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 'contributors()' for more information and
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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(119.992
+ ,157.302
+ ,0.00784
+ ,0.00007
+ ,0.00554
+ ,0.02971
+ ,122.4
+ ,148.65
+ ,0.00968
+ ,0.00008
+ ,0.00696
+ ,0.04368
+ ,116.682
+ ,131.111
+ ,0.0105
+ ,0.00009
+ ,0.00781
+ ,0.0359
+ ,116.676
+ ,137.871
+ ,0.00997
+ ,0.00009
+ ,0.00698
+ ,0.03772
+ ,116.014
+ ,141.781
+ ,0.01284
+ ,0.00011
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+ ,0.04465
+ ,120.552
+ ,131.162
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+ ,0.0075
+ ,0.03243
+ ,120.267
+ ,137.244
+ ,0.00333
+ ,0.00003
+ ,0.00202
+ ,0.01351
+ ,107.332
+ ,113.84
+ ,0.0029
+ ,0.00003
+ ,0.00182
+ ,0.01256
+ ,95.73
+ ,132.068
+ ,0.00551
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+ ,0.00332
+ ,0.01717
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+ ,0.02214
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+ ,139.173
+ ,179.139
+ ,0.0039
+ ,0.00003
+ ,0.00208
+ ,0.01797
+ ,152.845
+ ,163.305
+ ,0.00294
+ ,0.00002
+ ,0.00149
+ ,0.01246
+ ,142.167
+ ,217.455
+ ,0.00369
+ ,0.00003
+ ,0.00203
+ ,0.01359
+ ,144.188
+ ,349.259
+ ,0.00544
+ ,0.00004
+ ,0.00292
+ ,0.02074
+ ,168.778
+ ,232.181
+ ,0.00718
+ ,0.00004
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+ ,0.0343
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+ ,175.829
+ ,0.00742
+ ,0.00005
+ ,0.00432
+ ,0.05767
+ ,156.405
+ ,189.398
+ ,0.00768
+ ,0.00005
+ ,0.00399
+ ,0.0431
+ ,153.848
+ ,165.738
+ ,0.0084
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+ ,0.0045
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+ ,0.04525
+ ,167.93
+ ,193.221
+ ,0.00442
+ ,0.00003
+ ,0.00247
+ ,0.04246
+ ,173.917
+ ,192.735
+ ,0.00476
+ ,0.00003
+ ,0.00258
+ ,0.03772
+ ,163.656
+ ,200.841
+ ,0.00742
+ ,0.00005
+ ,0.0039
+ ,0.01497
+ ,104.4
+ ,206.002
+ ,0.00633
+ ,0.00006
+ ,0.00375
+ ,0.0378
+ ,171.041
+ ,208.313
+ ,0.00455
+ ,0.00003
+ ,0.00234
+ ,0.01872
+ ,146.845
+ ,208.701
+ ,0.00496
+ ,0.00003
+ ,0.00275
+ ,0.01826
+ ,155.358
+ ,227.383
+ ,0.0031
+ ,0.00002
+ ,0.00176
+ ,0.01661
+ ,162.568
+ ,198.346
+ ,0.00502
+ ,0.00003
+ ,0.00253
+ ,0.01799
+ ,197.076
+ ,206.896
+ ,0.00289
+ ,0.00001
+ ,0.00168
+ ,0.00802
+ ,199.228
+ ,209.512
+ ,0.00241
+ ,0.00001
+ ,0.00138
+ ,0.00762
+ ,198.383
+ ,215.203
+ ,0.00212
+ ,0.00001
+ ,0.00135
+ ,0.00951
+ ,202.266
+ ,211.604
+ ,0.0018
+ ,0.000009
+ ,0.00107
+ ,0.00719
+ ,203.184
+ ,211.526
+ ,0.00178
+ ,0.000009
+ ,0.00106
+ ,0.00726
+ ,201.464
+ ,210.565
+ ,0.00198
+ ,0.00001
+ ,0.00115
+ ,0.00957
+ ,177.876
+ ,192.921
+ ,0.00411
+ ,0.00002
+ ,0.00241
+ ,0.01612
+ ,176.17
+ ,185.604
+ ,0.00369
+ ,0.00002
+ ,0.00218
+ ,0.01491
+ ,180.198
+ ,201.249
+ ,0.00284
+ ,0.00002
+ ,0.00166
+ ,0.0119
+ ,187.733
+ ,202.324
+ ,0.00316
+ ,0.00002
+ ,0.00182
+ ,0.01366
+ ,186.163
+ ,197.724
+ ,0.00298
+ ,0.00002
+ ,0.00175
+ ,0.01233
+ ,184.055
+ ,196.537
+ ,0.00258
+ ,0.00001
+ ,0.00147
+ ,0.01234
+ ,237.226
+ ,247.326
+ ,0.00298
+ ,0.00001
+ ,0.00182
+ ,0.01133
+ ,241.404
+ ,248.834
+ ,0.00281
+ ,0.00001
+ ,0.00173
+ ,0.01251
+ ,243.439
+ ,250.912
+ ,0.0021
+ ,0.000009
+ ,0.00137
+ ,0.01033
+ ,242.852
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+ ,245.51
+ ,262.09
+ ,0.00235
+ ,0.00001
+ ,0.00148
+ ,0.01149
+ ,252.455
+ ,261.487
+ ,0.00185
+ ,0.000007
+ ,0.00113
+ ,0.0086
+ ,122.188
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+ ,0.00004
+ ,0.00203
+ ,0.01433
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+ ,130.049
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+ ,0.00003
+ ,0.00155
+ ,0.014
+ ,124.445
+ ,135.069
+ ,0.00431
+ ,0.00003
+ ,0.00167
+ ,0.01685
+ ,126.344
+ ,134.231
+ ,0.00448
+ ,0.00004
+ ,0.00169
+ ,0.01614
+ ,128.001
+ ,138.052
+ ,0.00436
+ ,0.00003
+ ,0.00166
+ ,0.01677
+ ,129.336
+ ,139.867
+ ,0.0049
+ ,0.00004
+ ,0.00183
+ ,0.01947
+ ,108.807
+ ,134.656
+ ,0.00761
+ ,0.00007
+ ,0.00486
+ ,0.02067
+ ,109.86
+ ,126.358
+ ,0.00874
+ ,0.00008
+ ,0.00539
+ ,0.02454
+ ,110.417
+ ,131.067
+ ,0.00784
+ ,0.00007
+ ,0.00514
+ ,0.02802
+ ,117.274
+ ,129.916
+ ,0.00752
+ ,0.00006
+ ,0.00469
+ ,0.01948
+ ,116.879
+ ,131.897
+ ,0.00788
+ ,0.00007
+ ,0.00493
+ ,0.02137
+ ,114.847
+ ,271.314
+ ,0.00867
+ ,0.00008
+ ,0.0052
+ ,0.02519
+ ,209.144
+ ,237.494
+ ,0.00282
+ ,0.00001
+ ,0.00152
+ ,0.01382
+ ,223.365
+ ,238.987
+ ,0.00264
+ ,0.00001
+ ,0.00151
+ ,0.0134
+ ,222.236
+ ,231.345
+ ,0.00266
+ ,0.00001
+ ,0.00144
+ ,0.012
+ ,228.832
+ ,234.619
+ ,0.00296
+ ,0.00001
+ ,0.00155
+ ,0.01179
+ ,229.401
+ ,252.221
+ ,0.00205
+ ,0.000009
+ ,0.00113
+ ,0.01016
+ ,228.969
+ ,239.541
+ ,0.00238
+ ,0.00001
+ ,0.0014
+ ,0.01234
+ ,140.341
+ ,159.774
+ ,0.00817
+ ,0.00006
+ ,0.0044
+ ,0.02428
+ ,136.969
+ ,166.607
+ ,0.00923
+ ,0.00007
+ ,0.00463
+ ,0.02603
+ ,143.533
+ ,162.215
+ ,0.01101
+ ,0.00008
+ ,0.00467
+ ,0.03392
+ ,148.09
+ ,162.824
+ ,0.00762
+ ,0.00005
+ ,0.00354
+ ,0.03635
+ ,142.729
+ ,162.408
+ ,0.00831
+ ,0.00006
+ ,0.00419
+ ,0.02949
+ ,136.358
+ ,176.595
+ ,0.00971
+ ,0.00007
+ ,0.00478
+ ,0.03736
+ ,120.08
+ ,139.71
+ ,0.00405
+ ,0.00003
+ ,0.0022
+ ,0.01345
+ ,112.014
+ ,588.518
+ ,0.00533
+ ,0.00005
+ ,0.00329
+ ,0.01956
+ ,110.793
+ ,128.101
+ ,0.00494
+ ,0.00004
+ ,0.00283
+ ,0.01831
+ ,110.707
+ ,122.611
+ ,0.00516
+ ,0.00005
+ ,0.00289
+ ,0.01715
+ ,112.876
+ ,148.826
+ ,0.005
+ ,0.00004
+ ,0.00289
+ ,0.02704
+ ,110.568
+ ,125.394
+ ,0.00462
+ ,0.00004
+ ,0.0028
+ ,0.01636
+ ,95.385
+ ,102.145
+ ,0.00608
+ ,0.00006
+ ,0.00332
+ ,0.02455
+ ,100.77
+ ,115.697
+ ,0.01038
+ ,0.0001
+ ,0.00576
+ ,0.02139
+ ,96.106
+ ,108.664
+ ,0.00694
+ ,0.00007
+ ,0.00415
+ ,0.02876
+ ,95.605
+ ,107.715
+ ,0.00702
+ ,0.00007
+ ,0.00371
+ ,0.0219
+ ,100.96
+ ,110.019
+ ,0.00606
+ ,0.00006
+ ,0.00348
+ ,0.01751
+ ,98.804
+ ,102.305
+ ,0.00432
+ ,0.00004
+ ,0.00258
+ ,0.01552
+ ,176.858
+ ,205.56
+ ,0.00747
+ ,0.00004
+ ,0.0042
+ ,0.0351
+ ,180.978
+ ,200.125
+ ,0.00406
+ ,0.00002
+ ,0.00244
+ ,0.02877
+ ,178.222
+ ,202.45
+ ,0.00321
+ ,0.00002
+ ,0.00194
+ ,0.02784
+ ,176.281
+ ,227.381
+ ,0.0052
+ ,0.00003
+ ,0.00312
+ ,0.04683
+ ,173.898
+ ,211.35
+ ,0.00448
+ ,0.00003
+ ,0.00254
+ ,0.04802
+ ,179.711
+ ,225.93
+ ,0.00709
+ ,0.00004
+ ,0.00419
+ ,0.03455
+ ,166.605
+ ,206.008
+ ,0.00742
+ ,0.00004
+ ,0.00453
+ ,0.05114
+ ,151.955
+ ,163.335
+ ,0.00419
+ ,0.00003
+ ,0.00227
+ ,0.0569
+ ,148.272
+ ,164.989
+ ,0.00459
+ ,0.00003
+ ,0.00256
+ ,0.03051
+ ,152.125
+ ,161.469
+ ,0.00382
+ ,0.00003
+ ,0.00226
+ ,0.04398
+ ,157.821
+ ,172.975
+ ,0.00358
+ ,0.00002
+ ,0.00196
+ ,0.02764
+ ,157.447
+ ,163.267
+ ,0.00369
+ ,0.00002
+ ,0.00197
+ ,0.02571
+ ,159.116
+ ,168.913
+ ,0.00342
+ ,0.00002
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+ ,0.02809
+ ,125.036
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+ ,0.03088
+ ,125.791
+ ,140.557
+ ,0.01378
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+ ,0.00655
+ ,0.03908
+ ,126.512
+ ,141.756
+ ,0.01936
+ ,0.00015
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+ ,125.641
+ ,141.068
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+ ,0.01522
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+ ,128.451
+ ,150.449
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+ ,586.567
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+ ,163.736
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+ ,0.00534
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+ ,0.00495
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+ ,0.00203
+ ,0.01148
+ ,117.004
+ ,144.466
+ ,0.00353
+ ,0.00003
+ ,0.00218
+ ,0.01318
+ ,115.38
+ ,123.109
+ ,0.00332
+ ,0.00003
+ ,0.00199
+ ,0.01133
+ ,116.388
+ ,129.038
+ ,0.00346
+ ,0.00003
+ ,0.00213
+ ,0.01331
+ ,151.737
+ ,190.204
+ ,0.00314
+ ,0.00002
+ ,0.00162
+ ,0.0123
+ ,148.79
+ ,158.359
+ ,0.00309
+ ,0.00002
+ ,0.00186
+ ,0.01309
+ ,148.143
+ ,155.982
+ ,0.00392
+ ,0.00003
+ ,0.00231
+ ,0.01263
+ ,150.44
+ ,163.441
+ ,0.00396
+ ,0.00003
+ ,0.00233
+ ,0.02148
+ ,148.462
+ ,161.078
+ ,0.00397
+ ,0.00003
+ ,0.00235
+ ,0.01559
+ ,149.818
+ ,163.417
+ ,0.00336
+ ,0.00002
+ ,0.00198
+ ,0.01666
+ ,117.226
+ ,123.925
+ ,0.00417
+ ,0.00004
+ ,0.0027
+ ,0.01949
+ ,116.848
+ ,217.552
+ ,0.00531
+ ,0.00005
+ ,0.00346
+ ,0.01756
+ ,116.286
+ ,177.291
+ ,0.00314
+ ,0.00003
+ ,0.00192
+ ,0.01691
+ ,116.556
+ ,592.03
+ ,0.00496
+ ,0.00004
+ ,0.00263
+ ,0.01491
+ ,116.342
+ ,581.289
+ ,0.00267
+ ,0.00002
+ ,0.00148
+ ,0.01144
+ ,114.563
+ ,119.167
+ ,0.00327
+ ,0.00003
+ ,0.00184
+ ,0.01095
+ ,201.774
+ ,262.707
+ ,0.00694
+ ,0.00003
+ ,0.00396
+ ,0.01758
+ ,174.188
+ ,230.978
+ ,0.00459
+ ,0.00003
+ ,0.00259
+ ,0.02745
+ ,209.516
+ ,253.017
+ ,0.00564
+ ,0.00003
+ ,0.00292
+ ,0.01879
+ ,174.688
+ ,240.005
+ ,0.0136
+ ,0.00008
+ ,0.00564
+ ,0.01667
+ ,198.764
+ ,396.961
+ ,0.0074
+ ,0.00004
+ ,0.0039
+ ,0.01588
+ ,214.289
+ ,260.277
+ ,0.00567
+ ,0.00003
+ ,0.00317
+ ,0.01373)
+ ,dim=c(6
+ ,195)
+ ,dimnames=list(c('MDVP:Fo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Jitter(%)'
+ ,'MDVP:Jitter(Abs)'
+ ,'MDVP:PPQ'
+ ,'MDVP:APQ')
+ ,1:195))
> y <- array(NA,dim=c(6,195),dimnames=list(c('MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:PPQ','MDVP:APQ'),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
MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Jitter(%) MDVP:Jitter(Abs) MDVP:PPQ MDVP:APQ
1 119.992 157.302 0.00784 7.0e-05 0.00554 0.02971
2 122.400 148.650 0.00968 8.0e-05 0.00696 0.04368
3 116.682 131.111 0.01050 9.0e-05 0.00781 0.03590
4 116.676 137.871 0.00997 9.0e-05 0.00698 0.03772
5 116.014 141.781 0.01284 1.1e-04 0.00908 0.04465
6 120.552 131.162 0.00968 8.0e-05 0.00750 0.03243
7 120.267 137.244 0.00333 3.0e-05 0.00202 0.01351
8 107.332 113.840 0.00290 3.0e-05 0.00182 0.01256
9 95.730 132.068 0.00551 6.0e-05 0.00332 0.01717
10 95.056 120.103 0.00532 6.0e-05 0.00332 0.02444
11 88.333 112.240 0.00505 6.0e-05 0.00330 0.01892
12 91.904 115.871 0.00540 6.0e-05 0.00336 0.02214
13 136.926 159.866 0.00293 2.0e-05 0.00153 0.01140
14 139.173 179.139 0.00390 3.0e-05 0.00208 0.01797
15 152.845 163.305 0.00294 2.0e-05 0.00149 0.01246
16 142.167 217.455 0.00369 3.0e-05 0.00203 0.01359
17 144.188 349.259 0.00544 4.0e-05 0.00292 0.02074
18 168.778 232.181 0.00718 4.0e-05 0.00387 0.03430
19 153.046 175.829 0.00742 5.0e-05 0.00432 0.05767
20 156.405 189.398 0.00768 5.0e-05 0.00399 0.04310
21 153.848 165.738 0.00840 5.0e-05 0.00450 0.04055
22 153.880 172.860 0.00480 3.0e-05 0.00267 0.04525
23 167.930 193.221 0.00442 3.0e-05 0.00247 0.04246
24 173.917 192.735 0.00476 3.0e-05 0.00258 0.03772
25 163.656 200.841 0.00742 5.0e-05 0.00390 0.01497
26 104.400 206.002 0.00633 6.0e-05 0.00375 0.03780
27 171.041 208.313 0.00455 3.0e-05 0.00234 0.01872
28 146.845 208.701 0.00496 3.0e-05 0.00275 0.01826
29 155.358 227.383 0.00310 2.0e-05 0.00176 0.01661
30 162.568 198.346 0.00502 3.0e-05 0.00253 0.01799
31 197.076 206.896 0.00289 1.0e-05 0.00168 0.00802
32 199.228 209.512 0.00241 1.0e-05 0.00138 0.00762
33 198.383 215.203 0.00212 1.0e-05 0.00135 0.00951
34 202.266 211.604 0.00180 9.0e-06 0.00107 0.00719
35 203.184 211.526 0.00178 9.0e-06 0.00106 0.00726
36 201.464 210.565 0.00198 1.0e-05 0.00115 0.00957
37 177.876 192.921 0.00411 2.0e-05 0.00241 0.01612
38 176.170 185.604 0.00369 2.0e-05 0.00218 0.01491
39 180.198 201.249 0.00284 2.0e-05 0.00166 0.01190
40 187.733 202.324 0.00316 2.0e-05 0.00182 0.01366
41 186.163 197.724 0.00298 2.0e-05 0.00175 0.01233
42 184.055 196.537 0.00258 1.0e-05 0.00147 0.01234
43 237.226 247.326 0.00298 1.0e-05 0.00182 0.01133
44 241.404 248.834 0.00281 1.0e-05 0.00173 0.01251
45 243.439 250.912 0.00210 9.0e-06 0.00137 0.01033
46 242.852 255.034 0.00225 9.0e-06 0.00139 0.01014
47 245.510 262.090 0.00235 1.0e-05 0.00148 0.01149
48 252.455 261.487 0.00185 7.0e-06 0.00113 0.00860
49 122.188 128.611 0.00524 4.0e-05 0.00203 0.01433
50 122.964 130.049 0.00428 3.0e-05 0.00155 0.01400
51 124.445 135.069 0.00431 3.0e-05 0.00167 0.01685
52 126.344 134.231 0.00448 4.0e-05 0.00169 0.01614
53 128.001 138.052 0.00436 3.0e-05 0.00166 0.01677
54 129.336 139.867 0.00490 4.0e-05 0.00183 0.01947
55 108.807 134.656 0.00761 7.0e-05 0.00486 0.02067
56 109.860 126.358 0.00874 8.0e-05 0.00539 0.02454
57 110.417 131.067 0.00784 7.0e-05 0.00514 0.02802
58 117.274 129.916 0.00752 6.0e-05 0.00469 0.01948
59 116.879 131.897 0.00788 7.0e-05 0.00493 0.02137
60 114.847 271.314 0.00867 8.0e-05 0.00520 0.02519
61 209.144 237.494 0.00282 1.0e-05 0.00152 0.01382
62 223.365 238.987 0.00264 1.0e-05 0.00151 0.01340
63 222.236 231.345 0.00266 1.0e-05 0.00144 0.01200
64 228.832 234.619 0.00296 1.0e-05 0.00155 0.01179
65 229.401 252.221 0.00205 9.0e-06 0.00113 0.01016
66 228.969 239.541 0.00238 1.0e-05 0.00140 0.01234
67 140.341 159.774 0.00817 6.0e-05 0.00440 0.02428
68 136.969 166.607 0.00923 7.0e-05 0.00463 0.02603
69 143.533 162.215 0.01101 8.0e-05 0.00467 0.03392
70 148.090 162.824 0.00762 5.0e-05 0.00354 0.03635
71 142.729 162.408 0.00831 6.0e-05 0.00419 0.02949
72 136.358 176.595 0.00971 7.0e-05 0.00478 0.03736
73 120.080 139.710 0.00405 3.0e-05 0.00220 0.01345
74 112.014 588.518 0.00533 5.0e-05 0.00329 0.01956
75 110.793 128.101 0.00494 4.0e-05 0.00283 0.01831
76 110.707 122.611 0.00516 5.0e-05 0.00289 0.01715
77 112.876 148.826 0.00500 4.0e-05 0.00289 0.02704
78 110.568 125.394 0.00462 4.0e-05 0.00280 0.01636
79 95.385 102.145 0.00608 6.0e-05 0.00332 0.02455
80 100.770 115.697 0.01038 1.0e-04 0.00576 0.02139
81 96.106 108.664 0.00694 7.0e-05 0.00415 0.02876
82 95.605 107.715 0.00702 7.0e-05 0.00371 0.02190
83 100.960 110.019 0.00606 6.0e-05 0.00348 0.01751
84 98.804 102.305 0.00432 4.0e-05 0.00258 0.01552
85 176.858 205.560 0.00747 4.0e-05 0.00420 0.03510
86 180.978 200.125 0.00406 2.0e-05 0.00244 0.02877
87 178.222 202.450 0.00321 2.0e-05 0.00194 0.02784
88 176.281 227.381 0.00520 3.0e-05 0.00312 0.04683
89 173.898 211.350 0.00448 3.0e-05 0.00254 0.04802
90 179.711 225.930 0.00709 4.0e-05 0.00419 0.03455
91 166.605 206.008 0.00742 4.0e-05 0.00453 0.05114
92 151.955 163.335 0.00419 3.0e-05 0.00227 0.05690
93 148.272 164.989 0.00459 3.0e-05 0.00256 0.03051
94 152.125 161.469 0.00382 3.0e-05 0.00226 0.04398
95 157.821 172.975 0.00358 2.0e-05 0.00196 0.02764
96 157.447 163.267 0.00369 2.0e-05 0.00197 0.02571
97 159.116 168.913 0.00342 2.0e-05 0.00184 0.02809
98 125.036 143.946 0.01280 1.0e-04 0.00623 0.03088
99 125.791 140.557 0.01378 1.1e-04 0.00655 0.03908
100 126.512 141.756 0.01936 1.5e-04 0.00990 0.05783
101 125.641 141.068 0.03316 2.6e-04 0.01522 0.06196
102 128.451 150.449 0.01551 1.2e-04 0.00909 0.05174
103 139.224 586.567 0.03011 2.2e-04 0.01628 0.06023
104 150.258 154.609 0.00248 2.0e-05 0.00136 0.01009
105 154.003 160.267 0.00183 1.0e-05 0.00100 0.00871
106 149.689 160.368 0.00257 2.0e-05 0.00134 0.01059
107 155.078 163.736 0.00168 1.0e-05 0.00092 0.00928
108 151.884 157.765 0.00258 2.0e-05 0.00122 0.01267
109 151.989 157.339 0.00174 1.0e-05 0.00096 0.00993
110 193.030 208.900 0.00766 4.0e-05 0.00389 0.02084
111 200.714 223.982 0.00621 3.0e-05 0.00337 0.01852
112 208.519 220.315 0.00609 3.0e-05 0.00339 0.01307
113 204.664 221.300 0.00841 4.0e-05 0.00485 0.01767
114 210.141 232.706 0.00534 3.0e-05 0.00280 0.01301
115 206.327 226.355 0.00495 2.0e-05 0.00246 0.01604
116 151.872 492.892 0.00856 6.0e-05 0.00385 0.01271
117 158.219 442.557 0.00476 3.0e-05 0.00207 0.01312
118 170.756 450.247 0.00555 3.0e-05 0.00261 0.01652
119 178.285 442.824 0.00462 3.0e-05 0.00194 0.01151
120 217.116 233.481 0.00404 2.0e-05 0.00128 0.01075
121 128.940 479.697 0.00581 5.0e-05 0.00314 0.01734
122 176.824 215.293 0.00460 3.0e-05 0.00221 0.01104
123 138.190 203.522 0.00704 5.0e-05 0.00398 0.03220
124 182.018 197.173 0.00842 5.0e-05 0.00449 0.01931
125 156.239 195.107 0.00694 4.0e-05 0.00395 0.01720
126 145.174 198.109 0.00733 5.0e-05 0.00422 0.01944
127 138.145 197.238 0.00544 4.0e-05 0.00327 0.02259
128 166.888 198.966 0.00638 4.0e-05 0.00351 0.02301
129 119.031 127.533 0.00440 4.0e-05 0.00192 0.00811
130 120.078 126.632 0.00270 2.0e-05 0.00135 0.00903
131 120.289 128.143 0.00492 4.0e-05 0.00238 0.01194
132 120.256 125.306 0.00407 3.0e-05 0.00205 0.01310
133 119.056 125.213 0.00346 3.0e-05 0.00170 0.00915
134 118.747 123.723 0.00331 3.0e-05 0.00171 0.00903
135 106.516 112.777 0.00589 6.0e-05 0.00319 0.03651
136 110.453 127.611 0.00494 4.0e-05 0.00315 0.03316
137 113.400 133.344 0.00451 4.0e-05 0.00283 0.04370
138 113.166 130.270 0.00502 4.0e-05 0.00312 0.04134
139 112.239 126.609 0.00472 4.0e-05 0.00290 0.04451
140 116.150 131.731 0.00381 3.0e-05 0.00232 0.02770
141 170.368 268.796 0.00571 3.0e-05 0.00269 0.02824
142 208.083 253.792 0.00757 4.0e-05 0.00428 0.04464
143 198.458 219.290 0.00376 2.0e-05 0.00215 0.02530
144 202.805 231.508 0.00370 2.0e-05 0.00211 0.01506
145 202.544 241.350 0.00254 1.0e-05 0.00133 0.02006
146 223.361 263.872 0.00352 2.0e-05 0.00188 0.01909
147 169.774 191.759 0.01568 9.0e-05 0.00946 0.08808
148 183.520 216.814 0.01466 8.0e-05 0.00819 0.06359
149 188.620 216.302 0.01719 9.0e-05 0.01027 0.06824
150 202.632 565.740 0.01627 8.0e-05 0.00963 0.06460
151 186.695 211.961 0.01872 1.0e-04 0.01154 0.06259
152 192.818 224.429 0.03107 1.6e-04 0.01958 0.13778
153 198.116 233.099 0.02714 1.4e-04 0.01699 0.08318
154 121.345 139.644 0.00684 6.0e-05 0.00332 0.02056
155 119.100 128.442 0.00692 6.0e-05 0.00300 0.02018
156 117.870 127.349 0.00647 5.0e-05 0.00300 0.02402
157 122.336 142.369 0.00727 6.0e-05 0.00339 0.01771
158 117.963 134.209 0.01813 1.5e-04 0.00718 0.02916
159 126.144 154.284 0.00975 8.0e-05 0.00454 0.02157
160 127.930 138.752 0.00605 5.0e-05 0.00318 0.03105
161 114.238 124.393 0.00581 5.0e-05 0.00316 0.04114
162 115.322 135.738 0.00619 5.0e-05 0.00329 0.02931
163 114.554 126.778 0.00651 6.0e-05 0.00340 0.03091
164 112.150 131.669 0.00519 5.0e-05 0.00284 0.01363
165 102.273 142.830 0.00907 9.0e-05 0.00461 0.02073
166 236.200 244.663 0.00277 1.0e-05 0.00153 0.01621
167 237.323 243.709 0.00303 1.0e-05 0.00159 0.00882
168 260.105 264.919 0.00339 1.0e-05 0.00186 0.01367
169 197.569 217.627 0.00803 4.0e-05 0.00448 0.01439
170 240.301 245.135 0.00517 2.0e-05 0.00283 0.01344
171 244.990 272.210 0.00451 2.0e-05 0.00237 0.01255
172 112.547 133.374 0.00355 3.0e-05 0.00190 0.01140
173 110.739 113.597 0.00356 3.0e-05 0.00200 0.01285
174 113.715 116.443 0.00349 3.0e-05 0.00203 0.01148
175 117.004 144.466 0.00353 3.0e-05 0.00218 0.01318
176 115.380 123.109 0.00332 3.0e-05 0.00199 0.01133
177 116.388 129.038 0.00346 3.0e-05 0.00213 0.01331
178 151.737 190.204 0.00314 2.0e-05 0.00162 0.01230
179 148.790 158.359 0.00309 2.0e-05 0.00186 0.01309
180 148.143 155.982 0.00392 3.0e-05 0.00231 0.01263
181 150.440 163.441 0.00396 3.0e-05 0.00233 0.02148
182 148.462 161.078 0.00397 3.0e-05 0.00235 0.01559
183 149.818 163.417 0.00336 2.0e-05 0.00198 0.01666
184 117.226 123.925 0.00417 4.0e-05 0.00270 0.01949
185 116.848 217.552 0.00531 5.0e-05 0.00346 0.01756
186 116.286 177.291 0.00314 3.0e-05 0.00192 0.01691
187 116.556 592.030 0.00496 4.0e-05 0.00263 0.01491
188 116.342 581.289 0.00267 2.0e-05 0.00148 0.01144
189 114.563 119.167 0.00327 3.0e-05 0.00184 0.01095
190 201.774 262.707 0.00694 3.0e-05 0.00396 0.01758
191 174.188 230.978 0.00459 3.0e-05 0.00259 0.02745
192 209.516 253.017 0.00564 3.0e-05 0.00292 0.01879
193 174.688 240.005 0.01360 8.0e-05 0.00564 0.01667
194 198.764 396.961 0.00740 4.0e-05 0.00390 0.01588
195 214.289 260.277 0.00567 3.0e-05 0.00317 0.01373
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `MDVP:Fhi(Hz)` `MDVP:Jitter(%)` `MDVP:Jitter(Abs)`
1.602e+02 5.493e-02 1.984e+04 -2.614e+06
`MDVP:PPQ` `MDVP:APQ`
-3.404e+03 -5.641e+02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-65.010 -16.081 -3.418 16.635 68.162
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.602e+02 5.425e+00 29.538 < 2e-16 ***
`MDVP:Fhi(Hz)` 5.493e-02 2.126e-02 2.584 0.01053 *
`MDVP:Jitter(%)` 1.984e+04 2.130e+03 9.311 < 2e-16 ***
`MDVP:Jitter(Abs)` -2.614e+06 1.626e+05 -16.073 < 2e-16 ***
`MDVP:PPQ` -3.404e+03 3.168e+03 -1.075 0.28397
`MDVP:APQ` -5.641e+02 1.859e+02 -3.034 0.00275 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 24.52 on 189 degrees of freedom
Multiple R-squared: 0.658, Adjusted R-squared: 0.6489
F-statistic: 72.72 on 5 and 189 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,] 7.742618e-03 1.548524e-02 0.992257382
[2,] 1.133007e-03 2.266014e-03 0.998866993
[3,] 1.554571e-04 3.109141e-04 0.999844543
[4,] 2.224004e-05 4.448009e-05 0.999977760
[5,] 3.147293e-06 6.294586e-06 0.999996853
[6,] 3.736279e-07 7.472558e-07 0.999999626
[7,] 1.011614e-05 2.023229e-05 0.999989884
[8,] 2.383327e-06 4.766655e-06 0.999997617
[9,] 6.021025e-06 1.204205e-05 0.999993979
[10,] 3.727243e-06 7.454485e-06 0.999996273
[11,] 9.480800e-07 1.896160e-06 0.999999052
[12,] 2.348330e-07 4.696660e-07 0.999999765
[13,] 2.704085e-07 5.408171e-07 0.999999730
[14,] 6.811517e-08 1.362303e-07 0.999999932
[15,] 2.733069e-07 5.466138e-07 0.999999727
[16,] 6.953092e-07 1.390618e-06 0.999999305
[17,] 2.417786e-06 4.835573e-06 0.999997582
[18,] 2.222969e-06 4.445938e-06 0.999997777
[19,] 4.896584e-06 9.793169e-06 0.999995103
[20,] 3.667619e-06 7.335238e-06 0.999996332
[21,] 1.464885e-06 2.929771e-06 0.999998535
[22,] 6.121011e-07 1.224202e-06 0.999999388
[23,] 2.253686e-06 4.507372e-06 0.999997746
[24,] 1.165909e-05 2.331818e-05 0.999988341
[25,] 2.843617e-05 5.687234e-05 0.999971564
[26,] 9.026566e-05 1.805313e-04 0.999909734
[27,] 1.781693e-04 3.563385e-04 0.999821831
[28,] 2.257628e-04 4.515257e-04 0.999774237
[29,] 1.347142e-04 2.694284e-04 0.999865286
[30,] 7.181644e-05 1.436329e-04 0.999928184
[31,] 5.688418e-05 1.137684e-04 0.999943116
[32,] 5.084805e-05 1.016961e-04 0.999949152
[33,] 4.656456e-05 9.312913e-05 0.999953435
[34,] 2.621021e-05 5.242041e-05 0.999973790
[35,] 7.564868e-05 1.512974e-04 0.999924351
[36,] 2.710198e-04 5.420396e-04 0.999728980
[37,] 1.390269e-03 2.780537e-03 0.998609731
[38,] 3.727365e-03 7.454730e-03 0.996272635
[39,] 9.025036e-03 1.805007e-02 0.990974964
[40,] 2.754101e-02 5.508202e-02 0.972458988
[41,] 3.126587e-02 6.253175e-02 0.968734126
[42,] 2.841380e-02 5.682761e-02 0.971586197
[43,] 2.540999e-02 5.081997e-02 0.974590014
[44,] 3.054359e-02 6.108718e-02 0.969456412
[45,] 2.757052e-02 5.514103e-02 0.972429484
[46,] 2.976922e-02 5.953843e-02 0.970230784
[47,] 2.381232e-02 4.762464e-02 0.976187678
[48,] 2.689654e-02 5.379309e-02 0.973103456
[49,] 2.151445e-02 4.302890e-02 0.978485551
[50,] 1.664632e-02 3.329265e-02 0.983353676
[51,] 1.439634e-02 2.879268e-02 0.985603659
[52,] 1.223246e-02 2.446492e-02 0.987767539
[53,] 9.567530e-03 1.913506e-02 0.990432470
[54,] 1.025131e-02 2.050261e-02 0.989748693
[55,] 1.107461e-02 2.214921e-02 0.988925395
[56,] 1.270013e-02 2.540026e-02 0.987299870
[57,] 2.033875e-02 4.067751e-02 0.979661245
[58,] 3.155455e-02 6.310910e-02 0.968445450
[59,] 2.759391e-02 5.518781e-02 0.972406093
[60,] 3.293675e-02 6.587351e-02 0.967063246
[61,] 8.243488e-02 1.648698e-01 0.917565119
[62,] 7.282855e-02 1.456571e-01 0.927171454
[63,] 6.124937e-02 1.224987e-01 0.938750628
[64,] 5.219171e-02 1.043834e-01 0.947808288
[65,] 6.685741e-02 1.337148e-01 0.933142585
[66,] 3.506688e-01 7.013377e-01 0.649331154
[67,] 3.808737e-01 7.617475e-01 0.619126252
[68,] 3.404152e-01 6.808304e-01 0.659584778
[69,] 3.558338e-01 7.116675e-01 0.644166249
[70,] 3.594836e-01 7.189673e-01 0.640516351
[71,] 3.257554e-01 6.515107e-01 0.674244650
[72,] 4.623153e-01 9.246305e-01 0.537684747
[73,] 4.487820e-01 8.975639e-01 0.551218038
[74,] 4.252787e-01 8.505575e-01 0.574721264
[75,] 3.862140e-01 7.724280e-01 0.613786019
[76,] 4.033774e-01 8.067547e-01 0.596622646
[77,] 3.798273e-01 7.596546e-01 0.620172704
[78,] 3.474733e-01 6.949466e-01 0.652526712
[79,] 3.268039e-01 6.536078e-01 0.673196107
[80,] 2.969276e-01 5.938553e-01 0.703072353
[81,] 2.963850e-01 5.927700e-01 0.703614994
[82,] 2.708282e-01 5.416564e-01 0.729171786
[83,] 2.561149e-01 5.122298e-01 0.743885085
[84,] 2.350927e-01 4.701854e-01 0.764907318
[85,] 2.118279e-01 4.236559e-01 0.788172059
[86,] 1.978947e-01 3.957894e-01 0.802105282
[87,] 1.807944e-01 3.615888e-01 0.819205586
[88,] 1.676331e-01 3.352662e-01 0.832366904
[89,] 1.460347e-01 2.920694e-01 0.853965323
[90,] 1.515168e-01 3.030336e-01 0.848483197
[91,] 1.857616e-01 3.715233e-01 0.814238357
[92,] 2.461898e-01 4.923796e-01 0.753810218
[93,] 4.318362e-01 8.636723e-01 0.568163849
[94,] 4.884749e-01 9.769499e-01 0.511525067
[95,] 7.601861e-01 4.796278e-01 0.239813925
[96,] 7.270356e-01 5.459287e-01 0.272964364
[97,] 7.170496e-01 5.659007e-01 0.282950358
[98,] 6.830277e-01 6.339446e-01 0.316972284
[99,] 6.609441e-01 6.781118e-01 0.339055922
[100,] 6.236341e-01 7.527318e-01 0.376365904
[101,] 6.117006e-01 7.765987e-01 0.388299350
[102,] 5.819926e-01 8.360148e-01 0.418007423
[103,] 5.437597e-01 9.124805e-01 0.456240274
[104,] 5.134757e-01 9.730485e-01 0.486524262
[105,] 4.934614e-01 9.869228e-01 0.506538594
[106,] 5.059755e-01 9.880491e-01 0.494024545
[107,] 4.652890e-01 9.305780e-01 0.534711020
[108,] 4.837355e-01 9.674710e-01 0.516264507
[109,] 5.004150e-01 9.991700e-01 0.499584985
[110,] 5.291816e-01 9.416367e-01 0.470818373
[111,] 4.874224e-01 9.748447e-01 0.512577647
[112,] 4.739509e-01 9.479018e-01 0.526049076
[113,] 4.435686e-01 8.871372e-01 0.556431415
[114,] 4.041042e-01 8.082083e-01 0.595895829
[115,] 3.719478e-01 7.438955e-01 0.628052229
[116,] 3.385538e-01 6.771077e-01 0.661446172
[117,] 3.494634e-01 6.989268e-01 0.650536596
[118,] 3.255912e-01 6.511823e-01 0.674408841
[119,] 2.948665e-01 5.897329e-01 0.705133528
[120,] 2.600830e-01 5.201660e-01 0.739917024
[121,] 2.372059e-01 4.744118e-01 0.762794081
[122,] 3.021812e-01 6.043625e-01 0.697818767
[123,] 2.961978e-01 5.923955e-01 0.703802244
[124,] 3.497592e-01 6.995185e-01 0.650240768
[125,] 3.553327e-01 7.106654e-01 0.644667319
[126,] 3.464853e-01 6.929706e-01 0.653514684
[127,] 3.453211e-01 6.906422e-01 0.654678894
[128,] 3.262184e-01 6.524368e-01 0.673781620
[129,] 2.868477e-01 5.736954e-01 0.713152299
[130,] 2.638636e-01 5.277273e-01 0.736136367
[131,] 2.330117e-01 4.660234e-01 0.766988279
[132,] 2.420313e-01 4.840625e-01 0.757968738
[133,] 2.618366e-01 5.236731e-01 0.738163439
[134,] 2.413290e-01 4.826581e-01 0.758670960
[135,] 2.262571e-01 4.525143e-01 0.773742866
[136,] 2.221195e-01 4.442391e-01 0.777880471
[137,] 1.965454e-01 3.930908e-01 0.803454618
[138,] 3.076368e-01 6.152735e-01 0.692363238
[139,] 2.935069e-01 5.870138e-01 0.706493112
[140,] 2.745167e-01 5.490335e-01 0.725483251
[141,] 2.726293e-01 5.452586e-01 0.727370689
[142,] 2.937924e-01 5.875848e-01 0.706207612
[143,] 2.741548e-01 5.483095e-01 0.725845244
[144,] 2.684344e-01 5.368687e-01 0.731565631
[145,] 3.386082e-01 6.772164e-01 0.661391824
[146,] 2.955776e-01 5.911553e-01 0.704422374
[147,] 2.523804e-01 5.047608e-01 0.747619606
[148,] 2.590654e-01 5.181308e-01 0.740934587
[149,] 2.201303e-01 4.402605e-01 0.779869746
[150,] 2.714873e-01 5.429746e-01 0.728512676
[151,] 2.584180e-01 5.168360e-01 0.741582011
[152,] 2.152688e-01 4.305375e-01 0.784731232
[153,] 1.951606e-01 3.903213e-01 0.804839366
[154,] 1.993361e-01 3.986722e-01 0.800663901
[155,] 1.613170e-01 3.226341e-01 0.838682956
[156,] 1.427334e-01 2.854668e-01 0.857266586
[157,] 7.165243e-01 5.669513e-01 0.283475660
[158,] 7.377488e-01 5.245024e-01 0.262251210
[159,] 7.869703e-01 4.260594e-01 0.213029686
[160,] 9.061278e-01 1.877444e-01 0.093872190
[161,] 9.186145e-01 1.627710e-01 0.081385500
[162,] 9.114638e-01 1.770725e-01 0.088536245
[163,] 9.964767e-01 7.046574e-03 0.003523287
[164,] 9.941990e-01 1.160196e-02 0.005800979
[165,] 9.923105e-01 1.537907e-02 0.007689536
[166,] 9.880353e-01 2.392945e-02 0.011964724
[167,] 9.834442e-01 3.311160e-02 0.016555798
[168,] 9.726364e-01 5.472717e-02 0.027363585
[169,] 9.632779e-01 7.344429e-02 0.036722145
[170,] 9.401669e-01 1.196662e-01 0.059833120
[171,] 9.074477e-01 1.851045e-01 0.092552252
[172,] 8.653451e-01 2.693098e-01 0.134654899
[173,] 7.950623e-01 4.098754e-01 0.204937701
[174,] 7.079480e-01 5.841039e-01 0.292051953
[175,] 6.612064e-01 6.775872e-01 0.338793598
[176,] 5.407257e-01 9.185487e-01 0.459274341
[177,] 4.042073e-01 8.084145e-01 0.595792744
[178,] 2.767925e-01 5.535849e-01 0.723207529
> postscript(file="/var/fisher/rcomp/tmp/1o66a1386011511.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/29jgk1386011511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3akny1386011511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4otza1386011511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/59zdl1386011511.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
14.19058595 19.42789024 23.05010272 31.38668939 36.91416924 14.03273231
7 8 9 10 11 12
-20.64367659 -24.98071397 -3.23684461 4.61594304 0.49852305 -1.05165302
13 14 15 16 17 18
-26.28819193 -12.62487029 -10.29467411 -10.21094960 -16.94154396 -9.55121796
19 20 21 22 23 24
13.90288529 2.01735770 -13.22375423 1.97194037 20.18634331 17.15675200
25 26 27 28 29 30
-2.37706238 -1.79189675 6.05591578 -25.15763494 -11.21486998 -10.95718707
31 32 33 34 35 36
4.53919379 14.82155055 20.38017451 25.93262455 27.25705834 25.84588168
37 38 39 40 41 42
-4.90128953 0.65992364 17.22050909 19.88663260 21.15112020 -0.04191455
43 44 45 46 47 48
43.02701463 50.85342367 61.78843778 57.96064916 61.92912528 68.16217727
49 50 51 52 53 54
-29.50038703 -37.71848396 -35.09214210 -10.71470391 -32.77091926 -14.00809700
55 56 57 58 59 60
1.39745269 10.61642187 3.74165027 -15.47679781 4.89862923 8.74975239
61 62 63 64 65 66
19.04203031 36.48041046 34.34645293 35.06804551 47.75741844 46.23876098
67 68 69 70 71 72
-5.22230468 -2.08806262 0.13405883 -8.98731387 -3.53190246 -5.86689998
73 74 75 76 77 78
-34.66860192 -33.34474463 -29.94845781 -8.40993393 -25.06523609 -24.87958145
79 80 81 82 83 84
-9.08140748 21.33850230 5.56024400 -1.84287511 -6.96869113 -30.64753473
85 86 87 88 89 90
-4.18659827 6.03456281 17.78415375 15.86727050 27.34305079 4.74064000
91 92 93 94 95 96
-3.30117988 17.87954749 -7.72737852 18.16907360 -8.38164295 -11.45892783
97 98 99 100 101 102
-3.84454717 3.00468388 16.35892106 32.86106429 66.24612247 26.08519158
103 104 105 106 107 108
13.94186698 -5.05923298 -16.87284008 -7.51576965 -12.96389783 -4.61134596
109 110 111 112 113 114
-16.38882444 -1.06586193 5.33519070 12.71566160 -3.50958196 26.49121565
115 116 117 118 119 120
5.17675856 -28.12295799 -27.87580234 -27.67492183 -6.39824464 26.62355533
121 122 123 124 125 126
-21.72532271 5.68906355 -10.46143656 0.80772466 -24.66690079 -15.31305861
127 128 129 130 131 132
-12.39948003 -1.34261420 -19.81963112 -38.69826048 -25.18312597 -34.80618357
133 134 135 136 137 138
-27.32115221 -24.60667424 11.53821187 -20.79558069 -4.77819757 -15.30336762
139 140 141 142 143 144
-9.03946754 -24.95319222 -14.38659446 28.05932520 25.46789223 24.42152346
145 146 147 148 149 150
20.65719992 48.26051830 5.11504536 -6.55766791 -15.77233049 -33.07423474
151 152 153 154 155 156
-20.53383714 -33.45739602 -42.57240401 -2.50659285 -7.02678713 -23.24162978
157 158 159 160 161 162
-11.56377226 23.69246392 0.76411276 -0.89891310 -3.41823379 -16.72534957
163 164 165 166 167 168
4.06532490 -10.21530162 16.91148303 48.07822170 40.13212711 58.26335639
169 170 171 172 173 174
-5.97516224 33.54837996 47.77355869 -34.11357174 -33.87529826 -30.33782568
175 176 177 178 179 180
-27.91189677 -25.88775641 -26.38889007 -16.49492348 -15.43839527 -5.00889072
181 182 183 184 185 186
1.14522380 -4.15570795 -17.62145709 -7.78986563 -8.28743139 -21.47817213
187 188 189 190 191 192
-52.66313328 -65.01020499 -26.22144104 -8.73339486 12.94006539 22.46891954
193 194 195
-30.78543929 -3.26815593 24.24485571
> postscript(file="/var/fisher/rcomp/tmp/6nnzf1386011511.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 14.19058595 NA
1 19.42789024 14.19058595
2 23.05010272 19.42789024
3 31.38668939 23.05010272
4 36.91416924 31.38668939
5 14.03273231 36.91416924
6 -20.64367659 14.03273231
7 -24.98071397 -20.64367659
8 -3.23684461 -24.98071397
9 4.61594304 -3.23684461
10 0.49852305 4.61594304
11 -1.05165302 0.49852305
12 -26.28819193 -1.05165302
13 -12.62487029 -26.28819193
14 -10.29467411 -12.62487029
15 -10.21094960 -10.29467411
16 -16.94154396 -10.21094960
17 -9.55121796 -16.94154396
18 13.90288529 -9.55121796
19 2.01735770 13.90288529
20 -13.22375423 2.01735770
21 1.97194037 -13.22375423
22 20.18634331 1.97194037
23 17.15675200 20.18634331
24 -2.37706238 17.15675200
25 -1.79189675 -2.37706238
26 6.05591578 -1.79189675
27 -25.15763494 6.05591578
28 -11.21486998 -25.15763494
29 -10.95718707 -11.21486998
30 4.53919379 -10.95718707
31 14.82155055 4.53919379
32 20.38017451 14.82155055
33 25.93262455 20.38017451
34 27.25705834 25.93262455
35 25.84588168 27.25705834
36 -4.90128953 25.84588168
37 0.65992364 -4.90128953
38 17.22050909 0.65992364
39 19.88663260 17.22050909
40 21.15112020 19.88663260
41 -0.04191455 21.15112020
42 43.02701463 -0.04191455
43 50.85342367 43.02701463
44 61.78843778 50.85342367
45 57.96064916 61.78843778
46 61.92912528 57.96064916
47 68.16217727 61.92912528
48 -29.50038703 68.16217727
49 -37.71848396 -29.50038703
50 -35.09214210 -37.71848396
51 -10.71470391 -35.09214210
52 -32.77091926 -10.71470391
53 -14.00809700 -32.77091926
54 1.39745269 -14.00809700
55 10.61642187 1.39745269
56 3.74165027 10.61642187
57 -15.47679781 3.74165027
58 4.89862923 -15.47679781
59 8.74975239 4.89862923
60 19.04203031 8.74975239
61 36.48041046 19.04203031
62 34.34645293 36.48041046
63 35.06804551 34.34645293
64 47.75741844 35.06804551
65 46.23876098 47.75741844
66 -5.22230468 46.23876098
67 -2.08806262 -5.22230468
68 0.13405883 -2.08806262
69 -8.98731387 0.13405883
70 -3.53190246 -8.98731387
71 -5.86689998 -3.53190246
72 -34.66860192 -5.86689998
73 -33.34474463 -34.66860192
74 -29.94845781 -33.34474463
75 -8.40993393 -29.94845781
76 -25.06523609 -8.40993393
77 -24.87958145 -25.06523609
78 -9.08140748 -24.87958145
79 21.33850230 -9.08140748
80 5.56024400 21.33850230
81 -1.84287511 5.56024400
82 -6.96869113 -1.84287511
83 -30.64753473 -6.96869113
84 -4.18659827 -30.64753473
85 6.03456281 -4.18659827
86 17.78415375 6.03456281
87 15.86727050 17.78415375
88 27.34305079 15.86727050
89 4.74064000 27.34305079
90 -3.30117988 4.74064000
91 17.87954749 -3.30117988
92 -7.72737852 17.87954749
93 18.16907360 -7.72737852
94 -8.38164295 18.16907360
95 -11.45892783 -8.38164295
96 -3.84454717 -11.45892783
97 3.00468388 -3.84454717
98 16.35892106 3.00468388
99 32.86106429 16.35892106
100 66.24612247 32.86106429
101 26.08519158 66.24612247
102 13.94186698 26.08519158
103 -5.05923298 13.94186698
104 -16.87284008 -5.05923298
105 -7.51576965 -16.87284008
106 -12.96389783 -7.51576965
107 -4.61134596 -12.96389783
108 -16.38882444 -4.61134596
109 -1.06586193 -16.38882444
110 5.33519070 -1.06586193
111 12.71566160 5.33519070
112 -3.50958196 12.71566160
113 26.49121565 -3.50958196
114 5.17675856 26.49121565
115 -28.12295799 5.17675856
116 -27.87580234 -28.12295799
117 -27.67492183 -27.87580234
118 -6.39824464 -27.67492183
119 26.62355533 -6.39824464
120 -21.72532271 26.62355533
121 5.68906355 -21.72532271
122 -10.46143656 5.68906355
123 0.80772466 -10.46143656
124 -24.66690079 0.80772466
125 -15.31305861 -24.66690079
126 -12.39948003 -15.31305861
127 -1.34261420 -12.39948003
128 -19.81963112 -1.34261420
129 -38.69826048 -19.81963112
130 -25.18312597 -38.69826048
131 -34.80618357 -25.18312597
132 -27.32115221 -34.80618357
133 -24.60667424 -27.32115221
134 11.53821187 -24.60667424
135 -20.79558069 11.53821187
136 -4.77819757 -20.79558069
137 -15.30336762 -4.77819757
138 -9.03946754 -15.30336762
139 -24.95319222 -9.03946754
140 -14.38659446 -24.95319222
141 28.05932520 -14.38659446
142 25.46789223 28.05932520
143 24.42152346 25.46789223
144 20.65719992 24.42152346
145 48.26051830 20.65719992
146 5.11504536 48.26051830
147 -6.55766791 5.11504536
148 -15.77233049 -6.55766791
149 -33.07423474 -15.77233049
150 -20.53383714 -33.07423474
151 -33.45739602 -20.53383714
152 -42.57240401 -33.45739602
153 -2.50659285 -42.57240401
154 -7.02678713 -2.50659285
155 -23.24162978 -7.02678713
156 -11.56377226 -23.24162978
157 23.69246392 -11.56377226
158 0.76411276 23.69246392
159 -0.89891310 0.76411276
160 -3.41823379 -0.89891310
161 -16.72534957 -3.41823379
162 4.06532490 -16.72534957
163 -10.21530162 4.06532490
164 16.91148303 -10.21530162
165 48.07822170 16.91148303
166 40.13212711 48.07822170
167 58.26335639 40.13212711
168 -5.97516224 58.26335639
169 33.54837996 -5.97516224
170 47.77355869 33.54837996
171 -34.11357174 47.77355869
172 -33.87529826 -34.11357174
173 -30.33782568 -33.87529826
174 -27.91189677 -30.33782568
175 -25.88775641 -27.91189677
176 -26.38889007 -25.88775641
177 -16.49492348 -26.38889007
178 -15.43839527 -16.49492348
179 -5.00889072 -15.43839527
180 1.14522380 -5.00889072
181 -4.15570795 1.14522380
182 -17.62145709 -4.15570795
183 -7.78986563 -17.62145709
184 -8.28743139 -7.78986563
185 -21.47817213 -8.28743139
186 -52.66313328 -21.47817213
187 -65.01020499 -52.66313328
188 -26.22144104 -65.01020499
189 -8.73339486 -26.22144104
190 12.94006539 -8.73339486
191 22.46891954 12.94006539
192 -30.78543929 22.46891954
193 -3.26815593 -30.78543929
194 24.24485571 -3.26815593
195 NA 24.24485571
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 19.42789024 14.19058595
[2,] 23.05010272 19.42789024
[3,] 31.38668939 23.05010272
[4,] 36.91416924 31.38668939
[5,] 14.03273231 36.91416924
[6,] -20.64367659 14.03273231
[7,] -24.98071397 -20.64367659
[8,] -3.23684461 -24.98071397
[9,] 4.61594304 -3.23684461
[10,] 0.49852305 4.61594304
[11,] -1.05165302 0.49852305
[12,] -26.28819193 -1.05165302
[13,] -12.62487029 -26.28819193
[14,] -10.29467411 -12.62487029
[15,] -10.21094960 -10.29467411
[16,] -16.94154396 -10.21094960
[17,] -9.55121796 -16.94154396
[18,] 13.90288529 -9.55121796
[19,] 2.01735770 13.90288529
[20,] -13.22375423 2.01735770
[21,] 1.97194037 -13.22375423
[22,] 20.18634331 1.97194037
[23,] 17.15675200 20.18634331
[24,] -2.37706238 17.15675200
[25,] -1.79189675 -2.37706238
[26,] 6.05591578 -1.79189675
[27,] -25.15763494 6.05591578
[28,] -11.21486998 -25.15763494
[29,] -10.95718707 -11.21486998
[30,] 4.53919379 -10.95718707
[31,] 14.82155055 4.53919379
[32,] 20.38017451 14.82155055
[33,] 25.93262455 20.38017451
[34,] 27.25705834 25.93262455
[35,] 25.84588168 27.25705834
[36,] -4.90128953 25.84588168
[37,] 0.65992364 -4.90128953
[38,] 17.22050909 0.65992364
[39,] 19.88663260 17.22050909
[40,] 21.15112020 19.88663260
[41,] -0.04191455 21.15112020
[42,] 43.02701463 -0.04191455
[43,] 50.85342367 43.02701463
[44,] 61.78843778 50.85342367
[45,] 57.96064916 61.78843778
[46,] 61.92912528 57.96064916
[47,] 68.16217727 61.92912528
[48,] -29.50038703 68.16217727
[49,] -37.71848396 -29.50038703
[50,] -35.09214210 -37.71848396
[51,] -10.71470391 -35.09214210
[52,] -32.77091926 -10.71470391
[53,] -14.00809700 -32.77091926
[54,] 1.39745269 -14.00809700
[55,] 10.61642187 1.39745269
[56,] 3.74165027 10.61642187
[57,] -15.47679781 3.74165027
[58,] 4.89862923 -15.47679781
[59,] 8.74975239 4.89862923
[60,] 19.04203031 8.74975239
[61,] 36.48041046 19.04203031
[62,] 34.34645293 36.48041046
[63,] 35.06804551 34.34645293
[64,] 47.75741844 35.06804551
[65,] 46.23876098 47.75741844
[66,] -5.22230468 46.23876098
[67,] -2.08806262 -5.22230468
[68,] 0.13405883 -2.08806262
[69,] -8.98731387 0.13405883
[70,] -3.53190246 -8.98731387
[71,] -5.86689998 -3.53190246
[72,] -34.66860192 -5.86689998
[73,] -33.34474463 -34.66860192
[74,] -29.94845781 -33.34474463
[75,] -8.40993393 -29.94845781
[76,] -25.06523609 -8.40993393
[77,] -24.87958145 -25.06523609
[78,] -9.08140748 -24.87958145
[79,] 21.33850230 -9.08140748
[80,] 5.56024400 21.33850230
[81,] -1.84287511 5.56024400
[82,] -6.96869113 -1.84287511
[83,] -30.64753473 -6.96869113
[84,] -4.18659827 -30.64753473
[85,] 6.03456281 -4.18659827
[86,] 17.78415375 6.03456281
[87,] 15.86727050 17.78415375
[88,] 27.34305079 15.86727050
[89,] 4.74064000 27.34305079
[90,] -3.30117988 4.74064000
[91,] 17.87954749 -3.30117988
[92,] -7.72737852 17.87954749
[93,] 18.16907360 -7.72737852
[94,] -8.38164295 18.16907360
[95,] -11.45892783 -8.38164295
[96,] -3.84454717 -11.45892783
[97,] 3.00468388 -3.84454717
[98,] 16.35892106 3.00468388
[99,] 32.86106429 16.35892106
[100,] 66.24612247 32.86106429
[101,] 26.08519158 66.24612247
[102,] 13.94186698 26.08519158
[103,] -5.05923298 13.94186698
[104,] -16.87284008 -5.05923298
[105,] -7.51576965 -16.87284008
[106,] -12.96389783 -7.51576965
[107,] -4.61134596 -12.96389783
[108,] -16.38882444 -4.61134596
[109,] -1.06586193 -16.38882444
[110,] 5.33519070 -1.06586193
[111,] 12.71566160 5.33519070
[112,] -3.50958196 12.71566160
[113,] 26.49121565 -3.50958196
[114,] 5.17675856 26.49121565
[115,] -28.12295799 5.17675856
[116,] -27.87580234 -28.12295799
[117,] -27.67492183 -27.87580234
[118,] -6.39824464 -27.67492183
[119,] 26.62355533 -6.39824464
[120,] -21.72532271 26.62355533
[121,] 5.68906355 -21.72532271
[122,] -10.46143656 5.68906355
[123,] 0.80772466 -10.46143656
[124,] -24.66690079 0.80772466
[125,] -15.31305861 -24.66690079
[126,] -12.39948003 -15.31305861
[127,] -1.34261420 -12.39948003
[128,] -19.81963112 -1.34261420
[129,] -38.69826048 -19.81963112
[130,] -25.18312597 -38.69826048
[131,] -34.80618357 -25.18312597
[132,] -27.32115221 -34.80618357
[133,] -24.60667424 -27.32115221
[134,] 11.53821187 -24.60667424
[135,] -20.79558069 11.53821187
[136,] -4.77819757 -20.79558069
[137,] -15.30336762 -4.77819757
[138,] -9.03946754 -15.30336762
[139,] -24.95319222 -9.03946754
[140,] -14.38659446 -24.95319222
[141,] 28.05932520 -14.38659446
[142,] 25.46789223 28.05932520
[143,] 24.42152346 25.46789223
[144,] 20.65719992 24.42152346
[145,] 48.26051830 20.65719992
[146,] 5.11504536 48.26051830
[147,] -6.55766791 5.11504536
[148,] -15.77233049 -6.55766791
[149,] -33.07423474 -15.77233049
[150,] -20.53383714 -33.07423474
[151,] -33.45739602 -20.53383714
[152,] -42.57240401 -33.45739602
[153,] -2.50659285 -42.57240401
[154,] -7.02678713 -2.50659285
[155,] -23.24162978 -7.02678713
[156,] -11.56377226 -23.24162978
[157,] 23.69246392 -11.56377226
[158,] 0.76411276 23.69246392
[159,] -0.89891310 0.76411276
[160,] -3.41823379 -0.89891310
[161,] -16.72534957 -3.41823379
[162,] 4.06532490 -16.72534957
[163,] -10.21530162 4.06532490
[164,] 16.91148303 -10.21530162
[165,] 48.07822170 16.91148303
[166,] 40.13212711 48.07822170
[167,] 58.26335639 40.13212711
[168,] -5.97516224 58.26335639
[169,] 33.54837996 -5.97516224
[170,] 47.77355869 33.54837996
[171,] -34.11357174 47.77355869
[172,] -33.87529826 -34.11357174
[173,] -30.33782568 -33.87529826
[174,] -27.91189677 -30.33782568
[175,] -25.88775641 -27.91189677
[176,] -26.38889007 -25.88775641
[177,] -16.49492348 -26.38889007
[178,] -15.43839527 -16.49492348
[179,] -5.00889072 -15.43839527
[180,] 1.14522380 -5.00889072
[181,] -4.15570795 1.14522380
[182,] -17.62145709 -4.15570795
[183,] -7.78986563 -17.62145709
[184,] -8.28743139 -7.78986563
[185,] -21.47817213 -8.28743139
[186,] -52.66313328 -21.47817213
[187,] -65.01020499 -52.66313328
[188,] -26.22144104 -65.01020499
[189,] -8.73339486 -26.22144104
[190,] 12.94006539 -8.73339486
[191,] 22.46891954 12.94006539
[192,] -30.78543929 22.46891954
[193,] -3.26815593 -30.78543929
[194,] 24.24485571 -3.26815593
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 19.42789024 14.19058595
2 23.05010272 19.42789024
3 31.38668939 23.05010272
4 36.91416924 31.38668939
5 14.03273231 36.91416924
6 -20.64367659 14.03273231
7 -24.98071397 -20.64367659
8 -3.23684461 -24.98071397
9 4.61594304 -3.23684461
10 0.49852305 4.61594304
11 -1.05165302 0.49852305
12 -26.28819193 -1.05165302
13 -12.62487029 -26.28819193
14 -10.29467411 -12.62487029
15 -10.21094960 -10.29467411
16 -16.94154396 -10.21094960
17 -9.55121796 -16.94154396
18 13.90288529 -9.55121796
19 2.01735770 13.90288529
20 -13.22375423 2.01735770
21 1.97194037 -13.22375423
22 20.18634331 1.97194037
23 17.15675200 20.18634331
24 -2.37706238 17.15675200
25 -1.79189675 -2.37706238
26 6.05591578 -1.79189675
27 -25.15763494 6.05591578
28 -11.21486998 -25.15763494
29 -10.95718707 -11.21486998
30 4.53919379 -10.95718707
31 14.82155055 4.53919379
32 20.38017451 14.82155055
33 25.93262455 20.38017451
34 27.25705834 25.93262455
35 25.84588168 27.25705834
36 -4.90128953 25.84588168
37 0.65992364 -4.90128953
38 17.22050909 0.65992364
39 19.88663260 17.22050909
40 21.15112020 19.88663260
41 -0.04191455 21.15112020
42 43.02701463 -0.04191455
43 50.85342367 43.02701463
44 61.78843778 50.85342367
45 57.96064916 61.78843778
46 61.92912528 57.96064916
47 68.16217727 61.92912528
48 -29.50038703 68.16217727
49 -37.71848396 -29.50038703
50 -35.09214210 -37.71848396
51 -10.71470391 -35.09214210
52 -32.77091926 -10.71470391
53 -14.00809700 -32.77091926
54 1.39745269 -14.00809700
55 10.61642187 1.39745269
56 3.74165027 10.61642187
57 -15.47679781 3.74165027
58 4.89862923 -15.47679781
59 8.74975239 4.89862923
60 19.04203031 8.74975239
61 36.48041046 19.04203031
62 34.34645293 36.48041046
63 35.06804551 34.34645293
64 47.75741844 35.06804551
65 46.23876098 47.75741844
66 -5.22230468 46.23876098
67 -2.08806262 -5.22230468
68 0.13405883 -2.08806262
69 -8.98731387 0.13405883
70 -3.53190246 -8.98731387
71 -5.86689998 -3.53190246
72 -34.66860192 -5.86689998
73 -33.34474463 -34.66860192
74 -29.94845781 -33.34474463
75 -8.40993393 -29.94845781
76 -25.06523609 -8.40993393
77 -24.87958145 -25.06523609
78 -9.08140748 -24.87958145
79 21.33850230 -9.08140748
80 5.56024400 21.33850230
81 -1.84287511 5.56024400
82 -6.96869113 -1.84287511
83 -30.64753473 -6.96869113
84 -4.18659827 -30.64753473
85 6.03456281 -4.18659827
86 17.78415375 6.03456281
87 15.86727050 17.78415375
88 27.34305079 15.86727050
89 4.74064000 27.34305079
90 -3.30117988 4.74064000
91 17.87954749 -3.30117988
92 -7.72737852 17.87954749
93 18.16907360 -7.72737852
94 -8.38164295 18.16907360
95 -11.45892783 -8.38164295
96 -3.84454717 -11.45892783
97 3.00468388 -3.84454717
98 16.35892106 3.00468388
99 32.86106429 16.35892106
100 66.24612247 32.86106429
101 26.08519158 66.24612247
102 13.94186698 26.08519158
103 -5.05923298 13.94186698
104 -16.87284008 -5.05923298
105 -7.51576965 -16.87284008
106 -12.96389783 -7.51576965
107 -4.61134596 -12.96389783
108 -16.38882444 -4.61134596
109 -1.06586193 -16.38882444
110 5.33519070 -1.06586193
111 12.71566160 5.33519070
112 -3.50958196 12.71566160
113 26.49121565 -3.50958196
114 5.17675856 26.49121565
115 -28.12295799 5.17675856
116 -27.87580234 -28.12295799
117 -27.67492183 -27.87580234
118 -6.39824464 -27.67492183
119 26.62355533 -6.39824464
120 -21.72532271 26.62355533
121 5.68906355 -21.72532271
122 -10.46143656 5.68906355
123 0.80772466 -10.46143656
124 -24.66690079 0.80772466
125 -15.31305861 -24.66690079
126 -12.39948003 -15.31305861
127 -1.34261420 -12.39948003
128 -19.81963112 -1.34261420
129 -38.69826048 -19.81963112
130 -25.18312597 -38.69826048
131 -34.80618357 -25.18312597
132 -27.32115221 -34.80618357
133 -24.60667424 -27.32115221
134 11.53821187 -24.60667424
135 -20.79558069 11.53821187
136 -4.77819757 -20.79558069
137 -15.30336762 -4.77819757
138 -9.03946754 -15.30336762
139 -24.95319222 -9.03946754
140 -14.38659446 -24.95319222
141 28.05932520 -14.38659446
142 25.46789223 28.05932520
143 24.42152346 25.46789223
144 20.65719992 24.42152346
145 48.26051830 20.65719992
146 5.11504536 48.26051830
147 -6.55766791 5.11504536
148 -15.77233049 -6.55766791
149 -33.07423474 -15.77233049
150 -20.53383714 -33.07423474
151 -33.45739602 -20.53383714
152 -42.57240401 -33.45739602
153 -2.50659285 -42.57240401
154 -7.02678713 -2.50659285
155 -23.24162978 -7.02678713
156 -11.56377226 -23.24162978
157 23.69246392 -11.56377226
158 0.76411276 23.69246392
159 -0.89891310 0.76411276
160 -3.41823379 -0.89891310
161 -16.72534957 -3.41823379
162 4.06532490 -16.72534957
163 -10.21530162 4.06532490
164 16.91148303 -10.21530162
165 48.07822170 16.91148303
166 40.13212711 48.07822170
167 58.26335639 40.13212711
168 -5.97516224 58.26335639
169 33.54837996 -5.97516224
170 47.77355869 33.54837996
171 -34.11357174 47.77355869
172 -33.87529826 -34.11357174
173 -30.33782568 -33.87529826
174 -27.91189677 -30.33782568
175 -25.88775641 -27.91189677
176 -26.38889007 -25.88775641
177 -16.49492348 -26.38889007
178 -15.43839527 -16.49492348
179 -5.00889072 -15.43839527
180 1.14522380 -5.00889072
181 -4.15570795 1.14522380
182 -17.62145709 -4.15570795
183 -7.78986563 -17.62145709
184 -8.28743139 -7.78986563
185 -21.47817213 -8.28743139
186 -52.66313328 -21.47817213
187 -65.01020499 -52.66313328
188 -26.22144104 -65.01020499
189 -8.73339486 -26.22144104
190 12.94006539 -8.73339486
191 22.46891954 12.94006539
192 -30.78543929 22.46891954
193 -3.26815593 -30.78543929
194 24.24485571 -3.26815593
> 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/7x2i21386011511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8q9u31386011511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9or061386011511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10fzif1386011511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11el7d1386011511.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/12n8oe1386011512.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/137wgy1386011512.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/14eimu1386011512.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/15hzko1386011512.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/169set1386011512.tab")
+ }
>
> try(system("convert tmp/1o66a1386011511.ps tmp/1o66a1386011511.png",intern=TRUE))
character(0)
> try(system("convert tmp/29jgk1386011511.ps tmp/29jgk1386011511.png",intern=TRUE))
character(0)
> try(system("convert tmp/3akny1386011511.ps tmp/3akny1386011511.png",intern=TRUE))
character(0)
> try(system("convert tmp/4otza1386011511.ps tmp/4otza1386011511.png",intern=TRUE))
character(0)
> try(system("convert tmp/59zdl1386011511.ps tmp/59zdl1386011511.png",intern=TRUE))
character(0)
> try(system("convert tmp/6nnzf1386011511.ps tmp/6nnzf1386011511.png",intern=TRUE))
character(0)
> try(system("convert tmp/7x2i21386011511.ps tmp/7x2i21386011511.png",intern=TRUE))
character(0)
> try(system("convert tmp/8q9u31386011511.ps tmp/8q9u31386011511.png",intern=TRUE))
character(0)
> try(system("convert tmp/9or061386011511.ps tmp/9or061386011511.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fzif1386011511.ps tmp/10fzif1386011511.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
13.322 2.349 15.659