R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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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(1
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+ ,0.00502
+ ,0.00004
+ ,0.00257
+ ,0.00312
+ ,1
+ ,112.239
+ ,126.609
+ ,104.095
+ ,0.00472
+ ,0.00004
+ ,0.00238
+ ,0.0029
+ ,1
+ ,116.15
+ ,131.731
+ ,109.815
+ ,0.00381
+ ,0.00003
+ ,0.00181
+ ,0.00232
+ ,1
+ ,170.368
+ ,268.796
+ ,79.543
+ ,0.00571
+ ,0.00003
+ ,0.00232
+ ,0.00269
+ ,1
+ ,208.083
+ ,253.792
+ ,91.802
+ ,0.00757
+ ,0.00004
+ ,0.00428
+ ,0.00428
+ ,1
+ ,198.458
+ ,219.29
+ ,148.691
+ ,0.00376
+ ,0.00002
+ ,0.00182
+ ,0.00215
+ ,1
+ ,202.805
+ ,231.508
+ ,86.232
+ ,0.0037
+ ,0.00002
+ ,0.00189
+ ,0.00211
+ ,1
+ ,202.544
+ ,241.35
+ ,164.168
+ ,0.00254
+ ,0.00001
+ ,0.001
+ ,0.00133
+ ,1
+ ,223.361
+ ,263.872
+ ,87.638
+ ,0.00352
+ ,0.00002
+ ,0.00169
+ ,0.00188
+ ,1
+ ,169.774
+ ,191.759
+ ,151.451
+ ,0.01568
+ ,0.00009
+ ,0.00863
+ ,0.00946
+ ,1
+ ,183.52
+ ,216.814
+ ,161.34
+ ,0.01466
+ ,0.00008
+ ,0.00849
+ ,0.00819
+ ,1
+ ,188.62
+ ,216.302
+ ,165.982
+ ,0.01719
+ ,0.00009
+ ,0.00996
+ ,0.01027
+ ,1
+ ,202.632
+ ,565.74
+ ,177.258
+ ,0.01627
+ ,0.00008
+ ,0.00919
+ ,0.00963
+ ,1
+ ,186.695
+ ,211.961
+ ,149.442
+ ,0.01872
+ ,0.0001
+ ,0.01075
+ ,0.01154
+ ,1
+ ,192.818
+ ,224.429
+ ,168.793
+ ,0.03107
+ ,0.00016
+ ,0.018
+ ,0.01958
+ ,1
+ ,198.116
+ ,233.099
+ ,174.478
+ ,0.02714
+ ,0.00014
+ ,0.01568
+ ,0.01699
+ ,1
+ ,121.345
+ ,139.644
+ ,98.25
+ ,0.00684
+ ,0.00006
+ ,0.00388
+ ,0.00332
+ ,1
+ ,119.1
+ ,128.442
+ ,88.833
+ ,0.00692
+ ,0.00006
+ ,0.00393
+ ,0.003
+ ,1
+ ,117.87
+ ,127.349
+ ,95.654
+ ,0.00647
+ ,0.00005
+ ,0.00356
+ ,0.003
+ ,1
+ ,122.336
+ ,142.369
+ ,94.794
+ ,0.00727
+ ,0.00006
+ ,0.00415
+ ,0.00339
+ ,1
+ ,117.963
+ ,134.209
+ ,100.757
+ ,0.01813
+ ,0.00015
+ ,0.01117
+ ,0.00718
+ ,1
+ ,126.144
+ ,154.284
+ ,97.543
+ ,0.00975
+ ,0.00008
+ ,0.00593
+ ,0.00454
+ ,1
+ ,127.93
+ ,138.752
+ ,112.173
+ ,0.00605
+ ,0.00005
+ ,0.00321
+ ,0.00318
+ ,1
+ ,114.238
+ ,124.393
+ ,77.022
+ ,0.00581
+ ,0.00005
+ ,0.00299
+ ,0.00316
+ ,1
+ ,115.322
+ ,135.738
+ ,107.802
+ ,0.00619
+ ,0.00005
+ ,0.00352
+ ,0.00329
+ ,1
+ ,114.554
+ ,126.778
+ ,91.121
+ ,0.00651
+ ,0.00006
+ ,0.00366
+ ,0.0034
+ ,1
+ ,112.15
+ ,131.669
+ ,97.527
+ ,0.00519
+ ,0.00005
+ ,0.00291
+ ,0.00284
+ ,1
+ ,102.273
+ ,142.83
+ ,85.902
+ ,0.00907
+ ,0.00009
+ ,0.00493
+ ,0.00461
+ ,0
+ ,236.2
+ ,244.663
+ ,102.137
+ ,0.00277
+ ,0.00001
+ ,0.00154
+ ,0.00153
+ ,0
+ ,237.323
+ ,243.709
+ ,229.256
+ ,0.00303
+ ,0.00001
+ ,0.00173
+ ,0.00159
+ ,0
+ ,260.105
+ ,264.919
+ ,237.303
+ ,0.00339
+ ,0.00001
+ ,0.00205
+ ,0.00186
+ ,0
+ ,197.569
+ ,217.627
+ ,90.794
+ ,0.00803
+ ,0.00004
+ ,0.0049
+ ,0.00448
+ ,0
+ ,240.301
+ ,245.135
+ ,219.783
+ ,0.00517
+ ,0.00002
+ ,0.00316
+ ,0.00283
+ ,0
+ ,244.99
+ ,272.21
+ ,239.17
+ ,0.00451
+ ,0.00002
+ ,0.00279
+ ,0.00237
+ ,0
+ ,112.547
+ ,133.374
+ ,105.715
+ ,0.00355
+ ,0.00003
+ ,0.00166
+ ,0.0019
+ ,0
+ ,110.739
+ ,113.597
+ ,100.139
+ ,0.00356
+ ,0.00003
+ ,0.0017
+ ,0.002
+ ,0
+ ,113.715
+ ,116.443
+ ,96.913
+ ,0.00349
+ ,0.00003
+ ,0.00171
+ ,0.00203
+ ,0
+ ,117.004
+ ,144.466
+ ,99.923
+ ,0.00353
+ ,0.00003
+ ,0.00176
+ ,0.00218
+ ,0
+ ,115.38
+ ,123.109
+ ,108.634
+ ,0.00332
+ ,0.00003
+ ,0.0016
+ ,0.00199
+ ,0
+ ,116.388
+ ,129.038
+ ,108.97
+ ,0.00346
+ ,0.00003
+ ,0.00169
+ ,0.00213
+ ,1
+ ,151.737
+ ,190.204
+ ,129.859
+ ,0.00314
+ ,0.00002
+ ,0.00135
+ ,0.00162
+ ,1
+ ,148.79
+ ,158.359
+ ,138.99
+ ,0.00309
+ ,0.00002
+ ,0.00152
+ ,0.00186
+ ,1
+ ,148.143
+ ,155.982
+ ,135.041
+ ,0.00392
+ ,0.00003
+ ,0.00204
+ ,0.00231
+ ,1
+ ,150.44
+ ,163.441
+ ,144.736
+ ,0.00396
+ ,0.00003
+ ,0.00206
+ ,0.00233
+ ,1
+ ,148.462
+ ,161.078
+ ,141.998
+ ,0.00397
+ ,0.00003
+ ,0.00202
+ ,0.00235
+ ,1
+ ,149.818
+ ,163.417
+ ,144.786
+ ,0.00336
+ ,0.00002
+ ,0.00174
+ ,0.00198
+ ,0
+ ,117.226
+ ,123.925
+ ,106.656
+ ,0.00417
+ ,0.00004
+ ,0.00186
+ ,0.0027
+ ,0
+ ,116.848
+ ,217.552
+ ,99.503
+ ,0.00531
+ ,0.00005
+ ,0.0026
+ ,0.00346
+ ,0
+ ,116.286
+ ,177.291
+ ,96.983
+ ,0.00314
+ ,0.00003
+ ,0.00134
+ ,0.00192
+ ,0
+ ,116.556
+ ,592.03
+ ,86.228
+ ,0.00496
+ ,0.00004
+ ,0.00254
+ ,0.00263
+ ,0
+ ,116.342
+ ,581.289
+ ,94.246
+ ,0.00267
+ ,0.00002
+ ,0.00115
+ ,0.00148
+ ,0
+ ,114.563
+ ,119.167
+ ,86.647
+ ,0.00327
+ ,0.00003
+ ,0.00146
+ ,0.00184
+ ,0
+ ,201.774
+ ,262.707
+ ,78.228
+ ,0.00694
+ ,0.00003
+ ,0.00412
+ ,0.00396
+ ,0
+ ,174.188
+ ,230.978
+ ,94.261
+ ,0.00459
+ ,0.00003
+ ,0.00263
+ ,0.00259
+ ,0
+ ,209.516
+ ,253.017
+ ,89.488
+ ,0.00564
+ ,0.00003
+ ,0.00331
+ ,0.00292
+ ,0
+ ,174.688
+ ,240.005
+ ,74.287
+ ,0.0136
+ ,0.00008
+ ,0.00624
+ ,0.00564
+ ,0
+ ,198.764
+ ,396.961
+ ,74.904
+ ,0.0074
+ ,0.00004
+ ,0.0037
+ ,0.0039
+ ,0
+ ,214.289
+ ,260.277
+ ,77.973
+ ,0.00567
+ ,0.00003
+ ,0.00295
+ ,0.00317)
+ ,dim=c(8
+ ,195)
+ ,dimnames=list(c('status'
+ ,'MDVP:Fo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Flo(Hz)'
+ ,'MDVP:Jitter(%)'
+ ,'MDVP:Jitter(Abs)'
+ ,'MDVP:RAP'
+ ,'MDVP:PPQ')
+ ,1:195))
> y <- array(NA,dim=c(8,195),dimnames=list(c('status','MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ'),1:195))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '8'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '8'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
MDVP:PPQ status MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%)
1 0.00554 1 119.992 157.302 74.997 0.00784
2 0.00696 1 122.400 148.650 113.819 0.00968
3 0.00781 1 116.682 131.111 111.555 0.01050
4 0.00698 1 116.676 137.871 111.366 0.00997
5 0.00908 1 116.014 141.781 110.655 0.01284
6 0.00750 1 120.552 131.162 113.787 0.00968
7 0.00202 1 120.267 137.244 114.820 0.00333
8 0.00182 1 107.332 113.840 104.315 0.00290
9 0.00332 1 95.730 132.068 91.754 0.00551
10 0.00332 1 95.056 120.103 91.226 0.00532
11 0.00330 1 88.333 112.240 84.072 0.00505
12 0.00336 1 91.904 115.871 86.292 0.00540
13 0.00153 1 136.926 159.866 131.276 0.00293
14 0.00208 1 139.173 179.139 76.556 0.00390
15 0.00149 1 152.845 163.305 75.836 0.00294
16 0.00203 1 142.167 217.455 83.159 0.00369
17 0.00292 1 144.188 349.259 82.764 0.00544
18 0.00387 1 168.778 232.181 75.603 0.00718
19 0.00432 1 153.046 175.829 68.623 0.00742
20 0.00399 1 156.405 189.398 142.822 0.00768
21 0.00450 1 153.848 165.738 65.782 0.00840
22 0.00267 1 153.880 172.860 78.128 0.00480
23 0.00247 1 167.930 193.221 79.068 0.00442
24 0.00258 1 173.917 192.735 86.180 0.00476
25 0.00390 1 163.656 200.841 76.779 0.00742
26 0.00375 1 104.400 206.002 77.968 0.00633
27 0.00234 1 171.041 208.313 75.501 0.00455
28 0.00275 1 146.845 208.701 81.737 0.00496
29 0.00176 1 155.358 227.383 80.055 0.00310
30 0.00253 1 162.568 198.346 77.630 0.00502
31 0.00168 0 197.076 206.896 192.055 0.00289
32 0.00138 0 199.228 209.512 192.091 0.00241
33 0.00135 0 198.383 215.203 193.104 0.00212
34 0.00107 0 202.266 211.604 197.079 0.00180
35 0.00106 0 203.184 211.526 196.160 0.00178
36 0.00115 0 201.464 210.565 195.708 0.00198
37 0.00241 1 177.876 192.921 168.013 0.00411
38 0.00218 1 176.170 185.604 163.564 0.00369
39 0.00166 1 180.198 201.249 175.456 0.00284
40 0.00182 1 187.733 202.324 173.015 0.00316
41 0.00175 1 186.163 197.724 177.584 0.00298
42 0.00147 1 184.055 196.537 166.977 0.00258
43 0.00182 0 237.226 247.326 225.227 0.00298
44 0.00173 0 241.404 248.834 232.483 0.00281
45 0.00137 0 243.439 250.912 232.435 0.00210
46 0.00139 0 242.852 255.034 227.911 0.00225
47 0.00148 0 245.510 262.090 231.848 0.00235
48 0.00113 0 252.455 261.487 182.786 0.00185
49 0.00203 0 122.188 128.611 115.765 0.00524
50 0.00155 0 122.964 130.049 114.676 0.00428
51 0.00167 0 124.445 135.069 117.495 0.00431
52 0.00169 0 126.344 134.231 112.773 0.00448
53 0.00166 0 128.001 138.052 122.080 0.00436
54 0.00183 0 129.336 139.867 118.604 0.00490
55 0.00486 1 108.807 134.656 102.874 0.00761
56 0.00539 1 109.860 126.358 104.437 0.00874
57 0.00514 1 110.417 131.067 103.370 0.00784
58 0.00469 1 117.274 129.916 110.402 0.00752
59 0.00493 1 116.879 131.897 108.153 0.00788
60 0.00520 1 114.847 271.314 104.680 0.00867
61 0.00152 0 209.144 237.494 109.379 0.00282
62 0.00151 0 223.365 238.987 98.664 0.00264
63 0.00144 0 222.236 231.345 205.495 0.00266
64 0.00155 0 228.832 234.619 223.634 0.00296
65 0.00113 0 229.401 252.221 221.156 0.00205
66 0.00140 0 228.969 239.541 113.201 0.00238
67 0.00440 1 140.341 159.774 67.021 0.00817
68 0.00463 1 136.969 166.607 66.004 0.00923
69 0.00467 1 143.533 162.215 65.809 0.01101
70 0.00354 1 148.090 162.824 67.343 0.00762
71 0.00419 1 142.729 162.408 65.476 0.00831
72 0.00478 1 136.358 176.595 65.750 0.00971
73 0.00220 1 120.080 139.710 111.208 0.00405
74 0.00329 1 112.014 588.518 107.024 0.00533
75 0.00283 1 110.793 128.101 107.316 0.00494
76 0.00289 1 110.707 122.611 105.007 0.00516
77 0.00289 1 112.876 148.826 106.981 0.00500
78 0.00280 1 110.568 125.394 106.821 0.00462
79 0.00332 1 95.385 102.145 90.264 0.00608
80 0.00576 1 100.770 115.697 85.545 0.01038
81 0.00415 1 96.106 108.664 84.510 0.00694
82 0.00371 1 95.605 107.715 87.549 0.00702
83 0.00348 1 100.960 110.019 95.628 0.00606
84 0.00258 1 98.804 102.305 87.804 0.00432
85 0.00420 1 176.858 205.560 75.344 0.00747
86 0.00244 1 180.978 200.125 155.495 0.00406
87 0.00194 1 178.222 202.450 141.047 0.00321
88 0.00312 1 176.281 227.381 125.610 0.00520
89 0.00254 1 173.898 211.350 74.677 0.00448
90 0.00419 1 179.711 225.930 144.878 0.00709
91 0.00453 1 166.605 206.008 78.032 0.00742
92 0.00227 1 151.955 163.335 147.226 0.00419
93 0.00256 1 148.272 164.989 142.299 0.00459
94 0.00226 1 152.125 161.469 76.596 0.00382
95 0.00196 1 157.821 172.975 68.401 0.00358
96 0.00197 1 157.447 163.267 149.605 0.00369
97 0.00184 1 159.116 168.913 144.811 0.00342
98 0.00623 1 125.036 143.946 116.187 0.01280
99 0.00655 1 125.791 140.557 96.206 0.01378
100 0.00990 1 126.512 141.756 99.770 0.01936
101 0.01522 1 125.641 141.068 116.346 0.03316
102 0.00909 1 128.451 150.449 75.632 0.01551
103 0.01628 1 139.224 586.567 66.157 0.03011
104 0.00136 1 150.258 154.609 75.349 0.00248
105 0.00100 1 154.003 160.267 128.621 0.00183
106 0.00134 1 149.689 160.368 133.608 0.00257
107 0.00092 1 155.078 163.736 144.148 0.00168
108 0.00122 1 151.884 157.765 133.751 0.00258
109 0.00096 1 151.989 157.339 132.857 0.00174
110 0.00389 1 193.030 208.900 80.297 0.00766
111 0.00337 1 200.714 223.982 89.686 0.00621
112 0.00339 1 208.519 220.315 199.020 0.00609
113 0.00485 1 204.664 221.300 189.621 0.00841
114 0.00280 1 210.141 232.706 185.258 0.00534
115 0.00246 1 206.327 226.355 92.020 0.00495
116 0.00385 1 151.872 492.892 69.085 0.00856
117 0.00207 1 158.219 442.557 71.948 0.00476
118 0.00261 1 170.756 450.247 79.032 0.00555
119 0.00194 1 178.285 442.824 82.063 0.00462
120 0.00128 1 217.116 233.481 93.978 0.00404
121 0.00314 1 128.940 479.697 88.251 0.00581
122 0.00221 1 176.824 215.293 83.961 0.00460
123 0.00398 1 138.190 203.522 83.340 0.00704
124 0.00449 1 182.018 197.173 79.187 0.00842
125 0.00395 1 156.239 195.107 79.820 0.00694
126 0.00422 1 145.174 198.109 80.637 0.00733
127 0.00327 1 138.145 197.238 81.114 0.00544
128 0.00351 1 166.888 198.966 79.512 0.00638
129 0.00192 1 119.031 127.533 109.216 0.00440
130 0.00135 1 120.078 126.632 105.667 0.00270
131 0.00238 1 120.289 128.143 100.209 0.00492
132 0.00205 1 120.256 125.306 104.773 0.00407
133 0.00170 1 119.056 125.213 86.795 0.00346
134 0.00171 1 118.747 123.723 109.836 0.00331
135 0.00319 1 106.516 112.777 93.105 0.00589
136 0.00315 1 110.453 127.611 105.554 0.00494
137 0.00283 1 113.400 133.344 107.816 0.00451
138 0.00312 1 113.166 130.270 100.673 0.00502
139 0.00290 1 112.239 126.609 104.095 0.00472
140 0.00232 1 116.150 131.731 109.815 0.00381
141 0.00269 1 170.368 268.796 79.543 0.00571
142 0.00428 1 208.083 253.792 91.802 0.00757
143 0.00215 1 198.458 219.290 148.691 0.00376
144 0.00211 1 202.805 231.508 86.232 0.00370
145 0.00133 1 202.544 241.350 164.168 0.00254
146 0.00188 1 223.361 263.872 87.638 0.00352
147 0.00946 1 169.774 191.759 151.451 0.01568
148 0.00819 1 183.520 216.814 161.340 0.01466
149 0.01027 1 188.620 216.302 165.982 0.01719
150 0.00963 1 202.632 565.740 177.258 0.01627
151 0.01154 1 186.695 211.961 149.442 0.01872
152 0.01958 1 192.818 224.429 168.793 0.03107
153 0.01699 1 198.116 233.099 174.478 0.02714
154 0.00332 1 121.345 139.644 98.250 0.00684
155 0.00300 1 119.100 128.442 88.833 0.00692
156 0.00300 1 117.870 127.349 95.654 0.00647
157 0.00339 1 122.336 142.369 94.794 0.00727
158 0.00718 1 117.963 134.209 100.757 0.01813
159 0.00454 1 126.144 154.284 97.543 0.00975
160 0.00318 1 127.930 138.752 112.173 0.00605
161 0.00316 1 114.238 124.393 77.022 0.00581
162 0.00329 1 115.322 135.738 107.802 0.00619
163 0.00340 1 114.554 126.778 91.121 0.00651
164 0.00284 1 112.150 131.669 97.527 0.00519
165 0.00461 1 102.273 142.830 85.902 0.00907
166 0.00153 0 236.200 244.663 102.137 0.00277
167 0.00159 0 237.323 243.709 229.256 0.00303
168 0.00186 0 260.105 264.919 237.303 0.00339
169 0.00448 0 197.569 217.627 90.794 0.00803
170 0.00283 0 240.301 245.135 219.783 0.00517
171 0.00237 0 244.990 272.210 239.170 0.00451
172 0.00190 0 112.547 133.374 105.715 0.00355
173 0.00200 0 110.739 113.597 100.139 0.00356
174 0.00203 0 113.715 116.443 96.913 0.00349
175 0.00218 0 117.004 144.466 99.923 0.00353
176 0.00199 0 115.380 123.109 108.634 0.00332
177 0.00213 0 116.388 129.038 108.970 0.00346
178 0.00162 1 151.737 190.204 129.859 0.00314
179 0.00186 1 148.790 158.359 138.990 0.00309
180 0.00231 1 148.143 155.982 135.041 0.00392
181 0.00233 1 150.440 163.441 144.736 0.00396
182 0.00235 1 148.462 161.078 141.998 0.00397
183 0.00198 1 149.818 163.417 144.786 0.00336
184 0.00270 0 117.226 123.925 106.656 0.00417
185 0.00346 0 116.848 217.552 99.503 0.00531
186 0.00192 0 116.286 177.291 96.983 0.00314
187 0.00263 0 116.556 592.030 86.228 0.00496
188 0.00148 0 116.342 581.289 94.246 0.00267
189 0.00184 0 114.563 119.167 86.647 0.00327
190 0.00396 0 201.774 262.707 78.228 0.00694
191 0.00259 0 174.188 230.978 94.261 0.00459
192 0.00292 0 209.516 253.017 89.488 0.00564
193 0.00564 0 174.688 240.005 74.287 0.01360
194 0.00390 0 198.764 396.961 74.904 0.00740
195 0.00317 0 214.289 260.277 77.973 0.00567
MDVP:Jitter(Abs) MDVP:RAP
1 7.0e-05 0.00370
2 8.0e-05 0.00465
3 9.0e-05 0.00544
4 9.0e-05 0.00502
5 1.1e-04 0.00655
6 8.0e-05 0.00463
7 3.0e-05 0.00155
8 3.0e-05 0.00144
9 6.0e-05 0.00293
10 6.0e-05 0.00268
11 6.0e-05 0.00254
12 6.0e-05 0.00281
13 2.0e-05 0.00118
14 3.0e-05 0.00165
15 2.0e-05 0.00121
16 3.0e-05 0.00157
17 4.0e-05 0.00211
18 4.0e-05 0.00284
19 5.0e-05 0.00364
20 5.0e-05 0.00372
21 5.0e-05 0.00428
22 3.0e-05 0.00232
23 3.0e-05 0.00220
24 3.0e-05 0.00221
25 5.0e-05 0.00380
26 6.0e-05 0.00316
27 3.0e-05 0.00250
28 3.0e-05 0.00250
29 2.0e-05 0.00159
30 3.0e-05 0.00280
31 1.0e-05 0.00166
32 1.0e-05 0.00134
33 1.0e-05 0.00113
34 9.0e-06 0.00093
35 9.0e-06 0.00094
36 1.0e-05 0.00105
37 2.0e-05 0.00233
38 2.0e-05 0.00205
39 2.0e-05 0.00153
40 2.0e-05 0.00168
41 2.0e-05 0.00165
42 1.0e-05 0.00134
43 1.0e-05 0.00169
44 1.0e-05 0.00157
45 9.0e-06 0.00109
46 9.0e-06 0.00117
47 1.0e-05 0.00127
48 7.0e-06 0.00092
49 4.0e-05 0.00169
50 3.0e-05 0.00124
51 3.0e-05 0.00141
52 4.0e-05 0.00131
53 3.0e-05 0.00137
54 4.0e-05 0.00165
55 7.0e-05 0.00349
56 8.0e-05 0.00398
57 7.0e-05 0.00352
58 6.0e-05 0.00299
59 7.0e-05 0.00334
60 8.0e-05 0.00373
61 1.0e-05 0.00147
62 1.0e-05 0.00154
63 1.0e-05 0.00152
64 1.0e-05 0.00175
65 9.0e-06 0.00114
66 1.0e-05 0.00136
67 6.0e-05 0.00430
68 7.0e-05 0.00507
69 8.0e-05 0.00647
70 5.0e-05 0.00467
71 6.0e-05 0.00469
72 7.0e-05 0.00534
73 3.0e-05 0.00180
74 5.0e-05 0.00268
75 4.0e-05 0.00260
76 5.0e-05 0.00277
77 4.0e-05 0.00270
78 4.0e-05 0.00226
79 6.0e-05 0.00331
80 1.0e-04 0.00622
81 7.0e-05 0.00389
82 7.0e-05 0.00428
83 6.0e-05 0.00351
84 4.0e-05 0.00247
85 4.0e-05 0.00418
86 2.0e-05 0.00220
87 2.0e-05 0.00163
88 3.0e-05 0.00287
89 3.0e-05 0.00237
90 4.0e-05 0.00391
91 4.0e-05 0.00387
92 3.0e-05 0.00224
93 3.0e-05 0.00250
94 3.0e-05 0.00191
95 2.0e-05 0.00196
96 2.0e-05 0.00201
97 2.0e-05 0.00178
98 1.0e-04 0.00743
99 1.1e-04 0.00826
100 1.5e-04 0.01159
101 2.6e-04 0.02144
102 1.2e-04 0.00905
103 2.2e-04 0.01854
104 2.0e-05 0.00105
105 1.0e-05 0.00076
106 2.0e-05 0.00116
107 1.0e-05 0.00068
108 2.0e-05 0.00115
109 1.0e-05 0.00075
110 4.0e-05 0.00450
111 3.0e-05 0.00371
112 3.0e-05 0.00368
113 4.0e-05 0.00502
114 3.0e-05 0.00321
115 2.0e-05 0.00302
116 6.0e-05 0.00404
117 3.0e-05 0.00214
118 3.0e-05 0.00244
119 3.0e-05 0.00157
120 2.0e-05 0.00127
121 5.0e-05 0.00241
122 3.0e-05 0.00209
123 5.0e-05 0.00406
124 5.0e-05 0.00506
125 4.0e-05 0.00403
126 5.0e-05 0.00414
127 4.0e-05 0.00294
128 4.0e-05 0.00368
129 4.0e-05 0.00214
130 2.0e-05 0.00116
131 4.0e-05 0.00269
132 3.0e-05 0.00224
133 3.0e-05 0.00169
134 3.0e-05 0.00168
135 6.0e-05 0.00291
136 4.0e-05 0.00244
137 4.0e-05 0.00219
138 4.0e-05 0.00257
139 4.0e-05 0.00238
140 3.0e-05 0.00181
141 3.0e-05 0.00232
142 4.0e-05 0.00428
143 2.0e-05 0.00182
144 2.0e-05 0.00189
145 1.0e-05 0.00100
146 2.0e-05 0.00169
147 9.0e-05 0.00863
148 8.0e-05 0.00849
149 9.0e-05 0.00996
150 8.0e-05 0.00919
151 1.0e-04 0.01075
152 1.6e-04 0.01800
153 1.4e-04 0.01568
154 6.0e-05 0.00388
155 6.0e-05 0.00393
156 5.0e-05 0.00356
157 6.0e-05 0.00415
158 1.5e-04 0.01117
159 8.0e-05 0.00593
160 5.0e-05 0.00321
161 5.0e-05 0.00299
162 5.0e-05 0.00352
163 6.0e-05 0.00366
164 5.0e-05 0.00291
165 9.0e-05 0.00493
166 1.0e-05 0.00154
167 1.0e-05 0.00173
168 1.0e-05 0.00205
169 4.0e-05 0.00490
170 2.0e-05 0.00316
171 2.0e-05 0.00279
172 3.0e-05 0.00166
173 3.0e-05 0.00170
174 3.0e-05 0.00171
175 3.0e-05 0.00176
176 3.0e-05 0.00160
177 3.0e-05 0.00169
178 2.0e-05 0.00135
179 2.0e-05 0.00152
180 3.0e-05 0.00204
181 3.0e-05 0.00206
182 3.0e-05 0.00202
183 2.0e-05 0.00174
184 4.0e-05 0.00186
185 5.0e-05 0.00260
186 3.0e-05 0.00134
187 4.0e-05 0.00254
188 2.0e-05 0.00115
189 3.0e-05 0.00146
190 3.0e-05 0.00412
191 3.0e-05 0.00263
192 3.0e-05 0.00331
193 8.0e-05 0.00624
194 4.0e-05 0.00370
195 3.0e-05 0.00295
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) status `MDVP:Fo(Hz)` `MDVP:Fhi(Hz)`
-1.305e-04 2.201e-04 -4.523e-06 -2.821e-07
`MDVP:Flo(Hz)` `MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP`
5.043e-06 9.026e-01 -1.658e+01 -3.958e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.0023036 -0.0002231 0.0000137 0.0002383 0.0018899
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.305e-04 3.558e-04 -0.367 0.714212
status 2.201e-04 1.092e-04 2.016 0.045236 *
`MDVP:Fo(Hz)` -4.523e-06 1.998e-06 -2.263 0.024757 *
`MDVP:Fhi(Hz)` -2.821e-07 5.125e-07 -0.550 0.582678
`MDVP:Flo(Hz)` 5.043e-06 1.232e-06 4.092 6.35e-05 ***
`MDVP:Jitter(%)` 9.026e-01 6.806e-02 13.262 < 2e-16 ***
`MDVP:Jitter(Abs)` -1.658e+01 5.479e+00 -3.025 0.002833 **
`MDVP:RAP` -3.958e-01 1.094e-01 -3.618 0.000382 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0005692 on 187 degrees of freedom
Multiple R-squared: 0.959, Adjusted R-squared: 0.9574
F-statistic: 624.3 on 7 and 187 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,] 9.425464e-02 1.885093e-01 9.057454e-01
[2,] 4.346497e-02 8.692993e-02 9.565350e-01
[3,] 1.490966e-02 2.981932e-02 9.850903e-01
[4,] 8.112045e-03 1.622409e-02 9.918880e-01
[5,] 3.859792e-03 7.719585e-03 9.961402e-01
[6,] 1.684101e-02 3.368201e-02 9.831590e-01
[7,] 7.367468e-03 1.473494e-02 9.926325e-01
[8,] 1.099929e-01 2.199857e-01 8.900071e-01
[9,] 7.419463e-02 1.483893e-01 9.258054e-01
[10,] 2.937234e-01 5.874468e-01 7.062766e-01
[11,] 2.251763e-01 4.503526e-01 7.748237e-01
[12,] 1.985648e-01 3.971296e-01 8.014352e-01
[13,] 1.581659e-01 3.163317e-01 8.418341e-01
[14,] 1.158031e-01 2.316062e-01 8.841969e-01
[15,] 9.441937e-02 1.888387e-01 9.055806e-01
[16,] 6.850790e-02 1.370158e-01 9.314921e-01
[17,] 5.792234e-02 1.158447e-01 9.420777e-01
[18,] 7.294268e-02 1.458854e-01 9.270573e-01
[19,] 1.066852e-01 2.133703e-01 8.933148e-01
[20,] 8.000857e-02 1.600171e-01 9.199914e-01
[21,] 5.746910e-02 1.149382e-01 9.425309e-01
[22,] 4.130701e-02 8.261401e-02 9.586930e-01
[23,] 2.855010e-02 5.710019e-02 9.714499e-01
[24,] 2.033025e-02 4.066051e-02 9.796697e-01
[25,] 1.391836e-02 2.783673e-02 9.860816e-01
[26,] 9.352729e-03 1.870546e-02 9.906473e-01
[27,] 6.633598e-03 1.326720e-02 9.933664e-01
[28,] 4.253592e-03 8.507183e-03 9.957464e-01
[29,] 2.844795e-03 5.689590e-03 9.971552e-01
[30,] 1.828440e-03 3.656880e-03 9.981716e-01
[31,] 1.131312e-03 2.262624e-03 9.988687e-01
[32,] 7.756172e-04 1.551234e-03 9.992244e-01
[33,] 4.671993e-04 9.343985e-04 9.995328e-01
[34,] 2.885117e-04 5.770233e-04 9.997115e-01
[35,] 1.816583e-04 3.633166e-04 9.998183e-01
[36,] 1.106926e-04 2.213853e-04 9.998893e-01
[37,] 6.644603e-05 1.328921e-04 9.999336e-01
[38,] 4.266582e-05 8.533164e-05 9.999573e-01
[39,] 9.764320e-04 1.952864e-03 9.990236e-01
[40,] 1.098445e-03 2.196889e-03 9.989016e-01
[41,] 1.046098e-03 2.092196e-03 9.989539e-01
[42,] 1.531758e-03 3.063516e-03 9.984682e-01
[43,] 1.716464e-03 3.432928e-03 9.982835e-01
[44,] 3.405379e-03 6.810758e-03 9.965946e-01
[45,] 3.039223e-03 6.078447e-03 9.969608e-01
[46,] 4.161865e-03 8.323730e-03 9.958381e-01
[47,] 4.208330e-03 8.416659e-03 9.957917e-01
[48,] 3.410704e-03 6.821407e-03 9.965893e-01
[49,] 3.414090e-03 6.828180e-03 9.965859e-01
[50,] 5.876893e-03 1.175379e-02 9.941231e-01
[51,] 6.114638e-03 1.222928e-02 9.938854e-01
[52,] 5.414918e-03 1.082984e-02 9.945851e-01
[53,] 3.928417e-03 7.856834e-03 9.960716e-01
[54,] 3.220952e-03 6.441904e-03 9.967790e-01
[55,] 2.350172e-03 4.700344e-03 9.976498e-01
[56,] 2.036232e-03 4.072464e-03 9.979638e-01
[57,] 4.411346e-03 8.822692e-03 9.955887e-01
[58,] 3.342863e-02 6.685727e-02 9.665714e-01
[59,] 4.862241e-01 9.724482e-01 5.137759e-01
[60,] 4.958263e-01 9.916526e-01 5.041737e-01
[61,] 4.758428e-01 9.516856e-01 5.241572e-01
[62,] 4.829148e-01 9.658296e-01 5.170852e-01
[63,] 4.509029e-01 9.018057e-01 5.490971e-01
[64,] 4.739099e-01 9.478198e-01 5.260901e-01
[65,] 4.403080e-01 8.806159e-01 5.596920e-01
[66,] 4.135192e-01 8.270384e-01 5.864808e-01
[67,] 3.839175e-01 7.678349e-01 6.160825e-01
[68,] 3.527779e-01 7.055557e-01 6.472221e-01
[69,] 3.337799e-01 6.675599e-01 6.662201e-01
[70,] 4.644554e-01 9.289107e-01 5.355446e-01
[71,] 5.024397e-01 9.951206e-01 4.975603e-01
[72,] 4.925998e-01 9.851997e-01 5.074002e-01
[73,] 4.820420e-01 9.640839e-01 5.179580e-01
[74,] 4.731688e-01 9.463376e-01 5.268312e-01
[75,] 4.610071e-01 9.220142e-01 5.389929e-01
[76,] 4.197363e-01 8.394727e-01 5.802637e-01
[77,] 3.877509e-01 7.755017e-01 6.122491e-01
[78,] 3.576075e-01 7.152150e-01 6.423925e-01
[79,] 3.322849e-01 6.645698e-01 6.677151e-01
[80,] 2.991512e-01 5.983025e-01 7.008488e-01
[81,] 3.249795e-01 6.499589e-01 6.750205e-01
[82,] 2.948769e-01 5.897539e-01 7.051231e-01
[83,] 2.607263e-01 5.214527e-01 7.392737e-01
[84,] 2.455075e-01 4.910150e-01 7.544925e-01
[85,] 2.263736e-01 4.527473e-01 7.736264e-01
[86,] 2.058437e-01 4.116875e-01 7.941563e-01
[87,] 1.813315e-01 3.626629e-01 8.186685e-01
[88,] 2.843856e-01 5.687711e-01 7.156144e-01
[89,] 3.817740e-01 7.635481e-01 6.182260e-01
[90,] 3.816159e-01 7.632318e-01 6.183841e-01
[91,] 7.236793e-01 5.526413e-01 2.763207e-01
[92,] 8.757686e-01 2.484629e-01 1.242314e-01
[93,] 9.479948e-01 1.040103e-01 5.200516e-02
[94,] 9.390423e-01 1.219153e-01 6.095766e-02
[95,] 9.281450e-01 1.437099e-01 7.185497e-02
[96,] 9.172104e-01 1.655791e-01 8.278957e-02
[97,] 9.044167e-01 1.911667e-01 9.558334e-02
[98,] 8.970723e-01 2.058554e-01 1.029277e-01
[99,] 8.817535e-01 2.364930e-01 1.182465e-01
[100,] 8.693835e-01 2.612330e-01 1.306165e-01
[101,] 8.485961e-01 3.028078e-01 1.514039e-01
[102,] 8.263786e-01 3.472428e-01 1.736214e-01
[103,] 8.209794e-01 3.580413e-01 1.790206e-01
[104,] 8.062239e-01 3.875522e-01 1.937761e-01
[105,] 7.939156e-01 4.121688e-01 2.060844e-01
[106,] 8.497318e-01 3.005364e-01 1.502682e-01
[107,] 8.502605e-01 2.994790e-01 1.497395e-01
[108,] 8.466004e-01 3.067991e-01 1.533996e-01
[109,] 8.700832e-01 2.598336e-01 1.299168e-01
[110,] 9.324848e-01 1.350304e-01 6.751519e-02
[111,] 9.341848e-01 1.316304e-01 6.581519e-02
[112,] 9.236380e-01 1.527241e-01 7.636203e-02
[113,] 9.116504e-01 1.766991e-01 8.834957e-02
[114,] 8.969148e-01 2.061703e-01 1.030852e-01
[115,] 8.896769e-01 2.206463e-01 1.103231e-01
[116,] 8.759016e-01 2.481968e-01 1.240984e-01
[117,] 8.647117e-01 2.705766e-01 1.352883e-01
[118,] 8.405094e-01 3.189812e-01 1.594906e-01
[119,] 8.395939e-01 3.208121e-01 1.604061e-01
[120,] 8.389630e-01 3.220740e-01 1.610370e-01
[121,] 8.281226e-01 3.437548e-01 1.718774e-01
[122,] 8.365770e-01 3.268460e-01 1.634230e-01
[123,] 8.209113e-01 3.581774e-01 1.790887e-01
[124,] 8.002619e-01 3.994762e-01 1.997381e-01
[125,] 8.227561e-01 3.544877e-01 1.772439e-01
[126,] 8.013719e-01 3.972563e-01 1.986281e-01
[127,] 7.838305e-01 4.323390e-01 2.161695e-01
[128,] 7.557567e-01 4.884866e-01 2.442433e-01
[129,] 7.252057e-01 5.495886e-01 2.747943e-01
[130,] 6.858578e-01 6.282844e-01 3.141422e-01
[131,] 6.910873e-01 6.178254e-01 3.089127e-01
[132,] 6.530308e-01 6.939383e-01 3.469692e-01
[133,] 6.110086e-01 7.779829e-01 3.889914e-01
[134,] 5.761407e-01 8.477185e-01 4.238593e-01
[135,] 5.409041e-01 9.181917e-01 4.590959e-01
[136,] 5.688075e-01 8.623850e-01 4.311925e-01
[137,] 5.957274e-01 8.085452e-01 4.042726e-01
[138,] 6.004282e-01 7.991436e-01 3.995718e-01
[139,] 6.017410e-01 7.965181e-01 3.982590e-01
[140,] 5.823904e-01 8.352192e-01 4.176096e-01
[141,] 5.967849e-01 8.064303e-01 4.032151e-01
[142,] 7.362010e-01 5.275981e-01 2.637990e-01
[143,] 9.999921e-01 1.579687e-05 7.898435e-06
[144,] 9.999848e-01 3.044953e-05 1.522477e-05
[145,] 9.999896e-01 2.071334e-05 1.035667e-05
[146,] 9.999864e-01 2.725978e-05 1.362989e-05
[147,] 9.999790e-01 4.193163e-05 2.096582e-05
[148,] 9.999999e-01 1.523292e-07 7.616461e-08
[149,] 1.000000e+00 2.534516e-08 1.267258e-08
[150,] 1.000000e+00 7.181154e-08 3.590577e-08
[151,] 9.999999e-01 2.000520e-07 1.000260e-07
[152,] 9.999998e-01 4.858193e-07 2.429096e-07
[153,] 9.999996e-01 8.367946e-07 4.183973e-07
[154,] 9.999993e-01 1.425667e-06 7.128337e-07
[155,] 9.999999e-01 1.781348e-07 8.906741e-08
[156,] 9.999999e-01 2.773602e-07 1.386801e-07
[157,] 9.999996e-01 8.687760e-07 4.343880e-07
[158,] 9.999987e-01 2.642938e-06 1.321469e-06
[159,] 9.999967e-01 6.686701e-06 3.343351e-06
[160,] 9.999932e-01 1.352317e-05 6.761585e-06
[161,] 9.999809e-01 3.821079e-05 1.910540e-05
[162,] 9.999565e-01 8.690403e-05 4.345201e-05
[163,] 9.998896e-01 2.208919e-04 1.104459e-04
[164,] 9.997359e-01 5.282792e-04 2.641396e-04
[165,] 9.993315e-01 1.337020e-03 6.685099e-04
[166,] 9.984677e-01 3.064516e-03 1.532258e-03
[167,] 9.963428e-01 7.314440e-03 3.657220e-03
[168,] 9.916489e-01 1.670220e-02 8.351098e-03
[169,] 9.815068e-01 3.698646e-02 1.849323e-02
[170,] 9.609797e-01 7.804055e-02 3.902028e-02
[171,] 9.224499e-01 1.551003e-01 7.755014e-02
[172,] 8.538148e-01 2.923704e-01 1.461852e-01
[173,] 7.410192e-01 5.179617e-01 2.589808e-01
[174,] 6.361457e-01 7.277086e-01 3.638543e-01
> postscript(file="/var/fisher/rcomp/tmp/10uef1386680130.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2t66l1386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3gqoi1386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4t5a11386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5whaa1386680130.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 = 195
Frequency = 1
1 2 3 4 5
1.207412e-03 1.321009e-03 1.889887e-03 1.374882e-03 1.823128e-03
6 7 8 9 10
1.839964e-03 3.899249e-05 1.714509e-04 4.186211e-04 4.874158e-04
11 12 13 14 15
6.591661e-04 5.160890e-04 -4.034154e-04 -8.560891e-05 -8.799748e-05
16 17 18 19 20
1.332708e-05 -2.484471e-04 -4.657658e-04 1.981207e-04 -6.900814e-04
21 22 23 24 25
-2.380375e-04 1.402537e-05 1.740749e-04 -2.777808e-05 -1.446467e-04
26 27 28 29 30
3.291086e-04 8.178094e-05 -1.906493e-05 1.961380e-04 -8.558779e-05
31 32 33 34 35
5.841665e-06 2.273903e-05 1.640598e-04 7.366623e-05 9.444071e-05
36 37 38 39 40
5.825796e-05 -1.241012e-04 -7.316243e-05 -6.908579e-05 -9.186056e-05
41 42 43 44 45
-4.270631e-05 -2.064821e-04 1.021880e-04 1.008682e-04 1.852102e-04
46 47 48 49 50
1.228039e-04 1.728521e-04 3.645874e-04 -1.232179e-03 -1.180121e-03
51 52 53 54 55
-1.026023e-03 -1.001116e-03 -1.103181e-03 -1.119937e-03 4.543326e-04
56 57 58 59 60
3.186086e-04 5.423748e-04 9.251342e-07 2.303746e-04 1.550744e-04
61 62 63 64 65
3.140038e-04 6.129566e-04 -2.905157e-05 -1.595314e-04 3.886672e-06
66 67 68 69 70
6.185863e-04 -2.578973e-05 -2.902543e-04 -1.107638e-03 -3.744013e-04
71 72 73 74 75
-1.884737e-04 -4.653145e-04 -3.138762e-04 4.117983e-04 -3.048227e-05
76 77 78 79 80
7.368823e-05 3.189448e-05 9.450884e-05 5.203758e-05 4.775032e-04
81 82 83 84 85
5.352141e-04 1.594901e-04 3.096254e-04 2.645843e-04 1.631337e-04
86 87 88 89 90
-2.122486e-05 8.146249e-05 1.978846e-04 3.114495e-04 5.723962e-05
91 92 93 94 95
3.557755e-04 -2.269220e-04 -1.864100e-04 3.228911e-04 1.638845e-04
96 97 98 99 100
-3.195788e-04 -2.635808e-04 -7.947428e-04 -7.618302e-04 -4.818319e-04
101 102 103 104 105
-1.983955e-03 8.136539e-04 4.584895e-04 1.221807e-04 -1.817851e-04
106 107 108 109 110
-2.302859e-04 -2.305214e-04 -3.547970e-04 -1.758071e-04 -1.426103e-04
111 112 113 114 115
1.594170e-04 -2.412779e-04 -1.490025e-04 -2.600937e-04 -3.784654e-05
116 117 118 119 120
-8.949839e-04 -4.942329e-04 -5.254146e-04 -6.836279e-04 -1.048116e-03
121 122 123 124 125
-1.377690e-04 -2.701494e-04 2.337231e-04 1.112748e-04 2.133674e-04
126 127 128 129 130
2.873225e-04 3.681338e-04 1.911178e-04 -6.076064e-04 -3.401573e-04
131 132 133 134 135
-3.480074e-04 -2.786098e-04 -2.104734e-04 -1.870608e-04 -2.575322e-05
136 137 138 139 140
2.334071e-04 2.061260e-04 2.202844e-04 1.733879e-04 1.370495e-05
141 142 143 144 145
-6.928404e-04 2.642834e-04 -7.204821e-05 3.079172e-04 -3.345008e-04
146 147 148 149 150
2.562483e-04 1.829030e-04 -3.682377e-04 1.752078e-04 1.968880e-07
151 152 153 154 155
6.161148e-04 1.306364e-03 1.011662e-03 -3.206400e-04 -6.588820e-04
156 157 158 159 160
-6.051696e-04 -5.092245e-04 -2.303622e-03 -5.549985e-04 -2.191828e-04
161 162 163 164 165
1.672501e-06 -1.486950e-04 -2.823894e-05 9.882155e-05 -1.537175e-04
166 167 168 169 170
5.577569e-04 -1.780129e-04 -3.786514e-05 4.619293e-04 -7.624785e-05
171 172 173 174 175
-1.558877e-04 -5.989102e-06 1.151798e-04 2.428524e-04 3.841372e-04
176 177 178 179 180
2.630607e-04 3.168515e-04 -3.529887e-04 -6.894313e-05 1.976601e-05
181 182 183 184 185
-2.482410e-05 -2.548491e-05 -1.287340e-04 4.930577e-04 7.434841e-04
186 187 188 189 190
3.307745e-04 2.111603e-04 2.020702e-04 2.088644e-04 5.464295e-04
191 192 193 194 195
4.932887e-04 3.347470e-04 -2.226152e-03 1.117950e-04 4.969068e-04
> postscript(file="/var/fisher/rcomp/tmp/6dktn1386680130.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 = 195
Frequency = 1
lag(myerror, k = 1) myerror
0 1.207412e-03 NA
1 1.321009e-03 1.207412e-03
2 1.889887e-03 1.321009e-03
3 1.374882e-03 1.889887e-03
4 1.823128e-03 1.374882e-03
5 1.839964e-03 1.823128e-03
6 3.899249e-05 1.839964e-03
7 1.714509e-04 3.899249e-05
8 4.186211e-04 1.714509e-04
9 4.874158e-04 4.186211e-04
10 6.591661e-04 4.874158e-04
11 5.160890e-04 6.591661e-04
12 -4.034154e-04 5.160890e-04
13 -8.560891e-05 -4.034154e-04
14 -8.799748e-05 -8.560891e-05
15 1.332708e-05 -8.799748e-05
16 -2.484471e-04 1.332708e-05
17 -4.657658e-04 -2.484471e-04
18 1.981207e-04 -4.657658e-04
19 -6.900814e-04 1.981207e-04
20 -2.380375e-04 -6.900814e-04
21 1.402537e-05 -2.380375e-04
22 1.740749e-04 1.402537e-05
23 -2.777808e-05 1.740749e-04
24 -1.446467e-04 -2.777808e-05
25 3.291086e-04 -1.446467e-04
26 8.178094e-05 3.291086e-04
27 -1.906493e-05 8.178094e-05
28 1.961380e-04 -1.906493e-05
29 -8.558779e-05 1.961380e-04
30 5.841665e-06 -8.558779e-05
31 2.273903e-05 5.841665e-06
32 1.640598e-04 2.273903e-05
33 7.366623e-05 1.640598e-04
34 9.444071e-05 7.366623e-05
35 5.825796e-05 9.444071e-05
36 -1.241012e-04 5.825796e-05
37 -7.316243e-05 -1.241012e-04
38 -6.908579e-05 -7.316243e-05
39 -9.186056e-05 -6.908579e-05
40 -4.270631e-05 -9.186056e-05
41 -2.064821e-04 -4.270631e-05
42 1.021880e-04 -2.064821e-04
43 1.008682e-04 1.021880e-04
44 1.852102e-04 1.008682e-04
45 1.228039e-04 1.852102e-04
46 1.728521e-04 1.228039e-04
47 3.645874e-04 1.728521e-04
48 -1.232179e-03 3.645874e-04
49 -1.180121e-03 -1.232179e-03
50 -1.026023e-03 -1.180121e-03
51 -1.001116e-03 -1.026023e-03
52 -1.103181e-03 -1.001116e-03
53 -1.119937e-03 -1.103181e-03
54 4.543326e-04 -1.119937e-03
55 3.186086e-04 4.543326e-04
56 5.423748e-04 3.186086e-04
57 9.251342e-07 5.423748e-04
58 2.303746e-04 9.251342e-07
59 1.550744e-04 2.303746e-04
60 3.140038e-04 1.550744e-04
61 6.129566e-04 3.140038e-04
62 -2.905157e-05 6.129566e-04
63 -1.595314e-04 -2.905157e-05
64 3.886672e-06 -1.595314e-04
65 6.185863e-04 3.886672e-06
66 -2.578973e-05 6.185863e-04
67 -2.902543e-04 -2.578973e-05
68 -1.107638e-03 -2.902543e-04
69 -3.744013e-04 -1.107638e-03
70 -1.884737e-04 -3.744013e-04
71 -4.653145e-04 -1.884737e-04
72 -3.138762e-04 -4.653145e-04
73 4.117983e-04 -3.138762e-04
74 -3.048227e-05 4.117983e-04
75 7.368823e-05 -3.048227e-05
76 3.189448e-05 7.368823e-05
77 9.450884e-05 3.189448e-05
78 5.203758e-05 9.450884e-05
79 4.775032e-04 5.203758e-05
80 5.352141e-04 4.775032e-04
81 1.594901e-04 5.352141e-04
82 3.096254e-04 1.594901e-04
83 2.645843e-04 3.096254e-04
84 1.631337e-04 2.645843e-04
85 -2.122486e-05 1.631337e-04
86 8.146249e-05 -2.122486e-05
87 1.978846e-04 8.146249e-05
88 3.114495e-04 1.978846e-04
89 5.723962e-05 3.114495e-04
90 3.557755e-04 5.723962e-05
91 -2.269220e-04 3.557755e-04
92 -1.864100e-04 -2.269220e-04
93 3.228911e-04 -1.864100e-04
94 1.638845e-04 3.228911e-04
95 -3.195788e-04 1.638845e-04
96 -2.635808e-04 -3.195788e-04
97 -7.947428e-04 -2.635808e-04
98 -7.618302e-04 -7.947428e-04
99 -4.818319e-04 -7.618302e-04
100 -1.983955e-03 -4.818319e-04
101 8.136539e-04 -1.983955e-03
102 4.584895e-04 8.136539e-04
103 1.221807e-04 4.584895e-04
104 -1.817851e-04 1.221807e-04
105 -2.302859e-04 -1.817851e-04
106 -2.305214e-04 -2.302859e-04
107 -3.547970e-04 -2.305214e-04
108 -1.758071e-04 -3.547970e-04
109 -1.426103e-04 -1.758071e-04
110 1.594170e-04 -1.426103e-04
111 -2.412779e-04 1.594170e-04
112 -1.490025e-04 -2.412779e-04
113 -2.600937e-04 -1.490025e-04
114 -3.784654e-05 -2.600937e-04
115 -8.949839e-04 -3.784654e-05
116 -4.942329e-04 -8.949839e-04
117 -5.254146e-04 -4.942329e-04
118 -6.836279e-04 -5.254146e-04
119 -1.048116e-03 -6.836279e-04
120 -1.377690e-04 -1.048116e-03
121 -2.701494e-04 -1.377690e-04
122 2.337231e-04 -2.701494e-04
123 1.112748e-04 2.337231e-04
124 2.133674e-04 1.112748e-04
125 2.873225e-04 2.133674e-04
126 3.681338e-04 2.873225e-04
127 1.911178e-04 3.681338e-04
128 -6.076064e-04 1.911178e-04
129 -3.401573e-04 -6.076064e-04
130 -3.480074e-04 -3.401573e-04
131 -2.786098e-04 -3.480074e-04
132 -2.104734e-04 -2.786098e-04
133 -1.870608e-04 -2.104734e-04
134 -2.575322e-05 -1.870608e-04
135 2.334071e-04 -2.575322e-05
136 2.061260e-04 2.334071e-04
137 2.202844e-04 2.061260e-04
138 1.733879e-04 2.202844e-04
139 1.370495e-05 1.733879e-04
140 -6.928404e-04 1.370495e-05
141 2.642834e-04 -6.928404e-04
142 -7.204821e-05 2.642834e-04
143 3.079172e-04 -7.204821e-05
144 -3.345008e-04 3.079172e-04
145 2.562483e-04 -3.345008e-04
146 1.829030e-04 2.562483e-04
147 -3.682377e-04 1.829030e-04
148 1.752078e-04 -3.682377e-04
149 1.968880e-07 1.752078e-04
150 6.161148e-04 1.968880e-07
151 1.306364e-03 6.161148e-04
152 1.011662e-03 1.306364e-03
153 -3.206400e-04 1.011662e-03
154 -6.588820e-04 -3.206400e-04
155 -6.051696e-04 -6.588820e-04
156 -5.092245e-04 -6.051696e-04
157 -2.303622e-03 -5.092245e-04
158 -5.549985e-04 -2.303622e-03
159 -2.191828e-04 -5.549985e-04
160 1.672501e-06 -2.191828e-04
161 -1.486950e-04 1.672501e-06
162 -2.823894e-05 -1.486950e-04
163 9.882155e-05 -2.823894e-05
164 -1.537175e-04 9.882155e-05
165 5.577569e-04 -1.537175e-04
166 -1.780129e-04 5.577569e-04
167 -3.786514e-05 -1.780129e-04
168 4.619293e-04 -3.786514e-05
169 -7.624785e-05 4.619293e-04
170 -1.558877e-04 -7.624785e-05
171 -5.989102e-06 -1.558877e-04
172 1.151798e-04 -5.989102e-06
173 2.428524e-04 1.151798e-04
174 3.841372e-04 2.428524e-04
175 2.630607e-04 3.841372e-04
176 3.168515e-04 2.630607e-04
177 -3.529887e-04 3.168515e-04
178 -6.894313e-05 -3.529887e-04
179 1.976601e-05 -6.894313e-05
180 -2.482410e-05 1.976601e-05
181 -2.548491e-05 -2.482410e-05
182 -1.287340e-04 -2.548491e-05
183 4.930577e-04 -1.287340e-04
184 7.434841e-04 4.930577e-04
185 3.307745e-04 7.434841e-04
186 2.111603e-04 3.307745e-04
187 2.020702e-04 2.111603e-04
188 2.088644e-04 2.020702e-04
189 5.464295e-04 2.088644e-04
190 4.932887e-04 5.464295e-04
191 3.347470e-04 4.932887e-04
192 -2.226152e-03 3.347470e-04
193 1.117950e-04 -2.226152e-03
194 4.969068e-04 1.117950e-04
195 NA 4.969068e-04
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.321009e-03 1.207412e-03
[2,] 1.889887e-03 1.321009e-03
[3,] 1.374882e-03 1.889887e-03
[4,] 1.823128e-03 1.374882e-03
[5,] 1.839964e-03 1.823128e-03
[6,] 3.899249e-05 1.839964e-03
[7,] 1.714509e-04 3.899249e-05
[8,] 4.186211e-04 1.714509e-04
[9,] 4.874158e-04 4.186211e-04
[10,] 6.591661e-04 4.874158e-04
[11,] 5.160890e-04 6.591661e-04
[12,] -4.034154e-04 5.160890e-04
[13,] -8.560891e-05 -4.034154e-04
[14,] -8.799748e-05 -8.560891e-05
[15,] 1.332708e-05 -8.799748e-05
[16,] -2.484471e-04 1.332708e-05
[17,] -4.657658e-04 -2.484471e-04
[18,] 1.981207e-04 -4.657658e-04
[19,] -6.900814e-04 1.981207e-04
[20,] -2.380375e-04 -6.900814e-04
[21,] 1.402537e-05 -2.380375e-04
[22,] 1.740749e-04 1.402537e-05
[23,] -2.777808e-05 1.740749e-04
[24,] -1.446467e-04 -2.777808e-05
[25,] 3.291086e-04 -1.446467e-04
[26,] 8.178094e-05 3.291086e-04
[27,] -1.906493e-05 8.178094e-05
[28,] 1.961380e-04 -1.906493e-05
[29,] -8.558779e-05 1.961380e-04
[30,] 5.841665e-06 -8.558779e-05
[31,] 2.273903e-05 5.841665e-06
[32,] 1.640598e-04 2.273903e-05
[33,] 7.366623e-05 1.640598e-04
[34,] 9.444071e-05 7.366623e-05
[35,] 5.825796e-05 9.444071e-05
[36,] -1.241012e-04 5.825796e-05
[37,] -7.316243e-05 -1.241012e-04
[38,] -6.908579e-05 -7.316243e-05
[39,] -9.186056e-05 -6.908579e-05
[40,] -4.270631e-05 -9.186056e-05
[41,] -2.064821e-04 -4.270631e-05
[42,] 1.021880e-04 -2.064821e-04
[43,] 1.008682e-04 1.021880e-04
[44,] 1.852102e-04 1.008682e-04
[45,] 1.228039e-04 1.852102e-04
[46,] 1.728521e-04 1.228039e-04
[47,] 3.645874e-04 1.728521e-04
[48,] -1.232179e-03 3.645874e-04
[49,] -1.180121e-03 -1.232179e-03
[50,] -1.026023e-03 -1.180121e-03
[51,] -1.001116e-03 -1.026023e-03
[52,] -1.103181e-03 -1.001116e-03
[53,] -1.119937e-03 -1.103181e-03
[54,] 4.543326e-04 -1.119937e-03
[55,] 3.186086e-04 4.543326e-04
[56,] 5.423748e-04 3.186086e-04
[57,] 9.251342e-07 5.423748e-04
[58,] 2.303746e-04 9.251342e-07
[59,] 1.550744e-04 2.303746e-04
[60,] 3.140038e-04 1.550744e-04
[61,] 6.129566e-04 3.140038e-04
[62,] -2.905157e-05 6.129566e-04
[63,] -1.595314e-04 -2.905157e-05
[64,] 3.886672e-06 -1.595314e-04
[65,] 6.185863e-04 3.886672e-06
[66,] -2.578973e-05 6.185863e-04
[67,] -2.902543e-04 -2.578973e-05
[68,] -1.107638e-03 -2.902543e-04
[69,] -3.744013e-04 -1.107638e-03
[70,] -1.884737e-04 -3.744013e-04
[71,] -4.653145e-04 -1.884737e-04
[72,] -3.138762e-04 -4.653145e-04
[73,] 4.117983e-04 -3.138762e-04
[74,] -3.048227e-05 4.117983e-04
[75,] 7.368823e-05 -3.048227e-05
[76,] 3.189448e-05 7.368823e-05
[77,] 9.450884e-05 3.189448e-05
[78,] 5.203758e-05 9.450884e-05
[79,] 4.775032e-04 5.203758e-05
[80,] 5.352141e-04 4.775032e-04
[81,] 1.594901e-04 5.352141e-04
[82,] 3.096254e-04 1.594901e-04
[83,] 2.645843e-04 3.096254e-04
[84,] 1.631337e-04 2.645843e-04
[85,] -2.122486e-05 1.631337e-04
[86,] 8.146249e-05 -2.122486e-05
[87,] 1.978846e-04 8.146249e-05
[88,] 3.114495e-04 1.978846e-04
[89,] 5.723962e-05 3.114495e-04
[90,] 3.557755e-04 5.723962e-05
[91,] -2.269220e-04 3.557755e-04
[92,] -1.864100e-04 -2.269220e-04
[93,] 3.228911e-04 -1.864100e-04
[94,] 1.638845e-04 3.228911e-04
[95,] -3.195788e-04 1.638845e-04
[96,] -2.635808e-04 -3.195788e-04
[97,] -7.947428e-04 -2.635808e-04
[98,] -7.618302e-04 -7.947428e-04
[99,] -4.818319e-04 -7.618302e-04
[100,] -1.983955e-03 -4.818319e-04
[101,] 8.136539e-04 -1.983955e-03
[102,] 4.584895e-04 8.136539e-04
[103,] 1.221807e-04 4.584895e-04
[104,] -1.817851e-04 1.221807e-04
[105,] -2.302859e-04 -1.817851e-04
[106,] -2.305214e-04 -2.302859e-04
[107,] -3.547970e-04 -2.305214e-04
[108,] -1.758071e-04 -3.547970e-04
[109,] -1.426103e-04 -1.758071e-04
[110,] 1.594170e-04 -1.426103e-04
[111,] -2.412779e-04 1.594170e-04
[112,] -1.490025e-04 -2.412779e-04
[113,] -2.600937e-04 -1.490025e-04
[114,] -3.784654e-05 -2.600937e-04
[115,] -8.949839e-04 -3.784654e-05
[116,] -4.942329e-04 -8.949839e-04
[117,] -5.254146e-04 -4.942329e-04
[118,] -6.836279e-04 -5.254146e-04
[119,] -1.048116e-03 -6.836279e-04
[120,] -1.377690e-04 -1.048116e-03
[121,] -2.701494e-04 -1.377690e-04
[122,] 2.337231e-04 -2.701494e-04
[123,] 1.112748e-04 2.337231e-04
[124,] 2.133674e-04 1.112748e-04
[125,] 2.873225e-04 2.133674e-04
[126,] 3.681338e-04 2.873225e-04
[127,] 1.911178e-04 3.681338e-04
[128,] -6.076064e-04 1.911178e-04
[129,] -3.401573e-04 -6.076064e-04
[130,] -3.480074e-04 -3.401573e-04
[131,] -2.786098e-04 -3.480074e-04
[132,] -2.104734e-04 -2.786098e-04
[133,] -1.870608e-04 -2.104734e-04
[134,] -2.575322e-05 -1.870608e-04
[135,] 2.334071e-04 -2.575322e-05
[136,] 2.061260e-04 2.334071e-04
[137,] 2.202844e-04 2.061260e-04
[138,] 1.733879e-04 2.202844e-04
[139,] 1.370495e-05 1.733879e-04
[140,] -6.928404e-04 1.370495e-05
[141,] 2.642834e-04 -6.928404e-04
[142,] -7.204821e-05 2.642834e-04
[143,] 3.079172e-04 -7.204821e-05
[144,] -3.345008e-04 3.079172e-04
[145,] 2.562483e-04 -3.345008e-04
[146,] 1.829030e-04 2.562483e-04
[147,] -3.682377e-04 1.829030e-04
[148,] 1.752078e-04 -3.682377e-04
[149,] 1.968880e-07 1.752078e-04
[150,] 6.161148e-04 1.968880e-07
[151,] 1.306364e-03 6.161148e-04
[152,] 1.011662e-03 1.306364e-03
[153,] -3.206400e-04 1.011662e-03
[154,] -6.588820e-04 -3.206400e-04
[155,] -6.051696e-04 -6.588820e-04
[156,] -5.092245e-04 -6.051696e-04
[157,] -2.303622e-03 -5.092245e-04
[158,] -5.549985e-04 -2.303622e-03
[159,] -2.191828e-04 -5.549985e-04
[160,] 1.672501e-06 -2.191828e-04
[161,] -1.486950e-04 1.672501e-06
[162,] -2.823894e-05 -1.486950e-04
[163,] 9.882155e-05 -2.823894e-05
[164,] -1.537175e-04 9.882155e-05
[165,] 5.577569e-04 -1.537175e-04
[166,] -1.780129e-04 5.577569e-04
[167,] -3.786514e-05 -1.780129e-04
[168,] 4.619293e-04 -3.786514e-05
[169,] -7.624785e-05 4.619293e-04
[170,] -1.558877e-04 -7.624785e-05
[171,] -5.989102e-06 -1.558877e-04
[172,] 1.151798e-04 -5.989102e-06
[173,] 2.428524e-04 1.151798e-04
[174,] 3.841372e-04 2.428524e-04
[175,] 2.630607e-04 3.841372e-04
[176,] 3.168515e-04 2.630607e-04
[177,] -3.529887e-04 3.168515e-04
[178,] -6.894313e-05 -3.529887e-04
[179,] 1.976601e-05 -6.894313e-05
[180,] -2.482410e-05 1.976601e-05
[181,] -2.548491e-05 -2.482410e-05
[182,] -1.287340e-04 -2.548491e-05
[183,] 4.930577e-04 -1.287340e-04
[184,] 7.434841e-04 4.930577e-04
[185,] 3.307745e-04 7.434841e-04
[186,] 2.111603e-04 3.307745e-04
[187,] 2.020702e-04 2.111603e-04
[188,] 2.088644e-04 2.020702e-04
[189,] 5.464295e-04 2.088644e-04
[190,] 4.932887e-04 5.464295e-04
[191,] 3.347470e-04 4.932887e-04
[192,] -2.226152e-03 3.347470e-04
[193,] 1.117950e-04 -2.226152e-03
[194,] 4.969068e-04 1.117950e-04
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.321009e-03 1.207412e-03
2 1.889887e-03 1.321009e-03
3 1.374882e-03 1.889887e-03
4 1.823128e-03 1.374882e-03
5 1.839964e-03 1.823128e-03
6 3.899249e-05 1.839964e-03
7 1.714509e-04 3.899249e-05
8 4.186211e-04 1.714509e-04
9 4.874158e-04 4.186211e-04
10 6.591661e-04 4.874158e-04
11 5.160890e-04 6.591661e-04
12 -4.034154e-04 5.160890e-04
13 -8.560891e-05 -4.034154e-04
14 -8.799748e-05 -8.560891e-05
15 1.332708e-05 -8.799748e-05
16 -2.484471e-04 1.332708e-05
17 -4.657658e-04 -2.484471e-04
18 1.981207e-04 -4.657658e-04
19 -6.900814e-04 1.981207e-04
20 -2.380375e-04 -6.900814e-04
21 1.402537e-05 -2.380375e-04
22 1.740749e-04 1.402537e-05
23 -2.777808e-05 1.740749e-04
24 -1.446467e-04 -2.777808e-05
25 3.291086e-04 -1.446467e-04
26 8.178094e-05 3.291086e-04
27 -1.906493e-05 8.178094e-05
28 1.961380e-04 -1.906493e-05
29 -8.558779e-05 1.961380e-04
30 5.841665e-06 -8.558779e-05
31 2.273903e-05 5.841665e-06
32 1.640598e-04 2.273903e-05
33 7.366623e-05 1.640598e-04
34 9.444071e-05 7.366623e-05
35 5.825796e-05 9.444071e-05
36 -1.241012e-04 5.825796e-05
37 -7.316243e-05 -1.241012e-04
38 -6.908579e-05 -7.316243e-05
39 -9.186056e-05 -6.908579e-05
40 -4.270631e-05 -9.186056e-05
41 -2.064821e-04 -4.270631e-05
42 1.021880e-04 -2.064821e-04
43 1.008682e-04 1.021880e-04
44 1.852102e-04 1.008682e-04
45 1.228039e-04 1.852102e-04
46 1.728521e-04 1.228039e-04
47 3.645874e-04 1.728521e-04
48 -1.232179e-03 3.645874e-04
49 -1.180121e-03 -1.232179e-03
50 -1.026023e-03 -1.180121e-03
51 -1.001116e-03 -1.026023e-03
52 -1.103181e-03 -1.001116e-03
53 -1.119937e-03 -1.103181e-03
54 4.543326e-04 -1.119937e-03
55 3.186086e-04 4.543326e-04
56 5.423748e-04 3.186086e-04
57 9.251342e-07 5.423748e-04
58 2.303746e-04 9.251342e-07
59 1.550744e-04 2.303746e-04
60 3.140038e-04 1.550744e-04
61 6.129566e-04 3.140038e-04
62 -2.905157e-05 6.129566e-04
63 -1.595314e-04 -2.905157e-05
64 3.886672e-06 -1.595314e-04
65 6.185863e-04 3.886672e-06
66 -2.578973e-05 6.185863e-04
67 -2.902543e-04 -2.578973e-05
68 -1.107638e-03 -2.902543e-04
69 -3.744013e-04 -1.107638e-03
70 -1.884737e-04 -3.744013e-04
71 -4.653145e-04 -1.884737e-04
72 -3.138762e-04 -4.653145e-04
73 4.117983e-04 -3.138762e-04
74 -3.048227e-05 4.117983e-04
75 7.368823e-05 -3.048227e-05
76 3.189448e-05 7.368823e-05
77 9.450884e-05 3.189448e-05
78 5.203758e-05 9.450884e-05
79 4.775032e-04 5.203758e-05
80 5.352141e-04 4.775032e-04
81 1.594901e-04 5.352141e-04
82 3.096254e-04 1.594901e-04
83 2.645843e-04 3.096254e-04
84 1.631337e-04 2.645843e-04
85 -2.122486e-05 1.631337e-04
86 8.146249e-05 -2.122486e-05
87 1.978846e-04 8.146249e-05
88 3.114495e-04 1.978846e-04
89 5.723962e-05 3.114495e-04
90 3.557755e-04 5.723962e-05
91 -2.269220e-04 3.557755e-04
92 -1.864100e-04 -2.269220e-04
93 3.228911e-04 -1.864100e-04
94 1.638845e-04 3.228911e-04
95 -3.195788e-04 1.638845e-04
96 -2.635808e-04 -3.195788e-04
97 -7.947428e-04 -2.635808e-04
98 -7.618302e-04 -7.947428e-04
99 -4.818319e-04 -7.618302e-04
100 -1.983955e-03 -4.818319e-04
101 8.136539e-04 -1.983955e-03
102 4.584895e-04 8.136539e-04
103 1.221807e-04 4.584895e-04
104 -1.817851e-04 1.221807e-04
105 -2.302859e-04 -1.817851e-04
106 -2.305214e-04 -2.302859e-04
107 -3.547970e-04 -2.305214e-04
108 -1.758071e-04 -3.547970e-04
109 -1.426103e-04 -1.758071e-04
110 1.594170e-04 -1.426103e-04
111 -2.412779e-04 1.594170e-04
112 -1.490025e-04 -2.412779e-04
113 -2.600937e-04 -1.490025e-04
114 -3.784654e-05 -2.600937e-04
115 -8.949839e-04 -3.784654e-05
116 -4.942329e-04 -8.949839e-04
117 -5.254146e-04 -4.942329e-04
118 -6.836279e-04 -5.254146e-04
119 -1.048116e-03 -6.836279e-04
120 -1.377690e-04 -1.048116e-03
121 -2.701494e-04 -1.377690e-04
122 2.337231e-04 -2.701494e-04
123 1.112748e-04 2.337231e-04
124 2.133674e-04 1.112748e-04
125 2.873225e-04 2.133674e-04
126 3.681338e-04 2.873225e-04
127 1.911178e-04 3.681338e-04
128 -6.076064e-04 1.911178e-04
129 -3.401573e-04 -6.076064e-04
130 -3.480074e-04 -3.401573e-04
131 -2.786098e-04 -3.480074e-04
132 -2.104734e-04 -2.786098e-04
133 -1.870608e-04 -2.104734e-04
134 -2.575322e-05 -1.870608e-04
135 2.334071e-04 -2.575322e-05
136 2.061260e-04 2.334071e-04
137 2.202844e-04 2.061260e-04
138 1.733879e-04 2.202844e-04
139 1.370495e-05 1.733879e-04
140 -6.928404e-04 1.370495e-05
141 2.642834e-04 -6.928404e-04
142 -7.204821e-05 2.642834e-04
143 3.079172e-04 -7.204821e-05
144 -3.345008e-04 3.079172e-04
145 2.562483e-04 -3.345008e-04
146 1.829030e-04 2.562483e-04
147 -3.682377e-04 1.829030e-04
148 1.752078e-04 -3.682377e-04
149 1.968880e-07 1.752078e-04
150 6.161148e-04 1.968880e-07
151 1.306364e-03 6.161148e-04
152 1.011662e-03 1.306364e-03
153 -3.206400e-04 1.011662e-03
154 -6.588820e-04 -3.206400e-04
155 -6.051696e-04 -6.588820e-04
156 -5.092245e-04 -6.051696e-04
157 -2.303622e-03 -5.092245e-04
158 -5.549985e-04 -2.303622e-03
159 -2.191828e-04 -5.549985e-04
160 1.672501e-06 -2.191828e-04
161 -1.486950e-04 1.672501e-06
162 -2.823894e-05 -1.486950e-04
163 9.882155e-05 -2.823894e-05
164 -1.537175e-04 9.882155e-05
165 5.577569e-04 -1.537175e-04
166 -1.780129e-04 5.577569e-04
167 -3.786514e-05 -1.780129e-04
168 4.619293e-04 -3.786514e-05
169 -7.624785e-05 4.619293e-04
170 -1.558877e-04 -7.624785e-05
171 -5.989102e-06 -1.558877e-04
172 1.151798e-04 -5.989102e-06
173 2.428524e-04 1.151798e-04
174 3.841372e-04 2.428524e-04
175 2.630607e-04 3.841372e-04
176 3.168515e-04 2.630607e-04
177 -3.529887e-04 3.168515e-04
178 -6.894313e-05 -3.529887e-04
179 1.976601e-05 -6.894313e-05
180 -2.482410e-05 1.976601e-05
181 -2.548491e-05 -2.482410e-05
182 -1.287340e-04 -2.548491e-05
183 4.930577e-04 -1.287340e-04
184 7.434841e-04 4.930577e-04
185 3.307745e-04 7.434841e-04
186 2.111603e-04 3.307745e-04
187 2.020702e-04 2.111603e-04
188 2.088644e-04 2.020702e-04
189 5.464295e-04 2.088644e-04
190 4.932887e-04 5.464295e-04
191 3.347470e-04 4.932887e-04
192 -2.226152e-03 3.347470e-04
193 1.117950e-04 -2.226152e-03
194 4.969068e-04 1.117950e-04
> 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/fisher/rcomp/tmp/7hqst1386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8oniv1386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9imu51386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10qtau1386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/fisher/rcomp/tmp/119vcu1386680130.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/129nea1386680130.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13qqc51386680130.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14tt341386680130.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15y60b1386680131.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/fisher/rcomp/tmp/16nv7f1386680131.tab")
+ }
>
> try(system("convert tmp/10uef1386680130.ps tmp/10uef1386680130.png",intern=TRUE))
character(0)
> try(system("convert tmp/2t66l1386680130.ps tmp/2t66l1386680130.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gqoi1386680130.ps tmp/3gqoi1386680130.png",intern=TRUE))
character(0)
> try(system("convert tmp/4t5a11386680130.ps tmp/4t5a11386680130.png",intern=TRUE))
character(0)
> try(system("convert tmp/5whaa1386680130.ps tmp/5whaa1386680130.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dktn1386680130.ps tmp/6dktn1386680130.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hqst1386680130.ps tmp/7hqst1386680130.png",intern=TRUE))
character(0)
> try(system("convert tmp/8oniv1386680130.ps tmp/8oniv1386680130.png",intern=TRUE))
character(0)
> try(system("convert tmp/9imu51386680130.ps tmp/9imu51386680130.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qtau1386680130.ps tmp/10qtau1386680130.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
20.689 4.216 24.870