536836
77 0.253331061 -0.262369137
78 -0.725909682 0.253331061
79 0.035020231 -0.725909682
80 0.293799029 0.035020231
81 0.050346859 0.293799029
82 -0.926552424 0.050346859
83 0.594207624 -0.926552424
84 0.202017670 0.594207624
85 0.216267254 0.202017670
86 -0.514134924 0.216267254
87 0.240034880 -0.514134924
88 -0.005997131 0.240034880
89 -0.240891886 -0.005997131
90 -0.726943931 -0.240891886
91 0.033219465 -0.726943931
92 0.291664416 0.033219465
93 0.301611570 0.291664416
94 -0.168672481 0.301611570
95 0.336719839 -0.168672481
96 0.441667391 0.336719839
97 0.456428121 0.441667391
98 -0.519365459 0.456428121
99 -0.258957956 -0.519365459
100 -0.251842257 -0.258957956
101 0.004056265 -0.251842257
102 -0.238812027 0.004056265
103 -0.468057309 -0.238812027
104 0.296059057 -0.468057309
105 0.306844083 0.296059057
106 -0.455017390 0.306844083
107 0.307809258 -0.455017390
108 0.191565697 0.307809258
109 0.453268457 0.191565697
110 -0.029019974 0.453268457
111 0.226137918 -0.029019974
112 -0.008311506 0.226137918
113 0.257171659 -0.008311506
114 -0.485333922 0.257171659
115 0.029686727 -0.485333922
116 0.552230444 0.029686727
117 -0.694261907 0.552230444
118 0.067667160 -0.694261907
119 0.818984357 0.067667160
120 -0.304340699 0.818984357
121 0.703409486 -0.304340699
122 -0.521617181 0.703409486
123 0.488561432 -0.521617181
124 -0.003588568 0.488561432
125 -0.488606266 -0.003588568
126 -0.477970467 -0.488606266
127 0.535557464 -0.477970467
128 0.796364829 0.535557464
129 -0.439412860 0.796364829
130 0.319060955 -0.439412860
131 0.826575526 0.319060955
132 -0.299530782 0.826575526
133 -0.287082062 -0.299530782
134 0.219843292 -0.287082062
135 0.231116929 0.219843292
136 0.249208000 0.231116929
137 -0.236887328 0.249208000
138 -0.480794902 -0.236887328
139 0.282264383 -0.480794902
140 0.302824010 0.282264383
141 0.288383496 0.302824010
142 -0.193581966 0.288383496
143 0.577706227 -0.193581966
144 -0.537989816 0.577706227
145 -0.285199271 -0.537989816
146 -0.505270451 -0.285199271
147 0.001378579 -0.505270451
148 0.258226457 0.001378579
149 -0.478487348 0.258226457
150 -0.468642580 -0.478487348
151 0.292464832 -0.468642580
152 0.304413083 0.292464832
153 0.319159771 0.304413083
154 -0.171204978 0.319159771
155 -0.413454397 -0.171204978
156 -0.035092445 -0.413454397
157 -0.022751452 -0.035092445
158 -0.026843961 -0.022751452
159 -0.251375095 -0.026843961
160 -0.235030929 -0.251375095
161 0.020344392 -0.235030929
162 0.528750601 0.020344392
163 0.029183676 0.528750601
164 0.300293579 0.029183676
165 -0.432281657 0.300293579
166 0.577730336 -0.432281657
167 0.090072120 0.577730336
168 0.209684139 0.090072120
169 -0.283249774 0.209684139
170 0.222331902 -0.283249774
171 -0.250648409 0.222331902
172 0.502803499 -0.250648409
173 0.253366035 0.502803499
174 0.270709959 0.253366035
175 -0.464067586 0.270709959
176 -0.205146078 -0.464067586
177 -0.187694428 -0.205146078
178 0.073006992 -0.187694428
179 0.336161156 0.073006992
180 -0.042453243 0.336161156
181 -0.028938949 -0.042453243
182 -0.013555994 -0.028938949
183 -0.016892260 -0.013555994
184 0.239906592 -0.016892260
185 0.010532631 0.239906592
186 0.517899558 0.010532631
187 0.541504301 0.517899558
188 -0.211264295 0.541504301
189 0.053539322 -0.211264295
190 0.066532195 0.053539322
191 0.072813580 0.066532195
192 -0.052279332 0.072813580
193 -0.279878920 -0.052279332
194 -0.271339484 -0.279878920
195 -0.510986733 -0.271339484
196 -0.246520116 -0.510986733
197 0.263497602 -0.246520116
198 -0.218500868 0.263497602
199 -0.453778090 -0.218500868
200 0.059437938 -0.453778090
201 0.326186516 0.059437938
202 0.361053637 0.326186516
203 0.352388109 0.361053637
204 0.484698087 0.352388109
205 -0.502260419 0.484698087
206 0.009320188 -0.502260419
207 0.260792941 0.009320188
208 -0.223223417 0.260792941
209 0.286500583 -0.223223417
210 0.564582684 0.286500583
211 0.556797916 0.564582684
212 0.567972530 0.556797916
213 -0.168056386 0.567972530
214 -0.157420586 -0.168056386
215 0.110004177 -0.157420586
216 -0.012599829 0.110004177
217 -0.256644774 -0.012599829
218 0.245423722 -0.256644774
219 -0.231604626 0.245423722
220 0.535146919 -0.231604626
221 0.544169428 0.535146919
222 -0.191703141 0.544169428
223 -0.438179879 -0.191703141
224 0.330977361 -0.438179879
225 -0.145585702 0.330977361
226 0.612551103 -0.145585702
227 0.369143996 0.612551103
228 0.239489920 0.369143996
229 0.007235683 0.239489920
230 -0.233129462 0.007235683
231 0.031536784 -0.233129462
232 -0.210924724 0.031536784
233 0.298469187 -0.210924724
234 0.054378774 0.298469187
235 0.066653752 0.054378774
236 0.331603441 0.066653752
237 0.351696787 0.331603441
238 0.104893324 0.351696787
239 -0.125826097 0.104893324
240 -0.503161051 -0.125826097
241 0.513989760 -0.503161051
242 0.266746506 0.513989760
243 -0.479507077 0.266746506
244 0.533515047 -0.479507077
245 -0.202536603 0.533515047
246 0.305629848 -0.202536603
247 -0.437593629 0.305629848
248 -0.442359169 -0.437593629
249 0.085449970 -0.442359169
250 0.346274729 0.085449970
251 -0.132766771 0.346274729
252 -0.014801051 -0.132766771
253 0.241881383 -0.014801051
254 0.257354670 0.241881383
255 -0.487203994 0.257354670
256 -0.478925268 -0.487203994
257 0.038414108 -0.478925268
258 -0.197998067 0.038414108
259 -0.453604447 -0.197998067
260 -0.442179397 -0.453604447
261 -0.427514338 -0.442179397
262 0.329290645 -0.427514338
263 -0.130720512 0.329290645
264 0.223055319 -0.130720512
265 -0.018267479 0.223055319
266 -0.510134035 -0.018267479
267 0.240103950 -0.510134035
268 0.015517074 0.240103950
269 -0.476293741 0.015517074
270 -0.219138508 -0.476293741
271 0.278591187 -0.219138508
272 -0.209067029 0.278591187
273 -0.203569555 -0.209067029
274 -0.460413669 -0.203569555
275 0.055130182 -0.460413669
276 0.172419717 0.055130182
277 0.434916662 0.172419717
278 0.189686361 0.434916662
279 0.204876204 0.189686361
280 -0.275392756 0.204876204
281 -0.028873908 -0.275392756
282 -0.004321786 -0.028873908
283 0.270869965 -0.004321786
284 -0.454785058 0.270869965
285 0.063548747 -0.454785058
286 -0.406615522 0.063548747
287 -0.388818169 -0.406615522
288 0.258554754 -0.388818169
289 0.017595053 0.258554754
290 0.022553712 0.017595053
291 -0.457970420 0.022553712
292 0.560242408 -0.457970420
293 -0.422606781 0.560242408
294 0.328020594 -0.422606781
295 0.095415316 0.328020594
296 0.107601544 0.095415316
297 -0.628733550 0.107601544
298 -0.118637357 -0.628733550
299 0.146658035 -0.118637357
300 -0.228125151 0.146658035
301 0.028840727 -0.228125151
302 -0.457717125 0.028840727
303 0.031232991 -0.457717125
304 -0.213975180 0.031232991
305 0.297403612 -0.213975180
306 -0.205655340 0.297403612
307 0.058103655 -0.205655340
308 0.064835105 0.058103655
309 0.582363836 0.064835105
310 0.336693744 0.582363836
311 -0.159593242 0.336693744
312 -0.041281819 -0.159593242
313 -0.278345874 -0.041281819
314 0.460483671 -0.278345874
315 0.229536548 0.460483671
316 0.245448043 0.229536548
317 -0.489951727 0.245448043
318 0.256978048 -0.489951727
319 -0.225914035 0.256978048
320 -0.450500911 -0.225914035
321 -0.427339113 -0.450500911
322 -0.165973957 -0.427339113
323 NA -0.165973957
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.380895697 -0.391858222
[2,] -0.118024976 -0.380895697
[3,] 0.399748851 -0.118024976
[4,] 0.163280935 0.399748851
[5,] 0.423860597 0.163280935
[6,] 0.179920015 0.423860597
[7,] -0.311384993 0.179920015
[8,] -0.043767117 -0.311384993
[9,] -0.011990185 -0.043767117
[10,] -0.001923055 -0.011990185
[11,] -0.232475855 -0.001923055
[12,] 0.151908940 -0.232475855
[13,] 0.402782202 0.151908940
[14,] -0.095183892 0.402782202
[15,] 0.178915989 -0.095183892
[16,] 0.194138839 0.178915989
[17,] -0.032292183 0.194138839
[18,] -0.270790448 -0.032292183
[19,] -0.488634410 -0.270790448
[20,] 0.262248335 -0.488634410
[21,] -0.473629574 0.262248335
[22,] -0.213922572 -0.473629574
[23,] 0.054484554 -0.213922572
[24,] 0.418961215 0.054484554
[25,] 0.437662456 0.418961215
[26,] -0.794190336 0.437662456
[27,] -0.030569503 -0.794190336
[28,] -0.271202479 -0.030569503
[29,] 0.489287643 -0.271202479
[30,] -0.501927825 0.489287643
[31,] -0.484531916 -0.501927825
[32,] 0.028315675 -0.484531916
[33,] 0.282428946 0.028315675
[34,] -0.709999950 0.282428946
[35,] 0.049762001 -0.709999950
[36,] 0.677580986 0.049762001
[37,] 0.188789399 0.677580986
[38,] -0.041163505 0.188789399
[39,] -0.535543483 -0.041163505
[40,] 0.219987394 -0.535543483
[41,] 0.232224025 0.219987394
[42,] -0.001375672 0.232224025
[43,] 0.251938075 -0.001375672
[44,] -0.228937298 0.251938075
[45,] -0.473006559 -0.228937298
[46,] 0.036526305 -0.473006559
[47,] -0.198373789 0.036526305
[48,] 0.913167251 -0.198373789
[49,] 0.434626218 0.913167251
[50,] -0.061043478 0.434626218
[51,] -0.297739097 -0.061043478
[52,] 0.218638868 -0.297739097
[53,] -0.020064365 0.218638868
[54,] -0.002618054 -0.020064365
[55,] -0.485456177 -0.002618054
[56,] 0.266516658 -0.485456177
[57,] 0.032967364 0.266516658
[58,] -0.699202671 0.032967364
[59,] 0.312232653 -0.699202671
[60,] 0.433396682 0.312232653
[61,] -0.312123193 0.433396682
[62,] -0.800899166 -0.312123193
[63,] -0.038973253 -0.800899166
[64,] 0.223179572 -0.038973253
[65,] -0.755744720 0.223179572
[66,] -0.489487917 -0.755744720
[67,] 0.265976944 -0.489487917
[68,] 0.015308657 0.265976944
[69,] -0.471097783 0.015308657
[70,] -0.201955365 -0.471097783
[71,] 0.558680830 -0.201955365
[72,] 0.167641038 0.558680830
[73,] 0.449226044 0.167641038
[74,] -0.289050045 0.449226044
[75,] 0.223536836 -0.289050045
[76,] -0.262369137 0.223536836
[77,] 0.253331061 -0.262369137
[78,] -0.725909682 0.253331061
[79,] 0.035020231 -0.725909682
[80,] 0.293799029 0.035020231
[81,] 0.050346859 0.293799029
[82,] -0.926552424 0.050346859
[83,] 0.594207624 -0.926552424
[84,] 0.202017670 0.594207624
[85,] 0.216267254 0.202017670
[86,] -0.514134924 0.216267254
[87,] 0.240034880 -0.514134924
[88,] -0.005997131 0.240034880
[89,] -0.240891886 -0.005997131
[90,] -0.726943931 -0.240891886
[91,] 0.033219465 -0.726943931
[92,] 0.291664416 0.033219465
[93,] 0.301611570 0.291664416
[94,] -0.168672481 0.301611570
[95,] 0.336719839 -0.168672481
[96,] 0.441667391 0.336719839
[97,] 0.456428121 0.441667391
[98,] -0.519365459 0.456428121
[99,] -0.258957956 -0.519365459
[100,] -0.251842257 -0.258957956
[101,] 0.004056265 -0.251842257
[102,] -0.238812027 0.004056265
[103,] -0.468057309 -0.238812027
[104,] 0.296059057 -0.468057309
[105,] 0.306844083 0.296059057
[106,] -0.455017390 0.306844083
[107,] 0.307809258 -0.455017390
[108,] 0.191565697 0.307809258
[109,] 0.453268457 0.191565697
[110,] -0.029019974 0.453268457
[111,] 0.226137918 -0.029019974
[112,] -0.008311506 0.226137918
[113,] 0.257171659 -0.008311506
[114,] -0.485333922 0.257171659
[115,] 0.029686727 -0.485333922
[116,] 0.552230444 0.029686727
[117,] -0.694261907 0.552230444
[118,] 0.067667160 -0.694261907
[119,] 0.818984357 0.067667160
[120,] -0.304340699 0.818984357
[121,] 0.703409486 -0.304340699
[122,] -0.521617181 0.703409486
[123,] 0.488561432 -0.521617181
[124,] -0.003588568 0.488561432
[125,] -0.488606266 -0.003588568
[126,] -0.477970467 -0.488606266
[127,] 0.535557464 -0.477970467
[128,] 0.796364829 0.535557464
[129,] -0.439412860 0.796364829
[130,] 0.319060955 -0.439412860
[131,] 0.826575526 0.319060955
[132,] -0.299530782 0.826575526
[133,] -0.287082062 -0.299530782
[134,] 0.219843292 -0.287082062
[135,] 0.231116929 0.219843292
[136,] 0.249208000 0.231116929
[137,] -0.236887328 0.249208000
[138,] -0.480794902 -0.236887328
[139,] 0.282264383 -0.480794902
[140,] 0.302824010 0.282264383
[141,] 0.288383496 0.302824010
[142,] -0.193581966 0.288383496
[143,] 0.577706227 -0.193581966
[144,] -0.537989816 0.577706227
[145,] -0.285199271 -0.537989816
[146,] -0.505270451 -0.285199271
[147,] 0.001378579 -0.505270451
[148,] 0.258226457 0.001378579
[149,] -0.478487348 0.258226457
[150,] -0.468642580 -0.478487348
[151,] 0.292464832 -0.468642580
[152,] 0.304413083 0.292464832
[153,] 0.319159771 0.304413083
[154,] -0.171204978 0.319159771
[155,] -0.413454397 -0.171204978
[156,] -0.035092445 -0.413454397
[157,] -0.022751452 -0.035092445
[158,] -0.026843961 -0.022751452
[159,] -0.251375095 -0.026843961
[160,] -0.235030929 -0.251375095
[161,] 0.020344392 -0.235030929
[162,] 0.528750601 0.020344392
[163,] 0.029183676 0.528750601
[164,] 0.300293579 0.029183676
[165,] -0.432281657 0.300293579
[166,] 0.577730336 -0.432281657
[167,] 0.090072120 0.577730336
[168,] 0.209684139 0.090072120
[169,] -0.283249774 0.209684139
[170,] 0.222331902 -0.283249774
[171,] -0.250648409 0.222331902
[172,] 0.502803499 -0.250648409
[173,] 0.253366035 0.502803499
[174,] 0.270709959 0.253366035
[175,] -0.464067586 0.270709959
[176,] -0.205146078 -0.464067586
[177,] -0.187694428 -0.205146078
[178,] 0.073006992 -0.187694428
[179,] 0.336161156 0.073006992
[180,] -0.042453243 0.336161156
[181,] -0.028938949 -0.042453243
[182,] -0.013555994 -0.028938949
[183,] -0.016892260 -0.013555994
[184,] 0.239906592 -0.016892260
[185,] 0.010532631 0.239906592
[186,] 0.517899558 0.010532631
[187,] 0.541504301 0.517899558
[188,] -0.211264295 0.541504301
[189,] 0.053539322 -0.211264295
[190,] 0.066532195 0.053539322
[191,] 0.072813580 0.066532195
[192,] -0.052279332 0.072813580
[193,] -0.279878920 -0.052279332
[194,] -0.271339484 -0.279878920
[195,] -0.510986733 -0.271339484
[196,] -0.246520116 -0.510986733
[197,] 0.263497602 -0.246520116
[198,] -0.218500868 0.263497602
[199,] -0.453778090 -0.218500868
[200,] 0.059437938 -0.453778090
[201,] 0.326186516 0.059437938
[202,] 0.361053637 0.326186516
[203,] 0.352388109 0.361053637
[204,] 0.484698087 0.352388109
[205,] -0.502260419 0.484698087
[206,] 0.009320188 -0.502260419
[207,] 0.260792941 0.009320188
[208,] -0.223223417 0.260792941
[209,] 0.286500583 -0.223223417
[210,] 0.564582684 0.286500583
[211,] 0.556797916 0.564582684
[212,] 0.567972530 0.556797916
[213,] -0.168056386 0.567972530
[214,] -0.157420586 -0.168056386
[215,] 0.110004177 -0.157420586
[216,] -0.012599829 0.110004177
[217,] -0.256644774 -0.012599829
[218,] 0.245423722 -0.256644774
[219,] -0.231604626 0.245423722
[220,] 0.535146919 -0.231604626
[221,] 0.544169428 0.535146919
[222,] -0.191703141 0.544169428
[223,] -0.438179879 -0.191703141
[224,] 0.330977361 -0.438179879
[225,] -0.145585702 0.330977361
[226,] 0.612551103 -0.145585702
[227,] 0.369143996 0.612551103
[228,] 0.239489920 0.369143996
[229,] 0.007235683 0.239489920
[230,] -0.233129462 0.007235683
[231,] 0.031536784 -0.233129462
[232,] -0.210924724 0.031536784
[233,] 0.298469187 -0.210924724
[234,] 0.054378774 0.298469187
[235,] 0.066653752 0.054378774
[236,] 0.331603441 0.066653752
[237,] 0.351696787 0.331603441
[238,] 0.104893324 0.351696787
[239,] -0.125826097 0.104893324
[240,] -0.503161051 -0.125826097
[241,] 0.513989760 -0.503161051
[242,] 0.266746506 0.513989760
[243,] -0.479507077 0.266746506
[244,] 0.533515047 -0.479507077
[245,] -0.202536603 0.533515047
[246,] 0.305629848 -0.202536603
[247,] -0.437593629 0.305629848
[248,] -0.442359169 -0.437593629
[249,] 0.085449970 -0.442359169
[250,] 0.346274729 0.085449970
[251,] -0.132766771 0.346274729
[252,] -0.014801051 -0.132766771
[253,] 0.241881383 -0.014801051
[254,] 0.257354670 0.241881383
[255,] -0.487203994 0.257354670
[256,] -0.478925268 -0.487203994
[257,] 0.038414108 -0.478925268
[258,] -0.197998067 0.038414108
[259,] -0.453604447 -0.197998067
[260,] -0.442179397 -0.453604447
[261,] -0.427514338 -0.442179397
[262,] 0.329290645 -0.427514338
[263,] -0.130720512 0.329290645
[264,] 0.223055319 -0.130720512
[265,] -0.018267479 0.223055319
[266,] -0.510134035 -0.018267479
[267,] 0.240103950 -0.510134035
[268,] 0.015517074 0.240103950
[269,] -0.476293741 0.015517074
[270,] -0.219138508 -0.476293741
[271,] 0.278591187 -0.219138508
[272,] -0.209067029 0.278591187
[273,] -0.203569555 -0.209067029
[274,] -0.460413669 -0.203569555
[275,] 0.055130182 -0.460413669
[276,] 0.172419717 0.055130182
[277,] 0.434916662 0.172419717
[278,] 0.189686361 0.434916662
[279,] 0.204876204 0.189686361
[280,] -0.275392756 0.204876204
[281,] -0.028873908 -0.275392756
[282,] -0.004321786 -0.028873908
[283,] 0.270869965 -0.004321786
[284,] -0.454785058 0.270869965
[285,] 0.063548747 -0.454785058
[286,] -0.406615522 0.063548747
[287,] -0.388818169 -0.406615522
[288,] 0.258554754 -0.388818169
[289,] 0.017595053 0.258554754
[290,] 0.022553712 0.017595053
[291,] -0.457970420 0.022553712
[292,] 0.560242408 -0.457970420
[293,] -0.422606781 0.560242408
[294,] 0.328020594 -0.422606781
[295,] 0.095415316 0.328020594
[296,] 0.107601544 0.095415316
[297,] -0.628733550 0.107601544
[298,] -0.118637357 -0.628733550
[299,] 0.146658035 -0.118637357
[300,] -0.228125151 0.146658035
[301,] 0.028840727 -0.228125151
[302,] -0.457717125 0.028840727
[303,] 0.031232991 -0.457717125
[304,] -0.213975180 0.031232991
[305,] 0.297403612 -0.213975180
[306,] -0.205655340 0.297403612
[307,] 0.058103655 -0.205655340
[308,] 0.064835105 0.058103655
[309,] 0.582363836 0.064835105
[310,] 0.336693744 0.582363836
[311,] -0.159593242 0.336693744
[312,] -0.041281819 -0.159593242
[313,] -0.278345874 -0.041281819
[314,] 0.460483671 -0.278345874
[315,] 0.229536548 0.460483671
[316,] 0.245448043 0.229536548
[317,] -0.489951727 0.245448043
[318,] 0.256978048 -0.489951727
[319,] -0.225914035 0.256978048
[320,] -0.450500911 -0.225914035
[321,] -0.427339113 -0.450500911
[322,] -0.165973957 -0.427339113
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.380895697 -0.391858222
2 -0.118024976 -0.380895697
3 0.399748851 -0.118024976
4 0.163280935 0.399748851
5 0.423860597 0.163280935
6 0.179920015 0.423860597
7 -0.311384993 0.179920015
8 -0.043767117 -0.311384993
9 -0.011990185 -0.043767117
10 -0.001923055 -0.011990185
11 -0.232475855 -0.001923055
12 0.151908940 -0.232475855
13 0.402782202 0.151908940
14 -0.095183892 0.402782202
15 0.178915989 -0.095183892
16 0.194138839 0.178915989
17 -0.032292183 0.194138839
18 -0.270790448 -0.032292183
19 -0.488634410 -0.270790448
20 0.262248335 -0.488634410
21 -0.473629574 0.262248335
22 -0.213922572 -0.473629574
23 0.054484554 -0.213922572
24 0.418961215 0.054484554
25 0.437662456 0.418961215
26 -0.794190336 0.437662456
27 -0.030569503 -0.794190336
28 -0.271202479 -0.030569503
29 0.489287643 -0.271202479
30 -0.501927825 0.489287643
31 -0.484531916 -0.501927825
32 0.028315675 -0.484531916
33 0.282428946 0.028315675
34 -0.709999950 0.282428946
35 0.049762001 -0.709999950
36 0.677580986 0.049762001
37 0.188789399 0.677580986
38 -0.041163505 0.188789399
39 -0.535543483 -0.041163505
40 0.219987394 -0.535543483
41 0.232224025 0.219987394
42 -0.001375672 0.232224025
43 0.251938075 -0.001375672
44 -0.228937298 0.251938075
45 -0.473006559 -0.228937298
46 0.036526305 -0.473006559
47 -0.198373789 0.036526305
48 0.913167251 -0.198373789
49 0.434626218 0.913167251
50 -0.061043478 0.434626218
51 -0.297739097 -0.061043478
52 0.218638868 -0.297739097
53 -0.020064365 0.218638868
54 -0.002618054 -0.020064365
55 -0.485456177 -0.002618054
56 0.266516658 -0.485456177
57 0.032967364 0.266516658
58 -0.699202671 0.032967364
59 0.312232653 -0.699202671
60 0.433396682 0.312232653
61 -0.312123193 0.433396682
62 -0.800899166 -0.312123193
63 -0.038973253 -0.800899166
64 0.223179572 -0.038973253
65 -0.755744720 0.223179572
66 -0.489487917 -0.755744720
67 0.265976944 -0.489487917
68 0.015308657 0.265976944
69 -0.471097783 0.015308657
70 -0.201955365 -0.471097783
71 0.558680830 -0.201955365
72 0.167641038 0.558680830
73 0.449226044 0.167641038
74 -0.289050045 0.449226044
75 0.223536836 -0.289050045
76 -0.262369137 0.223536836
77 0.253331061 -0.262369137
78 -0.725909682 0.253331061
79 0.035020231 -0.725909682
80 0.293799029 0.035020231
81 0.050346859 0.293799029
82 -0.926552424 0.050346859
83 0.594207624 -0.926552424
84 0.202017670 0.594207624
85 0.216267254 0.202017670
86 -0.514134924 0.216267254
87 0.240034880 -0.514134924
88 -0.005997131 0.240034880
89 -0.240891886 -0.005997131
90 -0.726943931 -0.240891886
91 0.033219465 -0.726943931
92 0.291664416 0.033219465
93 0.301611570 0.291664416
94 -0.168672481 0.301611570
95 0.336719839 -0.168672481
96 0.441667391 0.336719839
97 0.456428121 0.441667391
98 -0.519365459 0.456428121
99 -0.258957956 -0.519365459
100 -0.251842257 -0.258957956
101 0.004056265 -0.251842257
102 -0.238812027 0.004056265
103 -0.468057309 -0.238812027
104 0.296059057 -0.468057309
105 0.306844083 0.296059057
106 -0.455017390 0.306844083
107 0.307809258 -0.455017390
108 0.191565697 0.307809258
109 0.453268457 0.191565697
110 -0.029019974 0.453268457
111 0.226137918 -0.029019974
112 -0.008311506 0.226137918
113 0.257171659 -0.008311506
114 -0.485333922 0.257171659
115 0.029686727 -0.485333922
116 0.552230444 0.029686727
117 -0.694261907 0.552230444
118 0.067667160 -0.694261907
119 0.818984357 0.067667160
120 -0.304340699 0.818984357
121 0.703409486 -0.304340699
122 -0.521617181 0.703409486
123 0.488561432 -0.521617181
124 -0.003588568 0.488561432
125 -0.488606266 -0.003588568
126 -0.477970467 -0.488606266
127 0.535557464 -0.477970467
128 0.796364829 0.535557464
129 -0.439412860 0.796364829
130 0.319060955 -0.439412860
131 0.826575526 0.319060955
132 -0.299530782 0.826575526
133 -0.287082062 -0.299530782
134 0.219843292 -0.287082062
135 0.231116929 0.219843292
136 0.249208000 0.231116929
137 -0.236887328 0.249208000
138 -0.480794902 -0.236887328
139 0.282264383 -0.480794902
140 0.302824010 0.282264383
141 0.288383496 0.302824010
142 -0.193581966 0.288383496
143 0.577706227 -0.193581966
144 -0.537989816 0.577706227
145 -0.285199271 -0.537989816
146 -0.505270451 -0.285199271
147 0.001378579 -0.505270451
148 0.258226457 0.001378579
149 -0.478487348 0.258226457
150 -0.468642580 -0.478487348
151 0.292464832 -0.468642580
152 0.304413083 0.292464832
153 0.319159771 0.304413083
154 -0.171204978 0.319159771
155 -0.413454397 -0.171204978
156 -0.035092445 -0.413454397
157 -0.022751452 -0.035092445
158 -0.026843961 -0.022751452
159 -0.251375095 -0.026843961
160 -0.235030929 -0.251375095
161 0.020344392 -0.235030929
162 0.528750601 0.020344392
163 0.029183676 0.528750601
164 0.300293579 0.029183676
165 -0.432281657 0.300293579
166 0.577730336 -0.432281657
167 0.090072120 0.577730336
168 0.209684139 0.090072120
169 -0.283249774 0.209684139
170 0.222331902 -0.283249774
171 -0.250648409 0.222331902
172 0.502803499 -0.250648409
173 0.253366035 0.502803499
174 0.270709959 0.253366035
175 -0.464067586 0.270709959
176 -0.205146078 -0.464067586
177 -0.187694428 -0.205146078
178 0.073006992 -0.187694428
179 0.336161156 0.073006992
180 -0.042453243 0.336161156
181 -0.028938949 -0.042453243
182 -0.013555994 -0.028938949
183 -0.016892260 -0.013555994
184 0.239906592 -0.016892260
185 0.010532631 0.239906592
186 0.517899558 0.010532631
187 0.541504301 0.517899558
188 -0.211264295 0.541504301
189 0.053539322 -0.211264295
190 0.066532195 0.053539322
191 0.072813580 0.066532195
192 -0.052279332 0.072813580
193 -0.279878920 -0.052279332
194 -0.271339484 -0.279878920
195 -0.510986733 -0.271339484
196 -0.246520116 -0.510986733
197 0.263497602 -0.246520116
198 -0.218500868 0.263497602
199 -0.453778090 -0.218500868
200 0.059437938 -0.453778090
201 0.326186516 0.059437938
202 0.361053637 0.326186516
203 0.352388109 0.361053637
204 0.484698087 0.352388109
205 -0.502260419 0.484698087
206 0.009320188 -0.502260419
207 0.260792941 0.009320188
208 -0.223223417 0.260792941
209 0.286500583 -0.223223417
210 0.564582684 0.286500583
211 0.556797916 0.564582684
212 0.567972530 0.556797916
213 -0.168056386 0.567972530
214 -0.157420586 -0.168056386
215 0.110004177 -0.157420586
216 -0.012599829 0.110004177
217 -0.256644774 -0.012599829
218 0.245423722 -0.256644774
219 -0.231604626 0.245423722
220 0.535146919 -0.231604626
221 0.544169428 0.535146919
222 -0.191703141 0.544169428
223 -0.438179879 -0.191703141
224 0.330977361 -0.438179879
225 -0.145585702 0.330977361
226 0.612551103 -0.145585702
227 0.369143996 0.612551103
228 0.239489920 0.369143996
229 0.007235683 0.239489920
230 -0.233129462 0.007235683
231 0.031536784 -0.233129462
232 -0.210924724 0.031536784
233 0.298469187 -0.210924724
234 0.054378774 0.298469187
235 0.066653752 0.054378774
236 0.331603441 0.066653752
237 0.351696787 0.331603441
238 0.104893324 0.351696787
239 -0.125826097 0.104893324
240 -0.503161051 -0.125826097
241 0.513989760 -0.503161051
242 0.266746506 0.513989760
243 -0.479507077 0.266746506
244 0.533515047 -0.479507077
245 -0.202536603 0.533515047
246 0.305629848 -0.202536603
247 -0.437593629 0.305629848
248 -0.442359169 -0.437593629
249 0.085449970 -0.442359169
250 0.346274729 0.085449970
251 -0.132766771 0.346274729
252 -0.014801051 -0.132766771
253 0.241881383 -0.014801051
254 0.257354670 0.241881383
255 -0.487203994 0.257354670
256 -0.478925268 -0.487203994
257 0.038414108 -0.478925268
258 -0.197998067 0.038414108
259 -0.453604447 -0.197998067
260 -0.442179397 -0.453604447
261 -0.427514338 -0.442179397
262 0.329290645 -0.427514338
263 -0.130720512 0.329290645
264 0.223055319 -0.130720512
265 -0.018267479 0.223055319
266 -0.510134035 -0.018267479
267 0.240103950 -0.510134035
268 0.015517074 0.240103950
269 -0.476293741 0.015517074
270 -0.219138508 -0.476293741
271 0.278591187 -0.219138508
272 -0.209067029 0.278591187
273 -0.203569555 -0.209067029
274 -0.460413669 -0.203569555
275 0.055130182 -0.460413669
276 0.172419717 0.055130182
277 0.434916662 0.172419717
278 0.189686361 0.434916662
279 0.204876204 0.189686361
280 -0.275392756 0.204876204
281 -0.028873908 -0.275392756
282 -0.004321786 -0.028873908
283 0.270869965 -0.004321786
284 -0.454785058 0.270869965
285 0.063548747 -0.454785058
286 -0.406615522 0.063548747
287 -0.388818169 -0.406615522
288 0.258554754 -0.388818169
289 0.017595053 0.258554754
290 0.022553712 0.017595053
291 -0.457970420 0.022553712
292 0.560242408 -0.457970420
293 -0.422606781 0.560242408
294 0.328020594 -0.422606781
295 0.095415316 0.328020594
296 0.107601544 0.095415316
297 -0.628733550 0.107601544
298 -0.118637357 -0.628733550
299 0.146658035 -0.118637357
300 -0.228125151 0.146658035
301 0.028840727 -0.228125151
302 -0.457717125 0.028840727
303 0.031232991 -0.457717125
304 -0.213975180 0.031232991
305 0.297403612 -0.213975180
306 -0.205655340 0.297403612
307 0.058103655 -0.205655340
308 0.064835105 0.058103655
309 0.582363836 0.064835105
310 0.336693744 0.582363836
311 -0.159593242 0.336693744
312 -0.041281819 -0.159593242
313 -0.278345874 -0.041281819
314 0.460483671 -0.278345874
315 0.229536548 0.460483671
316 0.245448043 0.229536548
317 -0.489951727 0.245448043
318 0.256978048 -0.489951727
319 -0.225914035 0.256978048
320 -0.450500911 -0.225914035
321 -0.427339113 -0.450500911
322 -0.165973957 -0.427339113
> 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/rcomp/tmp/726451321948814.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8j73y1321948814.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9j4401321948815.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10hua81321948815.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/119gdj1321948815.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/rcomp/tmp/127ibh1321948815.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/rcomp/tmp/13uqvw1321948815.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/rcomp/tmp/14mp2p1321948815.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/rcomp/tmp/15w7vf1321948815.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/rcomp/tmp/16eyex1321948815.tab")
+ }
>
> try(system("convert tmp/1fb161321948814.ps tmp/1fb161321948814.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hcpa1321948814.ps tmp/2hcpa1321948814.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rf541321948814.ps tmp/3rf541321948814.png",intern=TRUE))
character(0)
> try(system("convert tmp/49q4r1321948814.ps tmp/49q4r1321948814.png",intern=TRUE))
character(0)
> try(system("convert tmp/5abfe1321948814.ps tmp/5abfe1321948814.png",intern=TRUE))
character(0)
> try(system("convert tmp/62wt51321948814.ps tmp/62wt51321948814.png",intern=TRUE))
character(0)
> try(system("convert tmp/726451321948814.ps tmp/726451321948814.png",intern=TRUE))
character(0)
> try(system("convert tmp/8j73y1321948814.ps tmp/8j73y1321948814.png",intern=TRUE))
character(0)
> try(system("convert tmp/9j4401321948815.ps tmp/9j4401321948815.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hua81321948815.ps tmp/10hua81321948815.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.656 0.684 13.362