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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+ ,130.27
+ ,100.673
+ ,0.00502
+ ,0.00257
+ ,0.00312
+ ,0.05279
+ ,1
+ ,112.239
+ ,126.609
+ ,104.095
+ ,0.00472
+ ,0.00238
+ ,0.0029
+ ,0.05643
+ ,1
+ ,116.15
+ ,131.731
+ ,109.815
+ ,0.00381
+ ,0.00181
+ ,0.00232
+ ,0.03026
+ ,1
+ ,170.368
+ ,268.796
+ ,79.543
+ ,0.00571
+ ,0.00232
+ ,0.00269
+ ,0.03273
+ ,1
+ ,208.083
+ ,253.792
+ ,91.802
+ ,0.00757
+ ,0.00428
+ ,0.00428
+ ,0.06725
+ ,1
+ ,198.458
+ ,219.29
+ ,148.691
+ ,0.00376
+ ,0.00182
+ ,0.00215
+ ,0.03527
+ ,1
+ ,202.805
+ ,231.508
+ ,86.232
+ ,0.0037
+ ,0.00189
+ ,0.00211
+ ,0.01997
+ ,1
+ ,202.544
+ ,241.35
+ ,164.168
+ ,0.00254
+ ,0.001
+ ,0.00133
+ ,0.02662
+ ,1
+ ,223.361
+ ,263.872
+ ,87.638
+ ,0.00352
+ ,0.00169
+ ,0.00188
+ ,0.02536
+ ,1
+ ,169.774
+ ,191.759
+ ,151.451
+ ,0.01568
+ ,0.00863
+ ,0.00946
+ ,0.08143
+ ,1
+ ,183.52
+ ,216.814
+ ,161.34
+ ,0.01466
+ ,0.00849
+ ,0.00819
+ ,0.0605
+ ,1
+ ,188.62
+ ,216.302
+ ,165.982
+ ,0.01719
+ ,0.00996
+ ,0.01027
+ ,0.07118
+ ,1
+ ,202.632
+ ,565.74
+ ,177.258
+ ,0.01627
+ ,0.00919
+ ,0.00963
+ ,0.0717
+ ,1
+ ,186.695
+ ,211.961
+ ,149.442
+ ,0.01872
+ ,0.01075
+ ,0.01154
+ ,0.0583
+ ,1
+ ,192.818
+ ,224.429
+ ,168.793
+ ,0.03107
+ ,0.018
+ ,0.01958
+ ,0.11908
+ ,1
+ ,198.116
+ ,233.099
+ ,174.478
+ ,0.02714
+ ,0.01568
+ ,0.01699
+ ,0.08684
+ ,1
+ ,121.345
+ ,139.644
+ ,98.25
+ ,0.00684
+ ,0.00388
+ ,0.00332
+ ,0.02534
+ ,1
+ ,119.1
+ ,128.442
+ ,88.833
+ ,0.00692
+ ,0.00393
+ ,0.003
+ ,0.02682
+ ,1
+ ,117.87
+ ,127.349
+ ,95.654
+ ,0.00647
+ ,0.00356
+ ,0.003
+ ,0.03087
+ ,1
+ ,122.336
+ ,142.369
+ ,94.794
+ ,0.00727
+ ,0.00415
+ ,0.00339
+ ,0.02293
+ ,1
+ ,117.963
+ ,134.209
+ ,100.757
+ ,0.01813
+ ,0.01117
+ ,0.00718
+ ,0.04912
+ ,1
+ ,126.144
+ ,154.284
+ ,97.543
+ ,0.00975
+ ,0.00593
+ ,0.00454
+ ,0.02852
+ ,1
+ ,127.93
+ ,138.752
+ ,112.173
+ ,0.00605
+ ,0.00321
+ ,0.00318
+ ,0.03235
+ ,1
+ ,114.238
+ ,124.393
+ ,77.022
+ ,0.00581
+ ,0.00299
+ ,0.00316
+ ,0.04009
+ ,1
+ ,115.322
+ ,135.738
+ ,107.802
+ ,0.00619
+ ,0.00352
+ ,0.00329
+ ,0.03273
+ ,1
+ ,114.554
+ ,126.778
+ ,91.121
+ ,0.00651
+ ,0.00366
+ ,0.0034
+ ,0.03658
+ ,1
+ ,112.15
+ ,131.669
+ ,97.527
+ ,0.00519
+ ,0.00291
+ ,0.00284
+ ,0.01756
+ ,1
+ ,102.273
+ ,142.83
+ ,85.902
+ ,0.00907
+ ,0.00493
+ ,0.00461
+ ,0.02814
+ ,0
+ ,236.2
+ ,244.663
+ ,102.137
+ ,0.00277
+ ,0.00154
+ ,0.00153
+ ,0.02448
+ ,0
+ ,237.323
+ ,243.709
+ ,229.256
+ ,0.00303
+ ,0.00173
+ ,0.00159
+ ,0.01242
+ ,0
+ ,260.105
+ ,264.919
+ ,237.303
+ ,0.00339
+ ,0.00205
+ ,0.00186
+ ,0.0203
+ ,0
+ ,197.569
+ ,217.627
+ ,90.794
+ ,0.00803
+ ,0.0049
+ ,0.00448
+ ,0.02177
+ ,0
+ ,240.301
+ ,245.135
+ ,219.783
+ ,0.00517
+ ,0.00316
+ ,0.00283
+ ,0.02018
+ ,0
+ ,244.99
+ ,272.21
+ ,239.17
+ ,0.00451
+ ,0.00279
+ ,0.00237
+ ,0.01897
+ ,0
+ ,112.547
+ ,133.374
+ ,105.715
+ ,0.00355
+ ,0.00166
+ ,0.0019
+ ,0.01358
+ ,0
+ ,110.739
+ ,113.597
+ ,100.139
+ ,0.00356
+ ,0.0017
+ ,0.002
+ ,0.01484
+ ,0
+ ,113.715
+ ,116.443
+ ,96.913
+ ,0.00349
+ ,0.00171
+ ,0.00203
+ ,0.01472
+ ,0
+ ,117.004
+ ,144.466
+ ,99.923
+ ,0.00353
+ ,0.00176
+ ,0.00218
+ ,0.01657
+ ,0
+ ,115.38
+ ,123.109
+ ,108.634
+ ,0.00332
+ ,0.0016
+ ,0.00199
+ ,0.01503
+ ,0
+ ,116.388
+ ,129.038
+ ,108.97
+ ,0.00346
+ ,0.00169
+ ,0.00213
+ ,0.01725
+ ,1
+ ,151.737
+ ,190.204
+ ,129.859
+ ,0.00314
+ ,0.00135
+ ,0.00162
+ ,0.01469
+ ,1
+ ,148.79
+ ,158.359
+ ,138.99
+ ,0.00309
+ ,0.00152
+ ,0.00186
+ ,0.01574
+ ,1
+ ,148.143
+ ,155.982
+ ,135.041
+ ,0.00392
+ ,0.00204
+ ,0.00231
+ ,0.0145
+ ,1
+ ,150.44
+ ,163.441
+ ,144.736
+ ,0.00396
+ ,0.00206
+ ,0.00233
+ ,0.02551
+ ,1
+ ,148.462
+ ,161.078
+ ,141.998
+ ,0.00397
+ ,0.00202
+ ,0.00235
+ ,0.01831
+ ,1
+ ,149.818
+ ,163.417
+ ,144.786
+ ,0.00336
+ ,0.00174
+ ,0.00198
+ ,0.02145
+ ,0
+ ,117.226
+ ,123.925
+ ,106.656
+ ,0.00417
+ ,0.00186
+ ,0.0027
+ ,0.01909
+ ,0
+ ,116.848
+ ,217.552
+ ,99.503
+ ,0.00531
+ ,0.0026
+ ,0.00346
+ ,0.01795
+ ,0
+ ,116.286
+ ,177.291
+ ,96.983
+ ,0.00314
+ ,0.00134
+ ,0.00192
+ ,0.01564
+ ,0
+ ,116.556
+ ,592.03
+ ,86.228
+ ,0.00496
+ ,0.00254
+ ,0.00263
+ ,0.0166
+ ,0
+ ,116.342
+ ,581.289
+ ,94.246
+ ,0.00267
+ ,0.00115
+ ,0.00148
+ ,0.013
+ ,0
+ ,114.563
+ ,119.167
+ ,86.647
+ ,0.00327
+ ,0.00146
+ ,0.00184
+ ,0.01185
+ ,0
+ ,201.774
+ ,262.707
+ ,78.228
+ ,0.00694
+ ,0.00412
+ ,0.00396
+ ,0.02574
+ ,0
+ ,174.188
+ ,230.978
+ ,94.261
+ ,0.00459
+ ,0.00263
+ ,0.00259
+ ,0.04087
+ ,0
+ ,209.516
+ ,253.017
+ ,89.488
+ ,0.00564
+ ,0.00331
+ ,0.00292
+ ,0.02751
+ ,0
+ ,174.688
+ ,240.005
+ ,74.287
+ ,0.0136
+ ,0.00624
+ ,0.00564
+ ,0.02308
+ ,0
+ ,198.764
+ ,396.961
+ ,74.904
+ ,0.0074
+ ,0.0037
+ ,0.0039
+ ,0.02296
+ ,0
+ ,214.289
+ ,260.277
+ ,77.973
+ ,0.00567
+ ,0.00295
+ ,0.00317
+ ,0.01884)
+ ,dim=c(8
+ ,195)
+ ,dimnames=list(c('status'
+ ,'MDVP:Fo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Flo(Hz)'
+ ,'MDVP:Jitter(%)'
+ ,'MDVP:RAP'
+ ,'MDVP:PPQ'
+ ,'MDVP:Shimmer')
+ ,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:RAP','MDVP:PPQ','MDVP:Shimmer'),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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'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
status MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) MDVP:RAP
1 1 119.992 157.302 74.997 0.00784 0.00370
2 1 122.400 148.650 113.819 0.00968 0.00465
3 1 116.682 131.111 111.555 0.01050 0.00544
4 1 116.676 137.871 111.366 0.00997 0.00502
5 1 116.014 141.781 110.655 0.01284 0.00655
6 1 120.552 131.162 113.787 0.00968 0.00463
7 1 120.267 137.244 114.820 0.00333 0.00155
8 1 107.332 113.840 104.315 0.00290 0.00144
9 1 95.730 132.068 91.754 0.00551 0.00293
10 1 95.056 120.103 91.226 0.00532 0.00268
11 1 88.333 112.240 84.072 0.00505 0.00254
12 1 91.904 115.871 86.292 0.00540 0.00281
13 1 136.926 159.866 131.276 0.00293 0.00118
14 1 139.173 179.139 76.556 0.00390 0.00165
15 1 152.845 163.305 75.836 0.00294 0.00121
16 1 142.167 217.455 83.159 0.00369 0.00157
17 1 144.188 349.259 82.764 0.00544 0.00211
18 1 168.778 232.181 75.603 0.00718 0.00284
19 1 153.046 175.829 68.623 0.00742 0.00364
20 1 156.405 189.398 142.822 0.00768 0.00372
21 1 153.848 165.738 65.782 0.00840 0.00428
22 1 153.880 172.860 78.128 0.00480 0.00232
23 1 167.930 193.221 79.068 0.00442 0.00220
24 1 173.917 192.735 86.180 0.00476 0.00221
25 1 163.656 200.841 76.779 0.00742 0.00380
26 1 104.400 206.002 77.968 0.00633 0.00316
27 1 171.041 208.313 75.501 0.00455 0.00250
28 1 146.845 208.701 81.737 0.00496 0.00250
29 1 155.358 227.383 80.055 0.00310 0.00159
30 1 162.568 198.346 77.630 0.00502 0.00280
31 0 197.076 206.896 192.055 0.00289 0.00166
32 0 199.228 209.512 192.091 0.00241 0.00134
33 0 198.383 215.203 193.104 0.00212 0.00113
34 0 202.266 211.604 197.079 0.00180 0.00093
35 0 203.184 211.526 196.160 0.00178 0.00094
36 0 201.464 210.565 195.708 0.00198 0.00105
37 1 177.876 192.921 168.013 0.00411 0.00233
38 1 176.170 185.604 163.564 0.00369 0.00205
39 1 180.198 201.249 175.456 0.00284 0.00153
40 1 187.733 202.324 173.015 0.00316 0.00168
41 1 186.163 197.724 177.584 0.00298 0.00165
42 1 184.055 196.537 166.977 0.00258 0.00134
43 0 237.226 247.326 225.227 0.00298 0.00169
44 0 241.404 248.834 232.483 0.00281 0.00157
45 0 243.439 250.912 232.435 0.00210 0.00109
46 0 242.852 255.034 227.911 0.00225 0.00117
47 0 245.510 262.090 231.848 0.00235 0.00127
48 0 252.455 261.487 182.786 0.00185 0.00092
49 0 122.188 128.611 115.765 0.00524 0.00169
50 0 122.964 130.049 114.676 0.00428 0.00124
51 0 124.445 135.069 117.495 0.00431 0.00141
52 0 126.344 134.231 112.773 0.00448 0.00131
53 0 128.001 138.052 122.080 0.00436 0.00137
54 0 129.336 139.867 118.604 0.00490 0.00165
55 1 108.807 134.656 102.874 0.00761 0.00349
56 1 109.860 126.358 104.437 0.00874 0.00398
57 1 110.417 131.067 103.370 0.00784 0.00352
58 1 117.274 129.916 110.402 0.00752 0.00299
59 1 116.879 131.897 108.153 0.00788 0.00334
60 1 114.847 271.314 104.680 0.00867 0.00373
61 0 209.144 237.494 109.379 0.00282 0.00147
62 0 223.365 238.987 98.664 0.00264 0.00154
63 0 222.236 231.345 205.495 0.00266 0.00152
64 0 228.832 234.619 223.634 0.00296 0.00175
65 0 229.401 252.221 221.156 0.00205 0.00114
66 0 228.969 239.541 113.201 0.00238 0.00136
67 1 140.341 159.774 67.021 0.00817 0.00430
68 1 136.969 166.607 66.004 0.00923 0.00507
69 1 143.533 162.215 65.809 0.01101 0.00647
70 1 148.090 162.824 67.343 0.00762 0.00467
71 1 142.729 162.408 65.476 0.00831 0.00469
72 1 136.358 176.595 65.750 0.00971 0.00534
73 1 120.080 139.710 111.208 0.00405 0.00180
74 1 112.014 588.518 107.024 0.00533 0.00268
75 1 110.793 128.101 107.316 0.00494 0.00260
76 1 110.707 122.611 105.007 0.00516 0.00277
77 1 112.876 148.826 106.981 0.00500 0.00270
78 1 110.568 125.394 106.821 0.00462 0.00226
79 1 95.385 102.145 90.264 0.00608 0.00331
80 1 100.770 115.697 85.545 0.01038 0.00622
81 1 96.106 108.664 84.510 0.00694 0.00389
82 1 95.605 107.715 87.549 0.00702 0.00428
83 1 100.960 110.019 95.628 0.00606 0.00351
84 1 98.804 102.305 87.804 0.00432 0.00247
85 1 176.858 205.560 75.344 0.00747 0.00418
86 1 180.978 200.125 155.495 0.00406 0.00220
87 1 178.222 202.450 141.047 0.00321 0.00163
88 1 176.281 227.381 125.610 0.00520 0.00287
89 1 173.898 211.350 74.677 0.00448 0.00237
90 1 179.711 225.930 144.878 0.00709 0.00391
91 1 166.605 206.008 78.032 0.00742 0.00387
92 1 151.955 163.335 147.226 0.00419 0.00224
93 1 148.272 164.989 142.299 0.00459 0.00250
94 1 152.125 161.469 76.596 0.00382 0.00191
95 1 157.821 172.975 68.401 0.00358 0.00196
96 1 157.447 163.267 149.605 0.00369 0.00201
97 1 159.116 168.913 144.811 0.00342 0.00178
98 1 125.036 143.946 116.187 0.01280 0.00743
99 1 125.791 140.557 96.206 0.01378 0.00826
100 1 126.512 141.756 99.770 0.01936 0.01159
101 1 125.641 141.068 116.346 0.03316 0.02144
102 1 128.451 150.449 75.632 0.01551 0.00905
103 1 139.224 586.567 66.157 0.03011 0.01854
104 1 150.258 154.609 75.349 0.00248 0.00105
105 1 154.003 160.267 128.621 0.00183 0.00076
106 1 149.689 160.368 133.608 0.00257 0.00116
107 1 155.078 163.736 144.148 0.00168 0.00068
108 1 151.884 157.765 133.751 0.00258 0.00115
109 1 151.989 157.339 132.857 0.00174 0.00075
110 1 193.030 208.900 80.297 0.00766 0.00450
111 1 200.714 223.982 89.686 0.00621 0.00371
112 1 208.519 220.315 199.020 0.00609 0.00368
113 1 204.664 221.300 189.621 0.00841 0.00502
114 1 210.141 232.706 185.258 0.00534 0.00321
115 1 206.327 226.355 92.020 0.00495 0.00302
116 1 151.872 492.892 69.085 0.00856 0.00404
117 1 158.219 442.557 71.948 0.00476 0.00214
118 1 170.756 450.247 79.032 0.00555 0.00244
119 1 178.285 442.824 82.063 0.00462 0.00157
120 1 217.116 233.481 93.978 0.00404 0.00127
121 1 128.940 479.697 88.251 0.00581 0.00241
122 1 176.824 215.293 83.961 0.00460 0.00209
123 1 138.190 203.522 83.340 0.00704 0.00406
124 1 182.018 197.173 79.187 0.00842 0.00506
125 1 156.239 195.107 79.820 0.00694 0.00403
126 1 145.174 198.109 80.637 0.00733 0.00414
127 1 138.145 197.238 81.114 0.00544 0.00294
128 1 166.888 198.966 79.512 0.00638 0.00368
129 1 119.031 127.533 109.216 0.00440 0.00214
130 1 120.078 126.632 105.667 0.00270 0.00116
131 1 120.289 128.143 100.209 0.00492 0.00269
132 1 120.256 125.306 104.773 0.00407 0.00224
133 1 119.056 125.213 86.795 0.00346 0.00169
134 1 118.747 123.723 109.836 0.00331 0.00168
135 1 106.516 112.777 93.105 0.00589 0.00291
136 1 110.453 127.611 105.554 0.00494 0.00244
137 1 113.400 133.344 107.816 0.00451 0.00219
138 1 113.166 130.270 100.673 0.00502 0.00257
139 1 112.239 126.609 104.095 0.00472 0.00238
140 1 116.150 131.731 109.815 0.00381 0.00181
141 1 170.368 268.796 79.543 0.00571 0.00232
142 1 208.083 253.792 91.802 0.00757 0.00428
143 1 198.458 219.290 148.691 0.00376 0.00182
144 1 202.805 231.508 86.232 0.00370 0.00189
145 1 202.544 241.350 164.168 0.00254 0.00100
146 1 223.361 263.872 87.638 0.00352 0.00169
147 1 169.774 191.759 151.451 0.01568 0.00863
148 1 183.520 216.814 161.340 0.01466 0.00849
149 1 188.620 216.302 165.982 0.01719 0.00996
150 1 202.632 565.740 177.258 0.01627 0.00919
151 1 186.695 211.961 149.442 0.01872 0.01075
152 1 192.818 224.429 168.793 0.03107 0.01800
153 1 198.116 233.099 174.478 0.02714 0.01568
154 1 121.345 139.644 98.250 0.00684 0.00388
155 1 119.100 128.442 88.833 0.00692 0.00393
156 1 117.870 127.349 95.654 0.00647 0.00356
157 1 122.336 142.369 94.794 0.00727 0.00415
158 1 117.963 134.209 100.757 0.01813 0.01117
159 1 126.144 154.284 97.543 0.00975 0.00593
160 1 127.930 138.752 112.173 0.00605 0.00321
161 1 114.238 124.393 77.022 0.00581 0.00299
162 1 115.322 135.738 107.802 0.00619 0.00352
163 1 114.554 126.778 91.121 0.00651 0.00366
164 1 112.150 131.669 97.527 0.00519 0.00291
165 1 102.273 142.830 85.902 0.00907 0.00493
166 0 236.200 244.663 102.137 0.00277 0.00154
167 0 237.323 243.709 229.256 0.00303 0.00173
168 0 260.105 264.919 237.303 0.00339 0.00205
169 0 197.569 217.627 90.794 0.00803 0.00490
170 0 240.301 245.135 219.783 0.00517 0.00316
171 0 244.990 272.210 239.170 0.00451 0.00279
172 0 112.547 133.374 105.715 0.00355 0.00166
173 0 110.739 113.597 100.139 0.00356 0.00170
174 0 113.715 116.443 96.913 0.00349 0.00171
175 0 117.004 144.466 99.923 0.00353 0.00176
176 0 115.380 123.109 108.634 0.00332 0.00160
177 0 116.388 129.038 108.970 0.00346 0.00169
178 1 151.737 190.204 129.859 0.00314 0.00135
179 1 148.790 158.359 138.990 0.00309 0.00152
180 1 148.143 155.982 135.041 0.00392 0.00204
181 1 150.440 163.441 144.736 0.00396 0.00206
182 1 148.462 161.078 141.998 0.00397 0.00202
183 1 149.818 163.417 144.786 0.00336 0.00174
184 0 117.226 123.925 106.656 0.00417 0.00186
185 0 116.848 217.552 99.503 0.00531 0.00260
186 0 116.286 177.291 96.983 0.00314 0.00134
187 0 116.556 592.030 86.228 0.00496 0.00254
188 0 116.342 581.289 94.246 0.00267 0.00115
189 0 114.563 119.167 86.647 0.00327 0.00146
190 0 201.774 262.707 78.228 0.00694 0.00412
191 0 174.188 230.978 94.261 0.00459 0.00263
192 0 209.516 253.017 89.488 0.00564 0.00331
193 0 174.688 240.005 74.287 0.01360 0.00624
194 0 198.764 396.961 74.904 0.00740 0.00370
195 0 214.289 260.277 77.973 0.00567 0.00295
MDVP:PPQ MDVP:Shimmer
1 0.00554 0.04374
2 0.00696 0.06134
3 0.00781 0.05233
4 0.00698 0.05492
5 0.00908 0.06425
6 0.00750 0.04701
7 0.00202 0.01608
8 0.00182 0.01567
9 0.00332 0.02093
10 0.00332 0.02838
11 0.00330 0.02143
12 0.00336 0.02752
13 0.00153 0.01259
14 0.00208 0.01642
15 0.00149 0.01828
16 0.00203 0.01503
17 0.00292 0.02047
18 0.00387 0.03327
19 0.00432 0.05517
20 0.00399 0.03995
21 0.00450 0.03810
22 0.00267 0.04137
23 0.00247 0.04351
24 0.00258 0.04192
25 0.00390 0.01659
26 0.00375 0.03767
27 0.00234 0.01966
28 0.00275 0.01919
29 0.00176 0.01718
30 0.00253 0.01791
31 0.00168 0.01098
32 0.00138 0.01015
33 0.00135 0.01263
34 0.00107 0.00954
35 0.00106 0.00958
36 0.00115 0.01194
37 0.00241 0.02126
38 0.00218 0.01851
39 0.00166 0.01444
40 0.00182 0.01663
41 0.00175 0.01495
42 0.00147 0.01463
43 0.00182 0.01752
44 0.00173 0.01760
45 0.00137 0.01419
46 0.00139 0.01494
47 0.00148 0.01608
48 0.00113 0.01152
49 0.00203 0.01613
50 0.00155 0.01681
51 0.00167 0.02184
52 0.00169 0.02033
53 0.00166 0.02297
54 0.00183 0.02498
55 0.00486 0.02719
56 0.00539 0.03209
57 0.00514 0.03715
58 0.00469 0.02293
59 0.00493 0.02645
60 0.00520 0.03225
61 0.00152 0.01861
62 0.00151 0.01906
63 0.00144 0.01643
64 0.00155 0.01644
65 0.00113 0.01457
66 0.00140 0.01745
67 0.00440 0.03198
68 0.00463 0.03111
69 0.00467 0.05384
70 0.00354 0.05428
71 0.00419 0.03485
72 0.00478 0.04978
73 0.00220 0.01706
74 0.00329 0.02448
75 0.00283 0.02442
76 0.00289 0.02215
77 0.00289 0.03999
78 0.00280 0.02199
79 0.00332 0.03202
80 0.00576 0.03121
81 0.00415 0.04024
82 0.00371 0.03156
83 0.00348 0.02427
84 0.00258 0.02223
85 0.00420 0.04795
86 0.00244 0.03852
87 0.00194 0.03759
88 0.00312 0.06511
89 0.00254 0.06727
90 0.00419 0.04313
91 0.00453 0.06640
92 0.00227 0.07959
93 0.00256 0.04190
94 0.00226 0.05925
95 0.00196 0.03716
96 0.00197 0.03272
97 0.00184 0.03381
98 0.00623 0.03886
99 0.00655 0.04689
100 0.00990 0.06734
101 0.01522 0.09178
102 0.00909 0.06170
103 0.01628 0.09419
104 0.00136 0.01131
105 0.00100 0.01030
106 0.00134 0.01346
107 0.00092 0.01064
108 0.00122 0.01450
109 0.00096 0.01024
110 0.00389 0.03044
111 0.00337 0.02286
112 0.00339 0.01761
113 0.00485 0.02378
114 0.00280 0.01680
115 0.00246 0.02105
116 0.00385 0.01843
117 0.00207 0.01458
118 0.00261 0.01725
119 0.00194 0.01279
120 0.00128 0.01299
121 0.00314 0.02008
122 0.00221 0.01169
123 0.00398 0.04479
124 0.00449 0.02503
125 0.00395 0.02343
126 0.00422 0.02362
127 0.00327 0.02791
128 0.00351 0.02857
129 0.00192 0.01033
130 0.00135 0.01022
131 0.00238 0.01412
132 0.00205 0.01516
133 0.00170 0.01201
134 0.00171 0.01043
135 0.00319 0.04932
136 0.00315 0.04128
137 0.00283 0.04879
138 0.00312 0.05279
139 0.00290 0.05643
140 0.00232 0.03026
141 0.00269 0.03273
142 0.00428 0.06725
143 0.00215 0.03527
144 0.00211 0.01997
145 0.00133 0.02662
146 0.00188 0.02536
147 0.00946 0.08143
148 0.00819 0.06050
149 0.01027 0.07118
150 0.00963 0.07170
151 0.01154 0.05830
152 0.01958 0.11908
153 0.01699 0.08684
154 0.00332 0.02534
155 0.00300 0.02682
156 0.00300 0.03087
157 0.00339 0.02293
158 0.00718 0.04912
159 0.00454 0.02852
160 0.00318 0.03235
161 0.00316 0.04009
162 0.00329 0.03273
163 0.00340 0.03658
164 0.00284 0.01756
165 0.00461 0.02814
166 0.00153 0.02448
167 0.00159 0.01242
168 0.00186 0.02030
169 0.00448 0.02177
170 0.00283 0.02018
171 0.00237 0.01897
172 0.00190 0.01358
173 0.00200 0.01484
174 0.00203 0.01472
175 0.00218 0.01657
176 0.00199 0.01503
177 0.00213 0.01725
178 0.00162 0.01469
179 0.00186 0.01574
180 0.00231 0.01450
181 0.00233 0.02551
182 0.00235 0.01831
183 0.00198 0.02145
184 0.00270 0.01909
185 0.00346 0.01795
186 0.00192 0.01564
187 0.00263 0.01660
188 0.00148 0.01300
189 0.00184 0.01185
190 0.00396 0.02574
191 0.00259 0.04087
192 0.00292 0.02751
193 0.00564 0.02308
194 0.00390 0.02296
195 0.00317 0.01884
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `MDVP:Fo(Hz)` `MDVP:Fhi(Hz)` `MDVP:Flo(Hz)`
1.271e+00 -2.239e-03 -2.552e-04 -2.411e-03
`MDVP:Jitter(%)` `MDVP:RAP` `MDVP:PPQ` `MDVP:Shimmer`
-8.599e+01 9.102e+01 5.441e+01 6.901e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.86279 -0.19360 0.08267 0.25589 0.61433
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.271e+00 1.402e-01 9.070 < 2e-16 ***
`MDVP:Fo(Hz)` -2.239e-03 9.236e-04 -2.424 0.01631 *
`MDVP:Fhi(Hz)` -2.552e-04 3.352e-04 -0.761 0.44746
`MDVP:Flo(Hz)` -2.411e-03 8.225e-04 -2.931 0.00380 **
`MDVP:Jitter(%)` -8.599e+01 5.764e+01 -1.492 0.13740
`MDVP:RAP` 9.102e+01 7.215e+01 1.262 0.20867
`MDVP:PPQ` 5.441e+01 4.926e+01 1.105 0.27078
`MDVP:Shimmer` 6.901e+00 2.402e+00 2.873 0.00454 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3699 on 187 degrees of freedom
Multiple R-squared: 0.2929, Adjusted R-squared: 0.2665
F-statistic: 11.07 on 7 and 187 DF, p-value: 1.111e-11
> 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,] 1.317556e-54 2.635113e-54 1.0000000000
[2,] 1.500739e-66 3.001479e-66 1.0000000000
[3,] 1.956010e-93 3.912021e-93 1.0000000000
[4,] 1.521771e-92 3.043542e-92 1.0000000000
[5,] 5.012060e-108 1.002412e-107 1.0000000000
[6,] 0.000000e+00 0.000000e+00 1.0000000000
[7,] 1.330465e-148 2.660930e-148 1.0000000000
[8,] 6.781184e-155 1.356237e-154 1.0000000000
[9,] 3.168340e-169 6.336680e-169 1.0000000000
[10,] 6.661978e-193 1.332396e-192 1.0000000000
[11,] 1.400411e-225 2.800822e-225 1.0000000000
[12,] 1.040644e-217 2.081288e-217 1.0000000000
[13,] 1.716867e-229 3.433734e-229 1.0000000000
[14,] 7.851214e-248 1.570243e-247 1.0000000000
[15,] 1.598754e-266 3.197508e-266 1.0000000000
[16,] 9.722517e-308 1.944503e-307 1.0000000000
[17,] 1.922852e-296 3.845703e-296 1.0000000000
[18,] 4.253687e-307 8.507375e-307 1.0000000000
[19,] 0.000000e+00 0.000000e+00 1.0000000000
[20,] 0.000000e+00 0.000000e+00 1.0000000000
[21,] 5.926277e-09 1.185255e-08 0.9999999941
[22,] 1.146973e-08 2.293945e-08 0.9999999885
[23,] 7.281364e-09 1.456273e-08 0.9999999927
[24,] 3.008124e-09 6.016248e-09 0.9999999970
[25,] 1.130801e-09 2.261602e-09 0.9999999989
[26,] 4.328271e-10 8.656543e-10 0.9999999996
[27,] 8.221234e-07 1.644247e-06 0.9999991779
[28,] 1.577772e-05 3.155544e-05 0.9999842223
[29,] 1.825880e-04 3.651760e-04 0.9998174120
[30,] 7.811026e-04 1.562205e-03 0.9992188974
[31,] 2.213278e-03 4.426557e-03 0.9977867216
[32,] 3.950001e-03 7.900001e-03 0.9960499995
[33,] 2.967536e-03 5.935072e-03 0.9970324641
[34,] 2.035650e-03 4.071301e-03 0.9979643497
[35,] 1.326961e-03 2.653923e-03 0.9986730387
[36,] 8.702961e-04 1.740592e-03 0.9991297039
[37,] 5.583374e-04 1.116675e-03 0.9994416626
[38,] 3.942077e-04 7.884154e-04 0.9996057923
[39,] 2.486608e-03 4.973216e-03 0.9975133922
[40,] 3.545127e-03 7.090255e-03 0.9964548726
[41,] 5.041862e-03 1.008372e-02 0.9949581381
[42,] 5.385921e-03 1.077184e-02 0.9946140794
[43,] 6.261900e-03 1.252380e-02 0.9937381001
[44,] 8.096544e-03 1.619309e-02 0.9919034557
[45,] 6.851690e-03 1.370338e-02 0.9931483104
[46,] 6.388435e-03 1.277687e-02 0.9936115647
[47,] 5.241458e-03 1.048292e-02 0.9947585422
[48,] 7.712098e-03 1.542420e-02 0.9922879024
[49,] 7.294435e-03 1.458887e-02 0.9927055653
[50,] 5.257427e-03 1.051485e-02 0.9947425731
[51,] 1.798181e-02 3.596361e-02 0.9820181930
[52,] 4.168117e-02 8.336234e-02 0.9583188299
[53,] 3.890483e-02 7.780965e-02 0.9610951741
[54,] 3.505106e-02 7.010213e-02 0.9649489365
[55,] 3.144960e-02 6.289921e-02 0.9685503968
[56,] 4.326177e-02 8.652355e-02 0.9567382272
[57,] 3.483472e-02 6.966945e-02 0.9651652762
[58,] 2.954015e-02 5.908030e-02 0.9704598508
[59,] 2.398842e-02 4.797684e-02 0.9760115816
[60,] 1.873347e-02 3.746693e-02 0.9812665331
[61,] 1.444782e-02 2.889563e-02 0.9855521839
[62,] 1.091015e-02 2.182031e-02 0.9890898470
[63,] 9.251409e-03 1.850282e-02 0.9907485909
[64,] 1.382413e-02 2.764826e-02 0.9861758717
[65,] 1.052246e-02 2.104491e-02 0.9894775425
[66,] 7.970789e-03 1.594158e-02 0.9920292110
[67,] 5.854539e-03 1.170908e-02 0.9941454614
[68,] 4.393575e-03 8.787149e-03 0.9956064253
[69,] 3.266510e-03 6.533021e-03 0.9967334897
[70,] 3.438958e-03 6.877916e-03 0.9965610421
[71,] 2.713251e-03 5.426503e-03 0.9972867487
[72,] 2.145238e-03 4.290475e-03 0.9978547624
[73,] 1.618272e-03 3.236545e-03 0.9983817276
[74,] 1.212994e-03 2.425988e-03 0.9987870060
[75,] 8.577569e-04 1.715514e-03 0.9991422431
[76,] 1.037153e-03 2.074306e-03 0.9989628468
[77,] 1.077597e-03 2.155194e-03 0.9989224032
[78,] 7.812781e-04 1.562556e-03 0.9992187219
[79,] 5.374590e-04 1.074918e-03 0.9994625410
[80,] 4.623961e-04 9.247922e-04 0.9995376039
[81,] 3.157277e-04 6.314555e-04 0.9996842723
[82,] 2.430792e-04 4.861584e-04 0.9997569208
[83,] 1.861853e-04 3.723707e-04 0.9998138147
[84,] 1.249710e-04 2.499421e-04 0.9998750290
[85,] 8.324428e-05 1.664886e-04 0.9999167557
[86,] 7.616972e-05 1.523394e-04 0.9999238303
[87,] 6.781858e-05 1.356372e-04 0.9999321814
[88,] 4.533603e-05 9.067206e-05 0.9999546640
[89,] 2.912963e-05 5.825927e-05 0.9999708704
[90,] 2.040837e-05 4.081673e-05 0.9999795916
[91,] 1.780163e-05 3.560327e-05 0.9999821984
[92,] 1.403120e-05 2.806239e-05 0.9999859688
[93,] 1.844625e-05 3.689249e-05 0.9999815538
[94,] 1.469778e-05 2.939557e-05 0.9999853022
[95,] 1.502954e-05 3.005909e-05 0.9999849705
[96,] 1.501103e-05 3.002207e-05 0.9999849890
[97,] 1.680595e-05 3.361189e-05 0.9999831941
[98,] 1.704247e-05 3.408494e-05 0.9999829575
[99,] 1.717514e-05 3.435027e-05 0.9999828249
[100,] 1.358975e-05 2.717949e-05 0.9999864103
[101,] 1.251953e-05 2.503907e-05 0.9999874805
[102,] 3.464631e-05 6.929262e-05 0.9999653537
[103,] 6.712245e-05 1.342449e-04 0.9999328776
[104,] 1.498929e-04 2.997857e-04 0.9998501071
[105,] 1.497749e-04 2.995499e-04 0.9998502251
[106,] 1.493565e-04 2.987130e-04 0.9998506435
[107,] 1.453228e-04 2.906456e-04 0.9998546772
[108,] 1.617330e-04 3.234660e-04 0.9998382670
[109,] 2.397302e-04 4.794605e-04 0.9997602698
[110,] 5.114630e-04 1.022926e-03 0.9994885370
[111,] 7.216904e-04 1.443381e-03 0.9992783096
[112,] 9.776484e-04 1.955297e-03 0.9990223516
[113,] 6.949288e-04 1.389858e-03 0.9993050712
[114,] 6.535975e-04 1.307195e-03 0.9993464025
[115,] 6.422334e-04 1.284467e-03 0.9993577666
[116,] 6.585041e-04 1.317008e-03 0.9993414959
[117,] 6.008150e-04 1.201630e-03 0.9993991850
[118,] 5.969355e-04 1.193871e-03 0.9994030645
[119,] 5.849708e-04 1.169942e-03 0.9994150292
[120,] 6.041656e-04 1.208331e-03 0.9993958344
[121,] 5.900363e-04 1.180073e-03 0.9994099637
[122,] 5.715308e-04 1.143062e-03 0.9994284692
[123,] 6.549342e-04 1.309868e-03 0.9993450658
[124,] 8.148985e-04 1.629797e-03 0.9991851015
[125,] 5.794450e-04 1.158890e-03 0.9994205550
[126,] 3.920522e-04 7.841044e-04 0.9996079478
[127,] 2.655211e-04 5.310423e-04 0.9997344789
[128,] 1.879027e-04 3.758055e-04 0.9998120973
[129,] 1.604952e-04 3.209903e-04 0.9998395048
[130,] 1.115696e-04 2.231392e-04 0.9998884304
[131,] 1.155145e-04 2.310290e-04 0.9998844855
[132,] 7.979513e-05 1.595903e-04 0.9999202049
[133,] 8.344820e-05 1.668964e-04 0.9999165518
[134,] 2.690972e-04 5.381943e-04 0.9997309028
[135,] 4.987776e-04 9.975553e-04 0.9995012224
[136,] 2.841842e-03 5.683684e-03 0.9971581579
[137,] 2.360320e-03 4.720640e-03 0.9976396802
[138,] 1.851693e-03 3.703387e-03 0.9981483067
[139,] 1.337670e-03 2.675340e-03 0.9986623299
[140,] 1.215388e-03 2.430776e-03 0.9987846121
[141,] 1.322389e-03 2.644777e-03 0.9986776113
[142,] 1.279090e-03 2.558180e-03 0.9987209099
[143,] 8.556020e-04 1.711204e-03 0.9991443980
[144,] 7.526550e-04 1.505310e-03 0.9992473450
[145,] 6.240444e-04 1.248089e-03 0.9993759556
[146,] 4.587775e-04 9.175549e-04 0.9995412225
[147,] 5.423010e-04 1.084602e-03 0.9994576990
[148,] 8.563408e-04 1.712682e-03 0.9991436592
[149,] 5.423030e-04 1.084606e-03 0.9994576970
[150,] 3.829081e-04 7.658162e-04 0.9996170919
[151,] 2.523638e-04 5.047276e-04 0.9997476362
[152,] 1.605915e-04 3.211830e-04 0.9998394085
[153,] 1.039081e-04 2.078163e-04 0.9998960919
[154,] 1.889071e-04 3.778143e-04 0.9998110929
[155,] 3.516431e-04 7.032861e-04 0.9996483569
[156,] 4.170518e-04 8.341035e-04 0.9995829482
[157,] 3.804907e-04 7.609813e-04 0.9996195093
[158,] 6.749509e-04 1.349902e-03 0.9993250491
[159,] 1.725003e-03 3.450005e-03 0.9982749973
[160,] 2.657395e-03 5.314789e-03 0.9973426054
[161,] 9.996312e-01 7.375855e-04 0.0003687928
[162,] 9.996978e-01 6.043535e-04 0.0003021767
[163,] 9.994960e-01 1.008029e-03 0.0005040146
[164,] 9.991697e-01 1.660508e-03 0.0008302542
[165,] 9.984976e-01 3.004817e-03 0.0015024087
[166,] 9.989302e-01 2.139529e-03 0.0010697647
[167,] 9.995027e-01 9.945755e-04 0.0004972877
[168,] 9.993539e-01 1.292132e-03 0.0006460662
[169,] 9.981574e-01 3.685184e-03 0.0018425922
[170,] 9.959863e-01 8.027406e-03 0.0040137028
[171,] 9.896811e-01 2.063779e-02 0.0103188933
[172,] 9.755592e-01 4.888161e-02 0.0244408049
[173,] 1.000000e+00 0.000000e+00 0.0000000000
[174,] 1.000000e+00 0.000000e+00 0.0000000000
> postscript(file="/var/wessaorg/rcomp/tmp/138yh1386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2qf9k1386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3vmo21386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4a4mc1386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5q0js1386668995.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 6
-0.047579233 -0.077764966 -0.085956373 -0.064765541 -0.138069119 -0.015103432
7 8 9 10 11 12
0.234194556 0.160684110 0.079988076 0.029154653 0.033427515 0.007928464
13 14 15 16 17 18
0.366961155 0.229263988 0.230854523 0.263199748 0.315777553 0.266846263
19 20 21 22 23 24
-0.027383778 0.300552590 0.099019778 0.076496616 0.089770113 0.153511599
25 26 27 28 29 30
0.296956753 -0.004316119 0.247529353 0.224689086 0.235075874 0.246000389
31 32 33 34 35 36
-0.384027640 -0.368555298 -0.387859086 -0.343255811 -0.345798255 -0.364981408
37 38 39 40 41 42
0.444724740 0.449173537 0.521470561 0.522774283 0.531755545 0.512422895
43 44 45 46 47 48
-0.251595168 -0.223715376 -0.192989681 -0.204805750 -0.200830022 -0.264344950
49 50 51 52 53 54
-0.610814889 -0.631507801 -0.674251617 -0.648544186 -0.653789563 -0.660888632
55 56 57 58 59 60
0.139359572 0.133287278 0.076320389 0.251675155 0.207623782 0.207996725
61 62 63 64 65 66
-0.581202316 -0.599228886 -0.320650666 -0.262508498 -0.249691266 -0.540373090
67 68 69 70 71 72
0.096324565 0.102623648 -0.017680839 -0.072859967 0.066776289 -0.017116834
73 74 75 76 77 78
0.248300216 0.254144546 0.133809907 0.142496173 0.028292646 0.153254459
79 80 81 82 83 84
0.005878834 -0.012248361 -0.085440437 -0.024254667 0.058157468 0.040578595
85 86 87 88 89 90
0.061219108 0.310111496 0.282112133 0.051046024 -0.080928638 0.266149700
91 92 93 94 95 96
-0.076506644 -0.050839489 0.184525940 -0.082077188 0.057437190 0.284901752
97 98 99 100 101 102
0.275788431 0.142759314 0.031309665 -0.104832322 -0.234968569 -0.173350904
103 104 105 106 107 108
-0.284491342 0.251851868 0.387169313 0.376479990 0.424283662 0.382195857
109 110 111 112 113 114
0.387886956 0.235137447 0.306645247 0.614329368 0.538811184 0.603919021
115 116 117 118 119 120
0.341898266 0.392760907 0.370608552 0.410538775 0.499255075 0.573450079
121 122 123 124 125 126
0.323388402 0.386348787 0.001132509 0.223891446 0.174082709 0.159571113
127 128 129 130 131 132
0.113540996 0.170332061 0.298871619 0.267213426 0.223600139 0.212447860
133 134 135 136 137 138
0.204782726 0.257631166 -0.051890363 0.009472388 -0.025654829 -0.078299178
139 140 141 142 143 144
-0.094714201 0.114932098 0.278094908 0.045057458 0.384729075 0.343233783
145 146 147 148 149 150
0.510855171 0.378941970 0.009046120 0.208625103 0.127993006 0.297921743
151 152 153 154 155 156
0.162156612 -0.229084022 0.035324612 0.152391623 0.131327827 0.111770757
157 158 159 160 161 162
0.172198477 0.082665746 0.140489463 0.152761324 -0.019242224 0.088443290
163 164 165 166 167 168
0.026441398 0.154238947 0.087422456 -0.587987920 -0.194218578 -0.185645187
169 170 171 172 173 174
-0.704022700 -0.277180285 -0.202734174 -0.773338936 -0.812793583 -0.820916116
175 176 177 178 179 180
-0.821184985 -0.791799251 -0.806310385 0.387639765 0.354852476 0.351394791
181 182 183 184 185 186
0.306360952 0.347831467 0.329675683 -0.809446062 -0.806450365 -0.796248183
187 188 189 190 191 192
-0.713711545 -0.680592952 -0.809091495 -0.735242973 -0.862786464 -0.686933740
193 194 195
-0.504472211 -0.615499545 -0.620575266
> postscript(file="/var/wessaorg/rcomp/tmp/61yez1386668995.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 -0.047579233 NA
1 -0.077764966 -0.047579233
2 -0.085956373 -0.077764966
3 -0.064765541 -0.085956373
4 -0.138069119 -0.064765541
5 -0.015103432 -0.138069119
6 0.234194556 -0.015103432
7 0.160684110 0.234194556
8 0.079988076 0.160684110
9 0.029154653 0.079988076
10 0.033427515 0.029154653
11 0.007928464 0.033427515
12 0.366961155 0.007928464
13 0.229263988 0.366961155
14 0.230854523 0.229263988
15 0.263199748 0.230854523
16 0.315777553 0.263199748
17 0.266846263 0.315777553
18 -0.027383778 0.266846263
19 0.300552590 -0.027383778
20 0.099019778 0.300552590
21 0.076496616 0.099019778
22 0.089770113 0.076496616
23 0.153511599 0.089770113
24 0.296956753 0.153511599
25 -0.004316119 0.296956753
26 0.247529353 -0.004316119
27 0.224689086 0.247529353
28 0.235075874 0.224689086
29 0.246000389 0.235075874
30 -0.384027640 0.246000389
31 -0.368555298 -0.384027640
32 -0.387859086 -0.368555298
33 -0.343255811 -0.387859086
34 -0.345798255 -0.343255811
35 -0.364981408 -0.345798255
36 0.444724740 -0.364981408
37 0.449173537 0.444724740
38 0.521470561 0.449173537
39 0.522774283 0.521470561
40 0.531755545 0.522774283
41 0.512422895 0.531755545
42 -0.251595168 0.512422895
43 -0.223715376 -0.251595168
44 -0.192989681 -0.223715376
45 -0.204805750 -0.192989681
46 -0.200830022 -0.204805750
47 -0.264344950 -0.200830022
48 -0.610814889 -0.264344950
49 -0.631507801 -0.610814889
50 -0.674251617 -0.631507801
51 -0.648544186 -0.674251617
52 -0.653789563 -0.648544186
53 -0.660888632 -0.653789563
54 0.139359572 -0.660888632
55 0.133287278 0.139359572
56 0.076320389 0.133287278
57 0.251675155 0.076320389
58 0.207623782 0.251675155
59 0.207996725 0.207623782
60 -0.581202316 0.207996725
61 -0.599228886 -0.581202316
62 -0.320650666 -0.599228886
63 -0.262508498 -0.320650666
64 -0.249691266 -0.262508498
65 -0.540373090 -0.249691266
66 0.096324565 -0.540373090
67 0.102623648 0.096324565
68 -0.017680839 0.102623648
69 -0.072859967 -0.017680839
70 0.066776289 -0.072859967
71 -0.017116834 0.066776289
72 0.248300216 -0.017116834
73 0.254144546 0.248300216
74 0.133809907 0.254144546
75 0.142496173 0.133809907
76 0.028292646 0.142496173
77 0.153254459 0.028292646
78 0.005878834 0.153254459
79 -0.012248361 0.005878834
80 -0.085440437 -0.012248361
81 -0.024254667 -0.085440437
82 0.058157468 -0.024254667
83 0.040578595 0.058157468
84 0.061219108 0.040578595
85 0.310111496 0.061219108
86 0.282112133 0.310111496
87 0.051046024 0.282112133
88 -0.080928638 0.051046024
89 0.266149700 -0.080928638
90 -0.076506644 0.266149700
91 -0.050839489 -0.076506644
92 0.184525940 -0.050839489
93 -0.082077188 0.184525940
94 0.057437190 -0.082077188
95 0.284901752 0.057437190
96 0.275788431 0.284901752
97 0.142759314 0.275788431
98 0.031309665 0.142759314
99 -0.104832322 0.031309665
100 -0.234968569 -0.104832322
101 -0.173350904 -0.234968569
102 -0.284491342 -0.173350904
103 0.251851868 -0.284491342
104 0.387169313 0.251851868
105 0.376479990 0.387169313
106 0.424283662 0.376479990
107 0.382195857 0.424283662
108 0.387886956 0.382195857
109 0.235137447 0.387886956
110 0.306645247 0.235137447
111 0.614329368 0.306645247
112 0.538811184 0.614329368
113 0.603919021 0.538811184
114 0.341898266 0.603919021
115 0.392760907 0.341898266
116 0.370608552 0.392760907
117 0.410538775 0.370608552
118 0.499255075 0.410538775
119 0.573450079 0.499255075
120 0.323388402 0.573450079
121 0.386348787 0.323388402
122 0.001132509 0.386348787
123 0.223891446 0.001132509
124 0.174082709 0.223891446
125 0.159571113 0.174082709
126 0.113540996 0.159571113
127 0.170332061 0.113540996
128 0.298871619 0.170332061
129 0.267213426 0.298871619
130 0.223600139 0.267213426
131 0.212447860 0.223600139
132 0.204782726 0.212447860
133 0.257631166 0.204782726
134 -0.051890363 0.257631166
135 0.009472388 -0.051890363
136 -0.025654829 0.009472388
137 -0.078299178 -0.025654829
138 -0.094714201 -0.078299178
139 0.114932098 -0.094714201
140 0.278094908 0.114932098
141 0.045057458 0.278094908
142 0.384729075 0.045057458
143 0.343233783 0.384729075
144 0.510855171 0.343233783
145 0.378941970 0.510855171
146 0.009046120 0.378941970
147 0.208625103 0.009046120
148 0.127993006 0.208625103
149 0.297921743 0.127993006
150 0.162156612 0.297921743
151 -0.229084022 0.162156612
152 0.035324612 -0.229084022
153 0.152391623 0.035324612
154 0.131327827 0.152391623
155 0.111770757 0.131327827
156 0.172198477 0.111770757
157 0.082665746 0.172198477
158 0.140489463 0.082665746
159 0.152761324 0.140489463
160 -0.019242224 0.152761324
161 0.088443290 -0.019242224
162 0.026441398 0.088443290
163 0.154238947 0.026441398
164 0.087422456 0.154238947
165 -0.587987920 0.087422456
166 -0.194218578 -0.587987920
167 -0.185645187 -0.194218578
168 -0.704022700 -0.185645187
169 -0.277180285 -0.704022700
170 -0.202734174 -0.277180285
171 -0.773338936 -0.202734174
172 -0.812793583 -0.773338936
173 -0.820916116 -0.812793583
174 -0.821184985 -0.820916116
175 -0.791799251 -0.821184985
176 -0.806310385 -0.791799251
177 0.387639765 -0.806310385
178 0.354852476 0.387639765
179 0.351394791 0.354852476
180 0.306360952 0.351394791
181 0.347831467 0.306360952
182 0.329675683 0.347831467
183 -0.809446062 0.329675683
184 -0.806450365 -0.809446062
185 -0.796248183 -0.806450365
186 -0.713711545 -0.796248183
187 -0.680592952 -0.713711545
188 -0.809091495 -0.680592952
189 -0.735242973 -0.809091495
190 -0.862786464 -0.735242973
191 -0.686933740 -0.862786464
192 -0.504472211 -0.686933740
193 -0.615499545 -0.504472211
194 -0.620575266 -0.615499545
195 NA -0.620575266
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.077764966 -0.047579233
[2,] -0.085956373 -0.077764966
[3,] -0.064765541 -0.085956373
[4,] -0.138069119 -0.064765541
[5,] -0.015103432 -0.138069119
[6,] 0.234194556 -0.015103432
[7,] 0.160684110 0.234194556
[8,] 0.079988076 0.160684110
[9,] 0.029154653 0.079988076
[10,] 0.033427515 0.029154653
[11,] 0.007928464 0.033427515
[12,] 0.366961155 0.007928464
[13,] 0.229263988 0.366961155
[14,] 0.230854523 0.229263988
[15,] 0.263199748 0.230854523
[16,] 0.315777553 0.263199748
[17,] 0.266846263 0.315777553
[18,] -0.027383778 0.266846263
[19,] 0.300552590 -0.027383778
[20,] 0.099019778 0.300552590
[21,] 0.076496616 0.099019778
[22,] 0.089770113 0.076496616
[23,] 0.153511599 0.089770113
[24,] 0.296956753 0.153511599
[25,] -0.004316119 0.296956753
[26,] 0.247529353 -0.004316119
[27,] 0.224689086 0.247529353
[28,] 0.235075874 0.224689086
[29,] 0.246000389 0.235075874
[30,] -0.384027640 0.246000389
[31,] -0.368555298 -0.384027640
[32,] -0.387859086 -0.368555298
[33,] -0.343255811 -0.387859086
[34,] -0.345798255 -0.343255811
[35,] -0.364981408 -0.345798255
[36,] 0.444724740 -0.364981408
[37,] 0.449173537 0.444724740
[38,] 0.521470561 0.449173537
[39,] 0.522774283 0.521470561
[40,] 0.531755545 0.522774283
[41,] 0.512422895 0.531755545
[42,] -0.251595168 0.512422895
[43,] -0.223715376 -0.251595168
[44,] -0.192989681 -0.223715376
[45,] -0.204805750 -0.192989681
[46,] -0.200830022 -0.204805750
[47,] -0.264344950 -0.200830022
[48,] -0.610814889 -0.264344950
[49,] -0.631507801 -0.610814889
[50,] -0.674251617 -0.631507801
[51,] -0.648544186 -0.674251617
[52,] -0.653789563 -0.648544186
[53,] -0.660888632 -0.653789563
[54,] 0.139359572 -0.660888632
[55,] 0.133287278 0.139359572
[56,] 0.076320389 0.133287278
[57,] 0.251675155 0.076320389
[58,] 0.207623782 0.251675155
[59,] 0.207996725 0.207623782
[60,] -0.581202316 0.207996725
[61,] -0.599228886 -0.581202316
[62,] -0.320650666 -0.599228886
[63,] -0.262508498 -0.320650666
[64,] -0.249691266 -0.262508498
[65,] -0.540373090 -0.249691266
[66,] 0.096324565 -0.540373090
[67,] 0.102623648 0.096324565
[68,] -0.017680839 0.102623648
[69,] -0.072859967 -0.017680839
[70,] 0.066776289 -0.072859967
[71,] -0.017116834 0.066776289
[72,] 0.248300216 -0.017116834
[73,] 0.254144546 0.248300216
[74,] 0.133809907 0.254144546
[75,] 0.142496173 0.133809907
[76,] 0.028292646 0.142496173
[77,] 0.153254459 0.028292646
[78,] 0.005878834 0.153254459
[79,] -0.012248361 0.005878834
[80,] -0.085440437 -0.012248361
[81,] -0.024254667 -0.085440437
[82,] 0.058157468 -0.024254667
[83,] 0.040578595 0.058157468
[84,] 0.061219108 0.040578595
[85,] 0.310111496 0.061219108
[86,] 0.282112133 0.310111496
[87,] 0.051046024 0.282112133
[88,] -0.080928638 0.051046024
[89,] 0.266149700 -0.080928638
[90,] -0.076506644 0.266149700
[91,] -0.050839489 -0.076506644
[92,] 0.184525940 -0.050839489
[93,] -0.082077188 0.184525940
[94,] 0.057437190 -0.082077188
[95,] 0.284901752 0.057437190
[96,] 0.275788431 0.284901752
[97,] 0.142759314 0.275788431
[98,] 0.031309665 0.142759314
[99,] -0.104832322 0.031309665
[100,] -0.234968569 -0.104832322
[101,] -0.173350904 -0.234968569
[102,] -0.284491342 -0.173350904
[103,] 0.251851868 -0.284491342
[104,] 0.387169313 0.251851868
[105,] 0.376479990 0.387169313
[106,] 0.424283662 0.376479990
[107,] 0.382195857 0.424283662
[108,] 0.387886956 0.382195857
[109,] 0.235137447 0.387886956
[110,] 0.306645247 0.235137447
[111,] 0.614329368 0.306645247
[112,] 0.538811184 0.614329368
[113,] 0.603919021 0.538811184
[114,] 0.341898266 0.603919021
[115,] 0.392760907 0.341898266
[116,] 0.370608552 0.392760907
[117,] 0.410538775 0.370608552
[118,] 0.499255075 0.410538775
[119,] 0.573450079 0.499255075
[120,] 0.323388402 0.573450079
[121,] 0.386348787 0.323388402
[122,] 0.001132509 0.386348787
[123,] 0.223891446 0.001132509
[124,] 0.174082709 0.223891446
[125,] 0.159571113 0.174082709
[126,] 0.113540996 0.159571113
[127,] 0.170332061 0.113540996
[128,] 0.298871619 0.170332061
[129,] 0.267213426 0.298871619
[130,] 0.223600139 0.267213426
[131,] 0.212447860 0.223600139
[132,] 0.204782726 0.212447860
[133,] 0.257631166 0.204782726
[134,] -0.051890363 0.257631166
[135,] 0.009472388 -0.051890363
[136,] -0.025654829 0.009472388
[137,] -0.078299178 -0.025654829
[138,] -0.094714201 -0.078299178
[139,] 0.114932098 -0.094714201
[140,] 0.278094908 0.114932098
[141,] 0.045057458 0.278094908
[142,] 0.384729075 0.045057458
[143,] 0.343233783 0.384729075
[144,] 0.510855171 0.343233783
[145,] 0.378941970 0.510855171
[146,] 0.009046120 0.378941970
[147,] 0.208625103 0.009046120
[148,] 0.127993006 0.208625103
[149,] 0.297921743 0.127993006
[150,] 0.162156612 0.297921743
[151,] -0.229084022 0.162156612
[152,] 0.035324612 -0.229084022
[153,] 0.152391623 0.035324612
[154,] 0.131327827 0.152391623
[155,] 0.111770757 0.131327827
[156,] 0.172198477 0.111770757
[157,] 0.082665746 0.172198477
[158,] 0.140489463 0.082665746
[159,] 0.152761324 0.140489463
[160,] -0.019242224 0.152761324
[161,] 0.088443290 -0.019242224
[162,] 0.026441398 0.088443290
[163,] 0.154238947 0.026441398
[164,] 0.087422456 0.154238947
[165,] -0.587987920 0.087422456
[166,] -0.194218578 -0.587987920
[167,] -0.185645187 -0.194218578
[168,] -0.704022700 -0.185645187
[169,] -0.277180285 -0.704022700
[170,] -0.202734174 -0.277180285
[171,] -0.773338936 -0.202734174
[172,] -0.812793583 -0.773338936
[173,] -0.820916116 -0.812793583
[174,] -0.821184985 -0.820916116
[175,] -0.791799251 -0.821184985
[176,] -0.806310385 -0.791799251
[177,] 0.387639765 -0.806310385
[178,] 0.354852476 0.387639765
[179,] 0.351394791 0.354852476
[180,] 0.306360952 0.351394791
[181,] 0.347831467 0.306360952
[182,] 0.329675683 0.347831467
[183,] -0.809446062 0.329675683
[184,] -0.806450365 -0.809446062
[185,] -0.796248183 -0.806450365
[186,] -0.713711545 -0.796248183
[187,] -0.680592952 -0.713711545
[188,] -0.809091495 -0.680592952
[189,] -0.735242973 -0.809091495
[190,] -0.862786464 -0.735242973
[191,] -0.686933740 -0.862786464
[192,] -0.504472211 -0.686933740
[193,] -0.615499545 -0.504472211
[194,] -0.620575266 -0.615499545
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.077764966 -0.047579233
2 -0.085956373 -0.077764966
3 -0.064765541 -0.085956373
4 -0.138069119 -0.064765541
5 -0.015103432 -0.138069119
6 0.234194556 -0.015103432
7 0.160684110 0.234194556
8 0.079988076 0.160684110
9 0.029154653 0.079988076
10 0.033427515 0.029154653
11 0.007928464 0.033427515
12 0.366961155 0.007928464
13 0.229263988 0.366961155
14 0.230854523 0.229263988
15 0.263199748 0.230854523
16 0.315777553 0.263199748
17 0.266846263 0.315777553
18 -0.027383778 0.266846263
19 0.300552590 -0.027383778
20 0.099019778 0.300552590
21 0.076496616 0.099019778
22 0.089770113 0.076496616
23 0.153511599 0.089770113
24 0.296956753 0.153511599
25 -0.004316119 0.296956753
26 0.247529353 -0.004316119
27 0.224689086 0.247529353
28 0.235075874 0.224689086
29 0.246000389 0.235075874
30 -0.384027640 0.246000389
31 -0.368555298 -0.384027640
32 -0.387859086 -0.368555298
33 -0.343255811 -0.387859086
34 -0.345798255 -0.343255811
35 -0.364981408 -0.345798255
36 0.444724740 -0.364981408
37 0.449173537 0.444724740
38 0.521470561 0.449173537
39 0.522774283 0.521470561
40 0.531755545 0.522774283
41 0.512422895 0.531755545
42 -0.251595168 0.512422895
43 -0.223715376 -0.251595168
44 -0.192989681 -0.223715376
45 -0.204805750 -0.192989681
46 -0.200830022 -0.204805750
47 -0.264344950 -0.200830022
48 -0.610814889 -0.264344950
49 -0.631507801 -0.610814889
50 -0.674251617 -0.631507801
51 -0.648544186 -0.674251617
52 -0.653789563 -0.648544186
53 -0.660888632 -0.653789563
54 0.139359572 -0.660888632
55 0.133287278 0.139359572
56 0.076320389 0.133287278
57 0.251675155 0.076320389
58 0.207623782 0.251675155
59 0.207996725 0.207623782
60 -0.581202316 0.207996725
61 -0.599228886 -0.581202316
62 -0.320650666 -0.599228886
63 -0.262508498 -0.320650666
64 -0.249691266 -0.262508498
65 -0.540373090 -0.249691266
66 0.096324565 -0.540373090
67 0.102623648 0.096324565
68 -0.017680839 0.102623648
69 -0.072859967 -0.017680839
70 0.066776289 -0.072859967
71 -0.017116834 0.066776289
72 0.248300216 -0.017116834
73 0.254144546 0.248300216
74 0.133809907 0.254144546
75 0.142496173 0.133809907
76 0.028292646 0.142496173
77 0.153254459 0.028292646
78 0.005878834 0.153254459
79 -0.012248361 0.005878834
80 -0.085440437 -0.012248361
81 -0.024254667 -0.085440437
82 0.058157468 -0.024254667
83 0.040578595 0.058157468
84 0.061219108 0.040578595
85 0.310111496 0.061219108
86 0.282112133 0.310111496
87 0.051046024 0.282112133
88 -0.080928638 0.051046024
89 0.266149700 -0.080928638
90 -0.076506644 0.266149700
91 -0.050839489 -0.076506644
92 0.184525940 -0.050839489
93 -0.082077188 0.184525940
94 0.057437190 -0.082077188
95 0.284901752 0.057437190
96 0.275788431 0.284901752
97 0.142759314 0.275788431
98 0.031309665 0.142759314
99 -0.104832322 0.031309665
100 -0.234968569 -0.104832322
101 -0.173350904 -0.234968569
102 -0.284491342 -0.173350904
103 0.251851868 -0.284491342
104 0.387169313 0.251851868
105 0.376479990 0.387169313
106 0.424283662 0.376479990
107 0.382195857 0.424283662
108 0.387886956 0.382195857
109 0.235137447 0.387886956
110 0.306645247 0.235137447
111 0.614329368 0.306645247
112 0.538811184 0.614329368
113 0.603919021 0.538811184
114 0.341898266 0.603919021
115 0.392760907 0.341898266
116 0.370608552 0.392760907
117 0.410538775 0.370608552
118 0.499255075 0.410538775
119 0.573450079 0.499255075
120 0.323388402 0.573450079
121 0.386348787 0.323388402
122 0.001132509 0.386348787
123 0.223891446 0.001132509
124 0.174082709 0.223891446
125 0.159571113 0.174082709
126 0.113540996 0.159571113
127 0.170332061 0.113540996
128 0.298871619 0.170332061
129 0.267213426 0.298871619
130 0.223600139 0.267213426
131 0.212447860 0.223600139
132 0.204782726 0.212447860
133 0.257631166 0.204782726
134 -0.051890363 0.257631166
135 0.009472388 -0.051890363
136 -0.025654829 0.009472388
137 -0.078299178 -0.025654829
138 -0.094714201 -0.078299178
139 0.114932098 -0.094714201
140 0.278094908 0.114932098
141 0.045057458 0.278094908
142 0.384729075 0.045057458
143 0.343233783 0.384729075
144 0.510855171 0.343233783
145 0.378941970 0.510855171
146 0.009046120 0.378941970
147 0.208625103 0.009046120
148 0.127993006 0.208625103
149 0.297921743 0.127993006
150 0.162156612 0.297921743
151 -0.229084022 0.162156612
152 0.035324612 -0.229084022
153 0.152391623 0.035324612
154 0.131327827 0.152391623
155 0.111770757 0.131327827
156 0.172198477 0.111770757
157 0.082665746 0.172198477
158 0.140489463 0.082665746
159 0.152761324 0.140489463
160 -0.019242224 0.152761324
161 0.088443290 -0.019242224
162 0.026441398 0.088443290
163 0.154238947 0.026441398
164 0.087422456 0.154238947
165 -0.587987920 0.087422456
166 -0.194218578 -0.587987920
167 -0.185645187 -0.194218578
168 -0.704022700 -0.185645187
169 -0.277180285 -0.704022700
170 -0.202734174 -0.277180285
171 -0.773338936 -0.202734174
172 -0.812793583 -0.773338936
173 -0.820916116 -0.812793583
174 -0.821184985 -0.820916116
175 -0.791799251 -0.821184985
176 -0.806310385 -0.791799251
177 0.387639765 -0.806310385
178 0.354852476 0.387639765
179 0.351394791 0.354852476
180 0.306360952 0.351394791
181 0.347831467 0.306360952
182 0.329675683 0.347831467
183 -0.809446062 0.329675683
184 -0.806450365 -0.809446062
185 -0.796248183 -0.806450365
186 -0.713711545 -0.796248183
187 -0.680592952 -0.713711545
188 -0.809091495 -0.680592952
189 -0.735242973 -0.809091495
190 -0.862786464 -0.735242973
191 -0.686933740 -0.862786464
192 -0.504472211 -0.686933740
193 -0.615499545 -0.504472211
194 -0.620575266 -0.615499545
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7n2dx1386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8m7aw1386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/96anz1386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10i1m41386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, 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/wessaorg/rcomp/tmp/11p0ey1386668995.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/wessaorg/rcomp/tmp/12vvja1386668995.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/wessaorg/rcomp/tmp/13x16e1386668995.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/wessaorg/rcomp/tmp/141oft1386668995.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/wessaorg/rcomp/tmp/15f76s1386668995.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/wessaorg/rcomp/tmp/16tj8a1386668995.tab")
+ }
>
> try(system("convert tmp/138yh1386668995.ps tmp/138yh1386668995.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qf9k1386668995.ps tmp/2qf9k1386668995.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vmo21386668995.ps tmp/3vmo21386668995.png",intern=TRUE))
character(0)
> try(system("convert tmp/4a4mc1386668995.ps tmp/4a4mc1386668995.png",intern=TRUE))
character(0)
> try(system("convert tmp/5q0js1386668995.ps tmp/5q0js1386668995.png",intern=TRUE))
character(0)
> try(system("convert tmp/61yez1386668995.ps tmp/61yez1386668995.png",intern=TRUE))
character(0)
> try(system("convert tmp/7n2dx1386668995.ps tmp/7n2dx1386668995.png",intern=TRUE))
character(0)
> try(system("convert tmp/8m7aw1386668995.ps tmp/8m7aw1386668995.png",intern=TRUE))
character(0)
> try(system("convert tmp/96anz1386668995.ps tmp/96anz1386668995.png",intern=TRUE))
character(0)
> try(system("convert tmp/10i1m41386668995.ps tmp/10i1m41386668995.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.060 2.604 17.645