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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
+ ,-4.813031
+ ,0.266482
+ ,119.992
+ ,157.302
+ ,74.997
+ ,1
+ ,-4.075192
+ ,0.33559
+ ,122.4
+ ,148.65
+ ,113.819
+ ,1
+ ,-4.443179
+ ,0.311173
+ ,116.682
+ ,131.111
+ ,111.555
+ ,1
+ ,-4.117501
+ ,0.334147
+ ,116.676
+ ,137.871
+ ,111.366
+ ,1
+ ,-3.747787
+ ,0.234513
+ ,116.014
+ ,141.781
+ ,110.655
+ ,1
+ ,-4.242867
+ ,0.299111
+ ,120.552
+ ,131.162
+ ,113.787
+ ,1
+ ,-5.634322
+ ,0.257682
+ ,120.267
+ ,137.244
+ ,114.82
+ ,1
+ ,-6.167603
+ ,0.183721
+ ,107.332
+ ,113.84
+ ,104.315
+ ,1
+ ,-5.498678
+ ,0.327769
+ ,95.73
+ ,132.068
+ ,91.754
+ ,1
+ ,-5.011879
+ ,0.325996
+ ,95.056
+ ,120.103
+ ,91.226
+ ,1
+ ,-5.24977
+ ,0.391002
+ ,88.333
+ ,112.24
+ ,84.072
+ ,1
+ ,-4.960234
+ ,0.363566
+ ,91.904
+ ,115.871
+ ,86.292
+ ,1
+ ,-6.547148
+ ,0.152813
+ ,136.926
+ ,159.866
+ ,131.276
+ ,1
+ ,-5.660217
+ ,0.254989
+ ,139.173
+ ,179.139
+ ,76.556
+ ,1
+ ,-6.105098
+ ,0.203653
+ ,152.845
+ ,163.305
+ ,75.836
+ ,1
+ ,-5.340115
+ ,0.210185
+ ,142.167
+ ,217.455
+ ,83.159
+ ,1
+ ,-5.44004
+ ,0.239764
+ ,144.188
+ ,349.259
+ ,82.764
+ ,1
+ ,-2.93107
+ ,0.434326
+ ,168.778
+ ,232.181
+ ,75.603
+ ,1
+ ,-3.949079
+ ,0.35787
+ ,153.046
+ ,175.829
+ ,68.623
+ ,1
+ ,-4.554466
+ ,0.340176
+ ,156.405
+ ,189.398
+ ,142.822
+ ,1
+ ,-4.095442
+ ,0.262564
+ ,153.848
+ ,165.738
+ ,65.782
+ ,1
+ ,-5.18696
+ ,0.237622
+ ,153.88
+ ,172.86
+ ,78.128
+ ,1
+ ,-4.330956
+ ,0.262384
+ ,167.93
+ ,193.221
+ ,79.068
+ ,1
+ ,-5.248776
+ ,0.210279
+ ,173.917
+ ,192.735
+ ,86.18
+ ,1
+ ,-5.557447
+ ,0.22089
+ ,163.656
+ ,200.841
+ ,76.779
+ ,1
+ ,-5.571843
+ ,0.236853
+ ,104.4
+ ,206.002
+ ,77.968
+ ,1
+ ,-6.18359
+ ,0.226278
+ ,171.041
+ ,208.313
+ ,75.501
+ ,1
+ ,-6.27169
+ ,0.196102
+ ,146.845
+ ,208.701
+ ,81.737
+ ,1
+ ,-7.120925
+ ,0.279789
+ ,155.358
+ ,227.383
+ ,80.055
+ ,1
+ ,-6.635729
+ ,0.209866
+ ,162.568
+ ,198.346
+ ,77.63
+ ,0
+ ,-7.3483
+ ,0.177551
+ ,197.076
+ ,206.896
+ ,192.055
+ ,0
+ ,-7.682587
+ ,0.173319
+ ,199.228
+ ,209.512
+ ,192.091
+ ,0
+ ,-7.067931
+ ,0.175181
+ ,198.383
+ ,215.203
+ ,193.104
+ ,0
+ ,-7.695734
+ ,0.17854
+ ,202.266
+ ,211.604
+ ,197.079
+ ,0
+ ,-7.964984
+ ,0.163519
+ ,203.184
+ ,211.526
+ ,196.16
+ ,0
+ ,-7.777685
+ ,0.170183
+ ,201.464
+ ,210.565
+ ,195.708
+ ,1
+ ,-6.149653
+ ,0.218037
+ ,177.876
+ ,192.921
+ ,168.013
+ ,1
+ ,-6.006414
+ ,0.196371
+ ,176.17
+ ,185.604
+ ,163.564
+ ,1
+ ,-6.452058
+ ,0.212294
+ ,180.198
+ ,201.249
+ ,175.456
+ ,1
+ ,-6.006647
+ ,0.266892
+ ,187.733
+ ,202.324
+ ,173.015
+ ,1
+ ,-6.647379
+ ,0.201095
+ ,186.163
+ ,197.724
+ ,177.584
+ ,1
+ ,-7.044105
+ ,0.063412
+ ,184.055
+ ,196.537
+ ,166.977
+ ,0
+ ,-7.31055
+ ,0.098648
+ ,237.226
+ ,247.326
+ ,225.227
+ ,0
+ ,-6.793547
+ ,0.158266
+ ,241.404
+ ,248.834
+ ,232.483
+ ,0
+ ,-7.057869
+ ,0.091608
+ ,243.439
+ ,250.912
+ ,232.435
+ ,0
+ ,-6.99582
+ ,0.102083
+ ,242.852
+ ,255.034
+ ,227.911
+ ,0
+ ,-7.156076
+ ,0.127642
+ ,245.51
+ ,262.09
+ ,231.848
+ ,0
+ ,-7.31951
+ ,0.200873
+ ,252.455
+ ,261.487
+ ,182.786
+ ,0
+ ,-6.439398
+ ,0.266392
+ ,122.188
+ ,128.611
+ ,115.765
+ ,0
+ ,-6.482096
+ ,0.264967
+ ,122.964
+ ,130.049
+ ,114.676
+ ,0
+ ,-6.650471
+ ,0.254498
+ ,124.445
+ ,135.069
+ ,117.495
+ ,0
+ ,-6.689151
+ ,0.291954
+ ,126.344
+ ,134.231
+ ,112.773
+ ,0
+ ,-7.072419
+ ,0.220434
+ ,128.001
+ ,138.052
+ ,122.08
+ ,0
+ ,-6.836811
+ ,0.269866
+ ,129.336
+ ,139.867
+ ,118.604
+ ,1
+ ,-4.649573
+ ,0.205558
+ ,108.807
+ ,134.656
+ ,102.874
+ ,1
+ ,-4.333543
+ ,0.221727
+ ,109.86
+ ,126.358
+ ,104.437
+ ,1
+ ,-4.438453
+ ,0.238298
+ ,110.417
+ ,131.067
+ ,103.37
+ ,1
+ ,-4.60826
+ ,0.290024
+ ,117.274
+ ,129.916
+ ,110.402
+ ,1
+ ,-4.476755
+ ,0.262633
+ ,116.879
+ ,131.897
+ ,108.153
+ ,1
+ ,-4.609161
+ ,0.221711
+ ,114.847
+ ,271.314
+ ,104.68
+ ,0
+ ,-7.040508
+ ,0.066994
+ ,209.144
+ ,237.494
+ ,109.379
+ ,0
+ ,-7.293801
+ ,0.086372
+ ,223.365
+ ,238.987
+ ,98.664
+ ,0
+ ,-6.966321
+ ,0.095882
+ ,222.236
+ ,231.345
+ ,205.495
+ ,0
+ ,-7.24562
+ ,0.018689
+ ,228.832
+ ,234.619
+ ,223.634
+ ,0
+ ,-7.496264
+ ,0.056844
+ ,229.401
+ ,252.221
+ ,221.156
+ ,0
+ ,-7.314237
+ ,0.006274
+ ,228.969
+ ,239.541
+ ,113.201
+ ,1
+ ,-5.409423
+ ,0.22685
+ ,140.341
+ ,159.774
+ ,67.021
+ ,1
+ ,-5.324574
+ ,0.20566
+ ,136.969
+ ,166.607
+ ,66.004
+ ,1
+ ,-5.86975
+ ,0.151814
+ ,143.533
+ ,162.215
+ ,65.809
+ ,1
+ ,-6.261141
+ ,0.120956
+ ,148.09
+ ,162.824
+ ,67.343
+ ,1
+ ,-5.720868
+ ,0.15883
+ ,142.729
+ ,162.408
+ ,65.476
+ ,1
+ ,-5.207985
+ ,0.224852
+ ,136.358
+ ,176.595
+ ,65.75
+ ,1
+ ,-5.79182
+ ,0.329066
+ ,120.08
+ ,139.71
+ ,111.208
+ ,1
+ ,-5.389129
+ ,0.306636
+ ,112.014
+ ,588.518
+ ,107.024
+ ,1
+ ,-5.31336
+ ,0.201861
+ ,110.793
+ ,128.101
+ ,107.316
+ ,1
+ ,-5.477592
+ ,0.315074
+ ,110.707
+ ,122.611
+ ,105.007
+ ,1
+ ,-5.775966
+ ,0.341169
+ ,112.876
+ ,148.826
+ ,106.981
+ ,1
+ ,-5.391029
+ ,0.250572
+ ,110.568
+ ,125.394
+ ,106.821
+ ,1
+ ,-5.115212
+ ,0.249494
+ ,95.385
+ ,102.145
+ ,90.264
+ ,1
+ ,-4.913885
+ ,0.265699
+ ,100.77
+ ,115.697
+ ,85.545
+ ,1
+ ,-4.441519
+ ,0.155097
+ ,96.106
+ ,108.664
+ ,84.51
+ ,1
+ ,-5.132032
+ ,0.210458
+ ,95.605
+ ,107.715
+ ,87.549
+ ,1
+ ,-5.022288
+ ,0.146948
+ ,100.96
+ ,110.019
+ ,95.628
+ ,1
+ ,-6.025367
+ ,0.078202
+ ,98.804
+ ,102.305
+ ,87.804
+ ,1
+ ,-5.288912
+ ,0.343073
+ ,176.858
+ ,205.56
+ ,75.344
+ ,1
+ ,-5.657899
+ ,0.315903
+ ,180.978
+ ,200.125
+ ,155.495
+ ,1
+ ,-6.366916
+ ,0.335753
+ ,178.222
+ ,202.45
+ ,141.047
+ ,1
+ ,-5.515071
+ ,0.299549
+ ,176.281
+ ,227.381
+ ,125.61
+ ,1
+ ,-5.783272
+ ,0.299793
+ ,173.898
+ ,211.35
+ ,74.677
+ ,1
+ ,-4.379411
+ ,0.375531
+ ,179.711
+ ,225.93
+ ,144.878
+ ,1
+ ,-4.508984
+ ,0.389232
+ ,166.605
+ ,206.008
+ ,78.032
+ ,1
+ ,-6.411497
+ ,0.207156
+ ,151.955
+ ,163.335
+ ,147.226
+ ,1
+ ,-5.952058
+ ,0.08784
+ ,148.272
+ ,164.989
+ ,142.299
+ ,1
+ ,-6.152551
+ ,0.17352
+ ,152.125
+ ,161.469
+ ,76.596
+ ,1
+ ,-6.251425
+ ,0.188056
+ ,157.821
+ ,172.975
+ ,68.401
+ ,1
+ ,-6.247076
+ ,0.180528
+ ,157.447
+ ,163.267
+ ,149.605
+ ,1
+ ,-6.41744
+ ,0.194627
+ ,159.116
+ ,168.913
+ ,144.811
+ ,1
+ ,-4.020042
+ ,0.265315
+ ,125.036
+ ,143.946
+ ,116.187
+ ,1
+ ,-5.159169
+ ,0.202146
+ ,125.791
+ ,140.557
+ ,96.206
+ ,1
+ ,-3.760348
+ ,0.242861
+ ,126.512
+ ,141.756
+ ,99.77
+ ,1
+ ,-3.700544
+ ,0.260481
+ ,125.641
+ ,141.068
+ ,116.346
+ ,1
+ ,-4.20273
+ ,0.310163
+ ,128.451
+ ,150.449
+ ,75.632
+ ,1
+ ,-3.269487
+ ,0.270641
+ ,139.224
+ ,586.567
+ ,66.157
+ ,1
+ ,-6.878393
+ ,0.089267
+ ,150.258
+ ,154.609
+ ,75.349
+ ,1
+ ,-7.111576
+ ,0.14478
+ ,154.003
+ ,160.267
+ ,128.621
+ ,1
+ ,-6.997403
+ ,0.210279
+ ,149.689
+ ,160.368
+ ,133.608
+ ,1
+ ,-6.981201
+ ,0.18455
+ ,155.078
+ ,163.736
+ ,144.148
+ ,1
+ ,-6.600023
+ ,0.249172
+ ,151.884
+ ,157.765
+ ,133.751
+ ,1
+ ,-6.739151
+ ,0.160686
+ ,151.989
+ ,157.339
+ ,132.857
+ ,1
+ ,-5.845099
+ ,0.278679
+ ,193.03
+ ,208.9
+ ,80.297
+ ,1
+ ,-5.25832
+ ,0.256454
+ ,200.714
+ ,223.982
+ ,89.686
+ ,1
+ ,-6.471427
+ ,0.184378
+ ,208.519
+ ,220.315
+ ,199.02
+ ,1
+ ,-4.876336
+ ,0.212054
+ ,204.664
+ ,221.3
+ ,189.621
+ ,1
+ ,-5.96304
+ ,0.250283
+ ,210.141
+ ,232.706
+ ,185.258
+ ,1
+ ,-6.729713
+ ,0.181701
+ ,206.327
+ ,226.355
+ ,92.02
+ ,1
+ ,-4.673241
+ ,0.261549
+ ,151.872
+ ,492.892
+ ,69.085
+ ,1
+ ,-6.051233
+ ,0.27328
+ ,158.219
+ ,442.557
+ ,71.948
+ ,1
+ ,-4.597834
+ ,0.372114
+ ,170.756
+ ,450.247
+ ,79.032
+ ,1
+ ,-4.913137
+ ,0.393056
+ ,178.285
+ ,442.824
+ ,82.063
+ ,1
+ ,-5.517173
+ ,0.389295
+ ,217.116
+ ,233.481
+ ,93.978
+ ,1
+ ,-6.186128
+ ,0.279933
+ ,128.94
+ ,479.697
+ ,88.251
+ ,1
+ ,-4.711007
+ ,0.281618
+ ,176.824
+ ,215.293
+ ,83.961
+ ,1
+ ,-5.418787
+ ,0.160267
+ ,138.19
+ ,203.522
+ ,83.34
+ ,1
+ ,-5.44514
+ ,0.142466
+ ,182.018
+ ,197.173
+ ,79.187
+ ,1
+ ,-5.944191
+ ,0.143359
+ ,156.239
+ ,195.107
+ ,79.82
+ ,1
+ ,-5.594275
+ ,0.12795
+ ,145.174
+ ,198.109
+ ,80.637
+ ,1
+ ,-5.540351
+ ,0.087165
+ ,138.145
+ ,197.238
+ ,81.114
+ ,1
+ ,-5.825257
+ ,0.115697
+ ,166.888
+ ,198.966
+ ,79.512
+ ,1
+ ,-6.890021
+ ,0.152941
+ ,119.031
+ ,127.533
+ ,109.216
+ ,1
+ ,-5.892061
+ ,0.195976
+ ,120.078
+ ,126.632
+ ,105.667
+ ,1
+ ,-6.135296
+ ,0.20363
+ ,120.289
+ ,128.143
+ ,100.209
+ ,1
+ ,-6.112667
+ ,0.217013
+ ,120.256
+ ,125.306
+ ,104.773
+ ,1
+ ,-5.436135
+ ,0.254909
+ ,119.056
+ ,125.213
+ ,86.795
+ ,1
+ ,-6.448134
+ ,0.178713
+ ,118.747
+ ,123.723
+ ,109.836
+ ,1
+ ,-5.301321
+ ,0.320385
+ ,106.516
+ ,112.777
+ ,93.105
+ ,1
+ ,-5.333619
+ ,0.322044
+ ,110.453
+ ,127.611
+ ,105.554
+ ,1
+ ,-4.378916
+ ,0.300067
+ ,113.4
+ ,133.344
+ ,107.816
+ ,1
+ ,-4.654894
+ ,0.304107
+ ,113.166
+ ,130.27
+ ,100.673
+ ,1
+ ,-5.634576
+ ,0.306014
+ ,112.239
+ ,126.609
+ ,104.095
+ ,1
+ ,-5.866357
+ ,0.23307
+ ,116.15
+ ,131.731
+ ,109.815
+ ,1
+ ,-4.796845
+ ,0.397749
+ ,170.368
+ ,268.796
+ ,79.543
+ ,1
+ ,-5.410336
+ ,0.288917
+ ,208.083
+ ,253.792
+ ,91.802
+ ,1
+ ,-5.585259
+ ,0.310746
+ ,198.458
+ ,219.29
+ ,148.691
+ ,1
+ ,-5.898673
+ ,0.213353
+ ,202.805
+ ,231.508
+ ,86.232
+ ,1
+ ,-6.132663
+ ,0.220617
+ ,202.544
+ ,241.35
+ ,164.168
+ ,1
+ ,-5.456811
+ ,0.345238
+ ,223.361
+ ,263.872
+ ,87.638
+ ,1
+ ,-3.297668
+ ,0.414758
+ ,169.774
+ ,191.759
+ ,151.451
+ ,1
+ ,-4.276605
+ ,0.355736
+ ,183.52
+ ,216.814
+ ,161.34
+ ,1
+ ,-3.377325
+ ,0.335357
+ ,188.62
+ ,216.302
+ ,165.982
+ ,1
+ ,-4.892495
+ ,0.262281
+ ,202.632
+ ,565.74
+ ,177.258
+ ,1
+ ,-4.484303
+ ,0.340256
+ ,186.695
+ ,211.961
+ ,149.442
+ ,1
+ ,-2.434031
+ ,0.450493
+ ,192.818
+ ,224.429
+ ,168.793
+ ,1
+ ,-2.839756
+ ,0.356224
+ ,198.116
+ ,233.099
+ ,174.478
+ ,1
+ ,-4.865194
+ ,0.246404
+ ,121.345
+ ,139.644
+ ,98.25
+ ,1
+ ,-4.239028
+ ,0.175691
+ ,119.1
+ ,128.442
+ ,88.833
+ ,1
+ ,-3.583722
+ ,0.207914
+ ,117.87
+ ,127.349
+ ,95.654
+ ,1
+ ,-5.4351
+ ,0.230532
+ ,122.336
+ ,142.369
+ ,94.794
+ ,1
+ ,-3.444478
+ ,0.303214
+ ,117.963
+ ,134.209
+ ,100.757
+ ,1
+ ,-5.070096
+ ,0.280091
+ ,126.144
+ ,154.284
+ ,97.543
+ ,1
+ ,-5.498456
+ ,0.234196
+ ,127.93
+ ,138.752
+ ,112.173
+ ,1
+ ,-5.185987
+ ,0.259229
+ ,114.238
+ ,124.393
+ ,77.022
+ ,1
+ ,-5.283009
+ ,0.226528
+ ,115.322
+ ,135.738
+ ,107.802
+ ,1
+ ,-5.529833
+ ,0.24275
+ ,114.554
+ ,126.778
+ ,91.121
+ ,1
+ ,-5.617124
+ ,0.184896
+ ,112.15
+ ,131.669
+ ,97.527
+ ,1
+ ,-2.929379
+ ,0.396746
+ ,102.273
+ ,142.83
+ ,85.902
+ ,0
+ ,-6.816086
+ ,0.17227
+ ,236.2
+ ,244.663
+ ,102.137
+ ,0
+ ,-7.018057
+ ,0.176316
+ ,237.323
+ ,243.709
+ ,229.256
+ ,0
+ ,-7.517934
+ ,0.160414
+ ,260.105
+ ,264.919
+ ,237.303
+ ,0
+ ,-5.736781
+ ,0.164529
+ ,197.569
+ ,217.627
+ ,90.794
+ ,0
+ ,-7.169701
+ ,0.073298
+ ,240.301
+ ,245.135
+ ,219.783
+ ,0
+ ,-7.3045
+ ,0.171088
+ ,244.99
+ ,272.21
+ ,239.17
+ ,0
+ ,-6.323531
+ ,0.218885
+ ,112.547
+ ,133.374
+ ,105.715
+ ,0
+ ,-6.085567
+ ,0.192375
+ ,110.739
+ ,113.597
+ ,100.139
+ ,0
+ ,-5.943501
+ ,0.19215
+ ,113.715
+ ,116.443
+ ,96.913
+ ,0
+ ,-6.012559
+ ,0.229298
+ ,117.004
+ ,144.466
+ ,99.923
+ ,0
+ ,-5.966779
+ ,0.197938
+ ,115.38
+ ,123.109
+ ,108.634
+ ,0
+ ,-6.016891
+ ,0.109256
+ ,116.388
+ ,129.038
+ ,108.97
+ ,1
+ ,-6.486822
+ ,0.197919
+ ,151.737
+ ,190.204
+ ,129.859
+ ,1
+ ,-6.311987
+ ,0.182459
+ ,148.79
+ ,158.359
+ ,138.99
+ ,1
+ ,-5.711205
+ ,0.240875
+ ,148.143
+ ,155.982
+ ,135.041
+ ,1
+ ,-6.261446
+ ,0.183218
+ ,150.44
+ ,163.441
+ ,144.736
+ ,1
+ ,-5.704053
+ ,0.216204
+ ,148.462
+ ,161.078
+ ,141.998
+ ,1
+ ,-6.27717
+ ,0.109397
+ ,149.818
+ ,163.417
+ ,144.786
+ ,0
+ ,-5.61907
+ ,0.191576
+ ,117.226
+ ,123.925
+ ,106.656
+ ,0
+ ,-5.198864
+ ,0.206768
+ ,116.848
+ ,217.552
+ ,99.503
+ ,0
+ ,-5.592584
+ ,0.133917
+ ,116.286
+ ,177.291
+ ,96.983
+ ,0
+ ,-6.431119
+ ,0.15331
+ ,116.556
+ ,592.03
+ ,86.228
+ ,0
+ ,-6.359018
+ ,0.116636
+ ,116.342
+ ,581.289
+ ,94.246
+ ,0
+ ,-6.710219
+ ,0.149694
+ ,114.563
+ ,119.167
+ ,86.647
+ ,0
+ ,-6.934474
+ ,0.15989
+ ,201.774
+ ,262.707
+ ,78.228
+ ,0
+ ,-6.538586
+ ,0.121952
+ ,174.188
+ ,230.978
+ ,94.261
+ ,0
+ ,-6.195325
+ ,0.129303
+ ,209.516
+ ,253.017
+ ,89.488
+ ,0
+ ,-6.787197
+ ,0.158453
+ ,174.688
+ ,240.005
+ ,74.287
+ ,0
+ ,-6.744577
+ ,0.207454
+ ,198.764
+ ,396.961
+ ,74.904
+ ,0
+ ,-5.724056
+ ,0.190667
+ ,214.289
+ ,260.277
+ ,77.973)
+ ,dim=c(6
+ ,195)
+ ,dimnames=list(c('status'
+ ,'spread1'
+ ,'spread2'
+ ,'MDVP:Fo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Flo(Hz)')
+ ,1:195))
> y <- array(NA,dim=c(6,195),dimnames=list(c('status','spread1','spread2','MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)'),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 spread1 spread2 MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz)
1 1 -4.813031 0.266482 119.992 157.302 74.997
2 1 -4.075192 0.335590 122.400 148.650 113.819
3 1 -4.443179 0.311173 116.682 131.111 111.555
4 1 -4.117501 0.334147 116.676 137.871 111.366
5 1 -3.747787 0.234513 116.014 141.781 110.655
6 1 -4.242867 0.299111 120.552 131.162 113.787
7 1 -5.634322 0.257682 120.267 137.244 114.820
8 1 -6.167603 0.183721 107.332 113.840 104.315
9 1 -5.498678 0.327769 95.730 132.068 91.754
10 1 -5.011879 0.325996 95.056 120.103 91.226
11 1 -5.249770 0.391002 88.333 112.240 84.072
12 1 -4.960234 0.363566 91.904 115.871 86.292
13 1 -6.547148 0.152813 136.926 159.866 131.276
14 1 -5.660217 0.254989 139.173 179.139 76.556
15 1 -6.105098 0.203653 152.845 163.305 75.836
16 1 -5.340115 0.210185 142.167 217.455 83.159
17 1 -5.440040 0.239764 144.188 349.259 82.764
18 1 -2.931070 0.434326 168.778 232.181 75.603
19 1 -3.949079 0.357870 153.046 175.829 68.623
20 1 -4.554466 0.340176 156.405 189.398 142.822
21 1 -4.095442 0.262564 153.848 165.738 65.782
22 1 -5.186960 0.237622 153.880 172.860 78.128
23 1 -4.330956 0.262384 167.930 193.221 79.068
24 1 -5.248776 0.210279 173.917 192.735 86.180
25 1 -5.557447 0.220890 163.656 200.841 76.779
26 1 -5.571843 0.236853 104.400 206.002 77.968
27 1 -6.183590 0.226278 171.041 208.313 75.501
28 1 -6.271690 0.196102 146.845 208.701 81.737
29 1 -7.120925 0.279789 155.358 227.383 80.055
30 1 -6.635729 0.209866 162.568 198.346 77.630
31 0 -7.348300 0.177551 197.076 206.896 192.055
32 0 -7.682587 0.173319 199.228 209.512 192.091
33 0 -7.067931 0.175181 198.383 215.203 193.104
34 0 -7.695734 0.178540 202.266 211.604 197.079
35 0 -7.964984 0.163519 203.184 211.526 196.160
36 0 -7.777685 0.170183 201.464 210.565 195.708
37 1 -6.149653 0.218037 177.876 192.921 168.013
38 1 -6.006414 0.196371 176.170 185.604 163.564
39 1 -6.452058 0.212294 180.198 201.249 175.456
40 1 -6.006647 0.266892 187.733 202.324 173.015
41 1 -6.647379 0.201095 186.163 197.724 177.584
42 1 -7.044105 0.063412 184.055 196.537 166.977
43 0 -7.310550 0.098648 237.226 247.326 225.227
44 0 -6.793547 0.158266 241.404 248.834 232.483
45 0 -7.057869 0.091608 243.439 250.912 232.435
46 0 -6.995820 0.102083 242.852 255.034 227.911
47 0 -7.156076 0.127642 245.510 262.090 231.848
48 0 -7.319510 0.200873 252.455 261.487 182.786
49 0 -6.439398 0.266392 122.188 128.611 115.765
50 0 -6.482096 0.264967 122.964 130.049 114.676
51 0 -6.650471 0.254498 124.445 135.069 117.495
52 0 -6.689151 0.291954 126.344 134.231 112.773
53 0 -7.072419 0.220434 128.001 138.052 122.080
54 0 -6.836811 0.269866 129.336 139.867 118.604
55 1 -4.649573 0.205558 108.807 134.656 102.874
56 1 -4.333543 0.221727 109.860 126.358 104.437
57 1 -4.438453 0.238298 110.417 131.067 103.370
58 1 -4.608260 0.290024 117.274 129.916 110.402
59 1 -4.476755 0.262633 116.879 131.897 108.153
60 1 -4.609161 0.221711 114.847 271.314 104.680
61 0 -7.040508 0.066994 209.144 237.494 109.379
62 0 -7.293801 0.086372 223.365 238.987 98.664
63 0 -6.966321 0.095882 222.236 231.345 205.495
64 0 -7.245620 0.018689 228.832 234.619 223.634
65 0 -7.496264 0.056844 229.401 252.221 221.156
66 0 -7.314237 0.006274 228.969 239.541 113.201
67 1 -5.409423 0.226850 140.341 159.774 67.021
68 1 -5.324574 0.205660 136.969 166.607 66.004
69 1 -5.869750 0.151814 143.533 162.215 65.809
70 1 -6.261141 0.120956 148.090 162.824 67.343
71 1 -5.720868 0.158830 142.729 162.408 65.476
72 1 -5.207985 0.224852 136.358 176.595 65.750
73 1 -5.791820 0.329066 120.080 139.710 111.208
74 1 -5.389129 0.306636 112.014 588.518 107.024
75 1 -5.313360 0.201861 110.793 128.101 107.316
76 1 -5.477592 0.315074 110.707 122.611 105.007
77 1 -5.775966 0.341169 112.876 148.826 106.981
78 1 -5.391029 0.250572 110.568 125.394 106.821
79 1 -5.115212 0.249494 95.385 102.145 90.264
80 1 -4.913885 0.265699 100.770 115.697 85.545
81 1 -4.441519 0.155097 96.106 108.664 84.510
82 1 -5.132032 0.210458 95.605 107.715 87.549
83 1 -5.022288 0.146948 100.960 110.019 95.628
84 1 -6.025367 0.078202 98.804 102.305 87.804
85 1 -5.288912 0.343073 176.858 205.560 75.344
86 1 -5.657899 0.315903 180.978 200.125 155.495
87 1 -6.366916 0.335753 178.222 202.450 141.047
88 1 -5.515071 0.299549 176.281 227.381 125.610
89 1 -5.783272 0.299793 173.898 211.350 74.677
90 1 -4.379411 0.375531 179.711 225.930 144.878
91 1 -4.508984 0.389232 166.605 206.008 78.032
92 1 -6.411497 0.207156 151.955 163.335 147.226
93 1 -5.952058 0.087840 148.272 164.989 142.299
94 1 -6.152551 0.173520 152.125 161.469 76.596
95 1 -6.251425 0.188056 157.821 172.975 68.401
96 1 -6.247076 0.180528 157.447 163.267 149.605
97 1 -6.417440 0.194627 159.116 168.913 144.811
98 1 -4.020042 0.265315 125.036 143.946 116.187
99 1 -5.159169 0.202146 125.791 140.557 96.206
100 1 -3.760348 0.242861 126.512 141.756 99.770
101 1 -3.700544 0.260481 125.641 141.068 116.346
102 1 -4.202730 0.310163 128.451 150.449 75.632
103 1 -3.269487 0.270641 139.224 586.567 66.157
104 1 -6.878393 0.089267 150.258 154.609 75.349
105 1 -7.111576 0.144780 154.003 160.267 128.621
106 1 -6.997403 0.210279 149.689 160.368 133.608
107 1 -6.981201 0.184550 155.078 163.736 144.148
108 1 -6.600023 0.249172 151.884 157.765 133.751
109 1 -6.739151 0.160686 151.989 157.339 132.857
110 1 -5.845099 0.278679 193.030 208.900 80.297
111 1 -5.258320 0.256454 200.714 223.982 89.686
112 1 -6.471427 0.184378 208.519 220.315 199.020
113 1 -4.876336 0.212054 204.664 221.300 189.621
114 1 -5.963040 0.250283 210.141 232.706 185.258
115 1 -6.729713 0.181701 206.327 226.355 92.020
116 1 -4.673241 0.261549 151.872 492.892 69.085
117 1 -6.051233 0.273280 158.219 442.557 71.948
118 1 -4.597834 0.372114 170.756 450.247 79.032
119 1 -4.913137 0.393056 178.285 442.824 82.063
120 1 -5.517173 0.389295 217.116 233.481 93.978
121 1 -6.186128 0.279933 128.940 479.697 88.251
122 1 -4.711007 0.281618 176.824 215.293 83.961
123 1 -5.418787 0.160267 138.190 203.522 83.340
124 1 -5.445140 0.142466 182.018 197.173 79.187
125 1 -5.944191 0.143359 156.239 195.107 79.820
126 1 -5.594275 0.127950 145.174 198.109 80.637
127 1 -5.540351 0.087165 138.145 197.238 81.114
128 1 -5.825257 0.115697 166.888 198.966 79.512
129 1 -6.890021 0.152941 119.031 127.533 109.216
130 1 -5.892061 0.195976 120.078 126.632 105.667
131 1 -6.135296 0.203630 120.289 128.143 100.209
132 1 -6.112667 0.217013 120.256 125.306 104.773
133 1 -5.436135 0.254909 119.056 125.213 86.795
134 1 -6.448134 0.178713 118.747 123.723 109.836
135 1 -5.301321 0.320385 106.516 112.777 93.105
136 1 -5.333619 0.322044 110.453 127.611 105.554
137 1 -4.378916 0.300067 113.400 133.344 107.816
138 1 -4.654894 0.304107 113.166 130.270 100.673
139 1 -5.634576 0.306014 112.239 126.609 104.095
140 1 -5.866357 0.233070 116.150 131.731 109.815
141 1 -4.796845 0.397749 170.368 268.796 79.543
142 1 -5.410336 0.288917 208.083 253.792 91.802
143 1 -5.585259 0.310746 198.458 219.290 148.691
144 1 -5.898673 0.213353 202.805 231.508 86.232
145 1 -6.132663 0.220617 202.544 241.350 164.168
146 1 -5.456811 0.345238 223.361 263.872 87.638
147 1 -3.297668 0.414758 169.774 191.759 151.451
148 1 -4.276605 0.355736 183.520 216.814 161.340
149 1 -3.377325 0.335357 188.620 216.302 165.982
150 1 -4.892495 0.262281 202.632 565.740 177.258
151 1 -4.484303 0.340256 186.695 211.961 149.442
152 1 -2.434031 0.450493 192.818 224.429 168.793
153 1 -2.839756 0.356224 198.116 233.099 174.478
154 1 -4.865194 0.246404 121.345 139.644 98.250
155 1 -4.239028 0.175691 119.100 128.442 88.833
156 1 -3.583722 0.207914 117.870 127.349 95.654
157 1 -5.435100 0.230532 122.336 142.369 94.794
158 1 -3.444478 0.303214 117.963 134.209 100.757
159 1 -5.070096 0.280091 126.144 154.284 97.543
160 1 -5.498456 0.234196 127.930 138.752 112.173
161 1 -5.185987 0.259229 114.238 124.393 77.022
162 1 -5.283009 0.226528 115.322 135.738 107.802
163 1 -5.529833 0.242750 114.554 126.778 91.121
164 1 -5.617124 0.184896 112.150 131.669 97.527
165 1 -2.929379 0.396746 102.273 142.830 85.902
166 0 -6.816086 0.172270 236.200 244.663 102.137
167 0 -7.018057 0.176316 237.323 243.709 229.256
168 0 -7.517934 0.160414 260.105 264.919 237.303
169 0 -5.736781 0.164529 197.569 217.627 90.794
170 0 -7.169701 0.073298 240.301 245.135 219.783
171 0 -7.304500 0.171088 244.990 272.210 239.170
172 0 -6.323531 0.218885 112.547 133.374 105.715
173 0 -6.085567 0.192375 110.739 113.597 100.139
174 0 -5.943501 0.192150 113.715 116.443 96.913
175 0 -6.012559 0.229298 117.004 144.466 99.923
176 0 -5.966779 0.197938 115.380 123.109 108.634
177 0 -6.016891 0.109256 116.388 129.038 108.970
178 1 -6.486822 0.197919 151.737 190.204 129.859
179 1 -6.311987 0.182459 148.790 158.359 138.990
180 1 -5.711205 0.240875 148.143 155.982 135.041
181 1 -6.261446 0.183218 150.440 163.441 144.736
182 1 -5.704053 0.216204 148.462 161.078 141.998
183 1 -6.277170 0.109397 149.818 163.417 144.786
184 0 -5.619070 0.191576 117.226 123.925 106.656
185 0 -5.198864 0.206768 116.848 217.552 99.503
186 0 -5.592584 0.133917 116.286 177.291 96.983
187 0 -6.431119 0.153310 116.556 592.030 86.228
188 0 -6.359018 0.116636 116.342 581.289 94.246
189 0 -6.710219 0.149694 114.563 119.167 86.647
190 0 -6.934474 0.159890 201.774 262.707 78.228
191 0 -6.538586 0.121952 174.188 230.978 94.261
192 0 -6.195325 0.129303 209.516 253.017 89.488
193 0 -6.787197 0.158453 174.688 240.005 74.287
194 0 -6.744577 0.207454 198.764 396.961 74.904
195 0 -5.724056 0.190667 214.289 260.277 77.973
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) spread1 spread2 `MDVP:Fo(Hz)` `MDVP:Fhi(Hz)`
1.7490811 0.1445826 0.8510729 -0.0006394 -0.0004734
`MDVP:Flo(Hz)`
-0.0014978
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.84666 -0.18000 0.04686 0.25062 0.67622
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7490811 0.2427541 7.205 1.34e-11 ***
spread1 0.1445826 0.0323338 4.472 1.34e-05 ***
spread2 0.8510729 0.3926045 2.168 0.0314 *
`MDVP:Fo(Hz)` -0.0006394 0.0008557 -0.747 0.4558
`MDVP:Fhi(Hz)` -0.0004734 0.0003034 -1.560 0.1204
`MDVP:Flo(Hz)` -0.0014978 0.0007381 -2.029 0.0438 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.345 on 189 degrees of freedom
Multiple R-squared: 0.3785, Adjusted R-squared: 0.362
F-statistic: 23.02 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,] 3.425607e-51 6.851214e-51 1.000000e+00
[2,] 5.057347e-68 1.011469e-67 1.000000e+00
[3,] 5.153617e-83 1.030723e-82 1.000000e+00
[4,] 1.759045e-96 3.518091e-96 1.000000e+00
[5,] 3.463705e-130 6.927409e-130 1.000000e+00
[6,] 8.878680e-123 1.775736e-122 1.000000e+00
[7,] 5.148300e-137 1.029660e-136 1.000000e+00
[8,] 0.000000e+00 0.000000e+00 1.000000e+00
[9,] 3.922412e-181 7.844824e-181 1.000000e+00
[10,] 5.349558e-181 1.069912e-180 1.000000e+00
[11,] 1.047696e-196 2.095391e-196 1.000000e+00
[12,] 1.030965e-224 2.061930e-224 1.000000e+00
[13,] 8.707486e-260 1.741497e-259 1.000000e+00
[14,] 4.389344e-240 8.778689e-240 1.000000e+00
[15,] 1.712837e-259 3.425674e-259 1.000000e+00
[16,] 1.730237e-270 3.460474e-270 1.000000e+00
[17,] 2.971340e-297 5.942680e-297 1.000000e+00
[18,] 0.000000e+00 0.000000e+00 1.000000e+00
[19,] 0.000000e+00 0.000000e+00 1.000000e+00
[20,] 0.000000e+00 0.000000e+00 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 8.069587e-09 1.613917e-08 1.000000e+00
[24,] 1.692312e-08 3.384624e-08 1.000000e+00
[25,] 1.662847e-08 3.325694e-08 1.000000e+00
[26,] 6.778867e-09 1.355773e-08 1.000000e+00
[27,] 2.495355e-09 4.990711e-09 1.000000e+00
[28,] 9.396800e-10 1.879360e-09 1.000000e+00
[29,] 7.244553e-07 1.448911e-06 9.999993e-01
[30,] 8.689121e-06 1.737824e-05 9.999913e-01
[31,] 1.123982e-04 2.247964e-04 9.998876e-01
[32,] 5.016913e-04 1.003383e-03 9.994983e-01
[33,] 1.658082e-03 3.316164e-03 9.983419e-01
[34,] 2.652995e-03 5.305991e-03 9.973470e-01
[35,] 2.370808e-03 4.741617e-03 9.976292e-01
[36,] 1.834193e-03 3.668387e-03 9.981658e-01
[37,] 1.382960e-03 2.765920e-03 9.986170e-01
[38,] 1.022656e-03 2.045311e-03 9.989773e-01
[39,] 6.825682e-04 1.365136e-03 9.993174e-01
[40,] 5.056857e-04 1.011371e-03 9.994943e-01
[41,] 6.847277e-03 1.369455e-02 9.931527e-01
[42,] 2.967312e-02 5.934625e-02 9.703269e-01
[43,] 6.957418e-02 1.391484e-01 9.304258e-01
[44,] 1.163896e-01 2.327791e-01 8.836104e-01
[45,] 1.644355e-01 3.288711e-01 8.355645e-01
[46,] 2.220672e-01 4.441345e-01 7.779328e-01
[47,] 1.951672e-01 3.903343e-01 8.048328e-01
[48,] 1.700811e-01 3.401623e-01 8.299189e-01
[49,] 1.438332e-01 2.876664e-01 8.561668e-01
[50,] 1.194746e-01 2.389492e-01 8.805254e-01
[51,] 9.754177e-02 1.950835e-01 9.024582e-01
[52,] 8.895494e-02 1.779099e-01 9.110451e-01
[53,] 1.480057e-01 2.960114e-01 8.519943e-01
[54,] 1.714575e-01 3.429150e-01 8.285425e-01
[55,] 1.635351e-01 3.270703e-01 8.364649e-01
[56,] 1.449677e-01 2.899355e-01 8.550323e-01
[57,] 1.261395e-01 2.522791e-01 8.738605e-01
[58,] 1.272342e-01 2.544684e-01 8.727658e-01
[59,] 1.079833e-01 2.159665e-01 8.920167e-01
[60,] 9.007591e-02 1.801518e-01 9.099241e-01
[61,] 7.988687e-02 1.597737e-01 9.201131e-01
[62,] 7.600656e-02 1.520131e-01 9.239934e-01
[63,] 6.538094e-02 1.307619e-01 9.346191e-01
[64,] 5.308169e-02 1.061634e-01 9.469183e-01
[65,] 4.826572e-02 9.653145e-02 9.517343e-01
[66,] 4.374229e-02 8.748459e-02 9.562577e-01
[67,] 3.525326e-02 7.050652e-02 9.647467e-01
[68,] 2.900429e-02 5.800857e-02 9.709957e-01
[69,] 2.482650e-02 4.965300e-02 9.751735e-01
[70,] 1.960165e-02 3.920330e-02 9.803983e-01
[71,] 1.495466e-02 2.990932e-02 9.850453e-01
[72,] 1.134160e-02 2.268319e-02 9.886584e-01
[73,] 9.315791e-03 1.863158e-02 9.906842e-01
[74,] 6.974811e-03 1.394962e-02 9.930252e-01
[75,] 5.323967e-03 1.064793e-02 9.946760e-01
[76,] 4.605022e-03 9.210044e-03 9.953950e-01
[77,] 3.528043e-03 7.056085e-03 9.964720e-01
[78,] 3.734876e-03 7.469751e-03 9.962651e-01
[79,] 4.546854e-03 9.093708e-03 9.954531e-01
[80,] 3.800031e-03 7.600061e-03 9.962000e-01
[81,] 2.963706e-03 5.927411e-03 9.970363e-01
[82,] 2.150512e-03 4.301024e-03 9.978495e-01
[83,] 1.577052e-03 3.154104e-03 9.984229e-01
[84,] 1.945720e-03 3.891439e-03 9.980543e-01
[85,] 2.227430e-03 4.454860e-03 9.977726e-01
[86,] 1.991337e-03 3.982675e-03 9.980087e-01
[87,] 1.772704e-03 3.545408e-03 9.982273e-01
[88,] 2.094058e-03 4.188117e-03 9.979059e-01
[89,] 2.514658e-03 5.029316e-03 9.974853e-01
[90,] 1.863402e-03 3.726804e-03 9.981366e-01
[91,] 1.390405e-03 2.780810e-03 9.986096e-01
[92,] 1.073725e-03 2.147450e-03 9.989263e-01
[93,] 7.948327e-04 1.589665e-03 9.992052e-01
[94,] 5.882973e-04 1.176595e-03 9.994117e-01
[95,] 6.224303e-04 1.244861e-03 9.993776e-01
[96,] 7.792693e-04 1.558539e-03 9.992207e-01
[97,] 1.164277e-03 2.328554e-03 9.988357e-01
[98,] 1.446498e-03 2.892997e-03 9.985535e-01
[99,] 1.985420e-03 3.970841e-03 9.980146e-01
[100,] 2.048962e-03 4.097924e-03 9.979510e-01
[101,] 2.565146e-03 5.130291e-03 9.974349e-01
[102,] 2.107137e-03 4.214274e-03 9.978929e-01
[103,] 1.673079e-03 3.346158e-03 9.983269e-01
[104,] 3.195752e-03 6.391505e-03 9.968042e-01
[105,] 3.171000e-03 6.342000e-03 9.968290e-01
[106,] 3.960702e-03 7.921404e-03 9.960393e-01
[107,] 5.066568e-03 1.013314e-02 9.949334e-01
[108,] 4.031350e-03 8.062699e-03 9.959687e-01
[109,] 3.750879e-03 7.501759e-03 9.962491e-01
[110,] 2.771284e-03 5.542568e-03 9.972287e-01
[111,] 2.040862e-03 4.081724e-03 9.979591e-01
[112,] 1.541050e-03 3.082100e-03 9.984590e-01
[113,] 1.695352e-03 3.390705e-03 9.983046e-01
[114,] 1.245550e-03 2.491101e-03 9.987544e-01
[115,] 1.099406e-03 2.198813e-03 9.989006e-01
[116,] 1.100031e-03 2.200062e-03 9.989000e-01
[117,] 1.247229e-03 2.494459e-03 9.987528e-01
[118,] 1.418095e-03 2.836191e-03 9.985819e-01
[119,] 2.087222e-03 4.174445e-03 9.979128e-01
[120,] 3.881670e-03 7.763339e-03 9.961183e-01
[121,] 5.117491e-03 1.023498e-02 9.948825e-01
[122,] 4.920819e-03 9.841638e-03 9.950792e-01
[123,] 4.866406e-03 9.732813e-03 9.951336e-01
[124,] 4.625410e-03 9.250820e-03 9.953746e-01
[125,] 3.739530e-03 7.479060e-03 9.962605e-01
[126,] 4.668599e-03 9.337197e-03 9.953314e-01
[127,] 3.374476e-03 6.748952e-03 9.966255e-01
[128,] 2.411762e-03 4.823523e-03 9.975882e-01
[129,] 1.712775e-03 3.425549e-03 9.982872e-01
[130,] 1.202229e-03 2.404458e-03 9.987978e-01
[131,] 8.632124e-04 1.726425e-03 9.991368e-01
[132,] 8.011198e-04 1.602240e-03 9.991989e-01
[133,] 5.414612e-04 1.082922e-03 9.994585e-01
[134,] 5.286498e-04 1.057300e-03 9.994714e-01
[135,] 4.783401e-04 9.566803e-04 9.995217e-01
[136,] 9.568480e-04 1.913696e-03 9.990432e-01
[137,] 1.688473e-03 3.376947e-03 9.983115e-01
[138,] 2.271850e-03 4.543699e-03 9.977282e-01
[139,] 1.750302e-03 3.500603e-03 9.982497e-01
[140,] 1.216139e-03 2.432279e-03 9.987839e-01
[141,] 8.291398e-04 1.658280e-03 9.991709e-01
[142,] 1.186531e-03 2.373062e-03 9.988135e-01
[143,] 8.715997e-04 1.743199e-03 9.991284e-01
[144,] 8.357355e-04 1.671471e-03 9.991643e-01
[145,] 7.105790e-04 1.421158e-03 9.992894e-01
[146,] 5.372261e-04 1.074452e-03 9.994628e-01
[147,] 4.534453e-04 9.068906e-04 9.995466e-01
[148,] 3.266103e-04 6.532206e-04 9.996734e-01
[149,] 3.424138e-04 6.848277e-04 9.996576e-01
[150,] 2.301749e-04 4.603498e-04 9.997698e-01
[151,] 1.765011e-04 3.530021e-04 9.998235e-01
[152,] 1.831447e-04 3.662895e-04 9.998169e-01
[153,] 1.947027e-04 3.894055e-04 9.998053e-01
[154,] 2.172893e-04 4.345787e-04 9.997827e-01
[155,] 3.315612e-04 6.631224e-04 9.996684e-01
[156,] 8.326373e-04 1.665275e-03 9.991674e-01
[157,] 5.922795e-04 1.184559e-03 9.994077e-01
[158,] 5.997368e-04 1.199474e-03 9.994003e-01
[159,] 7.103474e-04 1.420695e-03 9.992897e-01
[160,] 9.163872e-04 1.832774e-03 9.990836e-01
[161,] 1.191780e-03 2.383560e-03 9.988082e-01
[162,] 1.698353e-03 3.396705e-03 9.983016e-01
[163,] 9.995164e-01 9.671042e-04 4.835521e-04
[164,] 9.998053e-01 3.893931e-04 1.946966e-04
[165,] 9.997039e-01 5.921896e-04 2.960948e-04
[166,] 9.995177e-01 9.645627e-04 4.822813e-04
[167,] 9.994894e-01 1.021160e-03 5.105800e-04
[168,] 9.999034e-01 1.932777e-04 9.663886e-05
[169,] 9.998677e-01 2.645738e-04 1.322869e-04
[170,] 9.997540e-01 4.919807e-04 2.459904e-04
[171,] 9.993179e-01 1.364217e-03 6.821085e-04
[172,] 9.988382e-01 2.323505e-03 1.161752e-03
[173,] 9.969564e-01 6.087241e-03 3.043620e-03
[174,] 9.929711e-01 1.405777e-02 7.028885e-03
[175,] 1.000000e+00 0.000000e+00 0.000000e+00
[176,] 1.000000e+00 0.000000e+00 0.000000e+00
[177,] 1.000000e+00 0.000000e+00 0.000000e+00
[178,] 1.000000e+00 0.000000e+00 0.000000e+00
> postscript(file="/var/fisher/rcomp/tmp/1xqoh1386789011.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/286h21386789011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3yyqv1386789011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4o0wk1386789011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5wsiv1386789011.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
-0.016481652 -0.126385153 -0.067749344 -0.131476317 -0.099772149 -0.080603559
7 8 9 10 11 12
0.160079614 0.265045440 0.028131189 -0.047627986 -0.087293896 -0.098478515
13 14 15 16 17 18
0.427317411 0.140724589 0.248906114 0.162516141 0.214880077 -0.363880975
19 20 21 22 23 24
-0.198813311 0.023478547 -0.105058390 0.095866847 -0.028940891 0.162355479
25 26 27 28 29 30
0.181148390 0.135977998 0.273437093 0.305909007 0.369237230 0.345829326
31 32 33 34 35 36
-0.326146838 -0.271544756 -0.358327231 -0.263683616 -0.212797173 -0.247780603
37 38 39 40 41 42
0.411191377 0.397702847 0.476376398 0.367181708 0.519480478 0.676221679
43 44 45 46 47 48
-0.169957583 -0.281193241 -0.184033096 -0.207119433 -0.194765440 -0.302789339
49 50 51 52 53 54
-0.732375960 -0.725443999 -0.684644558 -0.717184801 -0.584094135 -0.663722698
55 56 57 58 59 60
0.035618003 -0.024748961 -0.022696883 -0.027796220 -0.026181188 0.087282285
61 62 63 64 65 66
-0.378186360 -0.364305381 -0.264076911 -0.125062610 -0.116312250 -0.267562634
67 68 69 70 71 72
0.105711529 0.111033097 0.237509103 0.325859577 0.209090726 0.081799283
73 74 75 76 77 78
0.117735831 0.279623300 0.139556686 0.060837194 0.098520607 0.107163025
79 80 81 82 83 84
0.022689987 -0.017419860 0.000553014 0.057055121 0.111854994 0.298641934
85 86 87 88 89 90
0.046862434 0.243445536 0.306761610 0.201850677 0.155021703 0.003353421
91 92 93 94 95 96
-0.107504215 0.396596405 0.422764479 0.281221220 0.279959752 0.402529130
97 98 99 100 101 102
0.411720918 -0.071544111 0.115866680 -0.114663257 -0.114361182 -0.138780139
103 104 105 106 107 108
-0.040940127 0.451562040 0.522893269 0.455400164 0.495781623 0.365230746
109 110 111 112 113 114
0.459180743 0.201421716 0.161613905 0.565363245 0.295110175 0.422059387
115 116 117 118 119 120
0.446180169 0.137887925 0.311658052 0.039673783 0.073278273 0.107394923
121 122 123 124 125 126
0.348776133 0.033101882 0.207507471 0.245266869 0.300147278 0.258239113
127 128 129 130 131 132
0.280961258 0.314668491 0.416993439 0.231007240 0.252335947 0.243146146
133 134 135 136 137 138
0.085340708 0.330113973 0.005670056 0.037112859 -0.074230448 -0.050070522
139 140 141 142 143 144
0.092751160 0.201835934 -0.038742946 0.177956280 0.247390147 0.290606057
145 146 147 148 149 150
0.439477586 0.145046302 -0.199116840 0.028113409 -0.074591391 0.397924783
151 152 153 154 155 156
0.053229949 -0.298222896 -0.143325799 0.035482623 -0.015710774 -0.128968250
157 158 159 160 161 162
0.128136571 -0.219257915 0.045377360 0.162073182 0.027390306 0.121413930
163 164 165 166 167 168
0.113577491 0.185808980 -0.401536195 -0.490384512 -0.273963642 -0.151496471
169 170 171 172 173 174
-0.694333705 -0.175971989 -0.194857054 -0.727659234 -0.758371945 -0.780302464
175 176 177 178 179 180
-0.782057381 -0.760087383 -0.673412913 0.401915612 0.386513028 0.242480532
181 182 183 184 185 186
0.390626593 0.275479534 0.455392864 -0.806341497 -0.846662228 -0.750927066
187 188 189 190 191 192
-0.465812624 -0.438236842 -0.646859800 -0.512013598 -0.545608467 -0.575621240
193 194 195
-0.566052648 -0.523303672 -0.706742180
> postscript(file="/var/fisher/rcomp/tmp/6jqrq1386789011.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 -0.016481652 NA
1 -0.126385153 -0.016481652
2 -0.067749344 -0.126385153
3 -0.131476317 -0.067749344
4 -0.099772149 -0.131476317
5 -0.080603559 -0.099772149
6 0.160079614 -0.080603559
7 0.265045440 0.160079614
8 0.028131189 0.265045440
9 -0.047627986 0.028131189
10 -0.087293896 -0.047627986
11 -0.098478515 -0.087293896
12 0.427317411 -0.098478515
13 0.140724589 0.427317411
14 0.248906114 0.140724589
15 0.162516141 0.248906114
16 0.214880077 0.162516141
17 -0.363880975 0.214880077
18 -0.198813311 -0.363880975
19 0.023478547 -0.198813311
20 -0.105058390 0.023478547
21 0.095866847 -0.105058390
22 -0.028940891 0.095866847
23 0.162355479 -0.028940891
24 0.181148390 0.162355479
25 0.135977998 0.181148390
26 0.273437093 0.135977998
27 0.305909007 0.273437093
28 0.369237230 0.305909007
29 0.345829326 0.369237230
30 -0.326146838 0.345829326
31 -0.271544756 -0.326146838
32 -0.358327231 -0.271544756
33 -0.263683616 -0.358327231
34 -0.212797173 -0.263683616
35 -0.247780603 -0.212797173
36 0.411191377 -0.247780603
37 0.397702847 0.411191377
38 0.476376398 0.397702847
39 0.367181708 0.476376398
40 0.519480478 0.367181708
41 0.676221679 0.519480478
42 -0.169957583 0.676221679
43 -0.281193241 -0.169957583
44 -0.184033096 -0.281193241
45 -0.207119433 -0.184033096
46 -0.194765440 -0.207119433
47 -0.302789339 -0.194765440
48 -0.732375960 -0.302789339
49 -0.725443999 -0.732375960
50 -0.684644558 -0.725443999
51 -0.717184801 -0.684644558
52 -0.584094135 -0.717184801
53 -0.663722698 -0.584094135
54 0.035618003 -0.663722698
55 -0.024748961 0.035618003
56 -0.022696883 -0.024748961
57 -0.027796220 -0.022696883
58 -0.026181188 -0.027796220
59 0.087282285 -0.026181188
60 -0.378186360 0.087282285
61 -0.364305381 -0.378186360
62 -0.264076911 -0.364305381
63 -0.125062610 -0.264076911
64 -0.116312250 -0.125062610
65 -0.267562634 -0.116312250
66 0.105711529 -0.267562634
67 0.111033097 0.105711529
68 0.237509103 0.111033097
69 0.325859577 0.237509103
70 0.209090726 0.325859577
71 0.081799283 0.209090726
72 0.117735831 0.081799283
73 0.279623300 0.117735831
74 0.139556686 0.279623300
75 0.060837194 0.139556686
76 0.098520607 0.060837194
77 0.107163025 0.098520607
78 0.022689987 0.107163025
79 -0.017419860 0.022689987
80 0.000553014 -0.017419860
81 0.057055121 0.000553014
82 0.111854994 0.057055121
83 0.298641934 0.111854994
84 0.046862434 0.298641934
85 0.243445536 0.046862434
86 0.306761610 0.243445536
87 0.201850677 0.306761610
88 0.155021703 0.201850677
89 0.003353421 0.155021703
90 -0.107504215 0.003353421
91 0.396596405 -0.107504215
92 0.422764479 0.396596405
93 0.281221220 0.422764479
94 0.279959752 0.281221220
95 0.402529130 0.279959752
96 0.411720918 0.402529130
97 -0.071544111 0.411720918
98 0.115866680 -0.071544111
99 -0.114663257 0.115866680
100 -0.114361182 -0.114663257
101 -0.138780139 -0.114361182
102 -0.040940127 -0.138780139
103 0.451562040 -0.040940127
104 0.522893269 0.451562040
105 0.455400164 0.522893269
106 0.495781623 0.455400164
107 0.365230746 0.495781623
108 0.459180743 0.365230746
109 0.201421716 0.459180743
110 0.161613905 0.201421716
111 0.565363245 0.161613905
112 0.295110175 0.565363245
113 0.422059387 0.295110175
114 0.446180169 0.422059387
115 0.137887925 0.446180169
116 0.311658052 0.137887925
117 0.039673783 0.311658052
118 0.073278273 0.039673783
119 0.107394923 0.073278273
120 0.348776133 0.107394923
121 0.033101882 0.348776133
122 0.207507471 0.033101882
123 0.245266869 0.207507471
124 0.300147278 0.245266869
125 0.258239113 0.300147278
126 0.280961258 0.258239113
127 0.314668491 0.280961258
128 0.416993439 0.314668491
129 0.231007240 0.416993439
130 0.252335947 0.231007240
131 0.243146146 0.252335947
132 0.085340708 0.243146146
133 0.330113973 0.085340708
134 0.005670056 0.330113973
135 0.037112859 0.005670056
136 -0.074230448 0.037112859
137 -0.050070522 -0.074230448
138 0.092751160 -0.050070522
139 0.201835934 0.092751160
140 -0.038742946 0.201835934
141 0.177956280 -0.038742946
142 0.247390147 0.177956280
143 0.290606057 0.247390147
144 0.439477586 0.290606057
145 0.145046302 0.439477586
146 -0.199116840 0.145046302
147 0.028113409 -0.199116840
148 -0.074591391 0.028113409
149 0.397924783 -0.074591391
150 0.053229949 0.397924783
151 -0.298222896 0.053229949
152 -0.143325799 -0.298222896
153 0.035482623 -0.143325799
154 -0.015710774 0.035482623
155 -0.128968250 -0.015710774
156 0.128136571 -0.128968250
157 -0.219257915 0.128136571
158 0.045377360 -0.219257915
159 0.162073182 0.045377360
160 0.027390306 0.162073182
161 0.121413930 0.027390306
162 0.113577491 0.121413930
163 0.185808980 0.113577491
164 -0.401536195 0.185808980
165 -0.490384512 -0.401536195
166 -0.273963642 -0.490384512
167 -0.151496471 -0.273963642
168 -0.694333705 -0.151496471
169 -0.175971989 -0.694333705
170 -0.194857054 -0.175971989
171 -0.727659234 -0.194857054
172 -0.758371945 -0.727659234
173 -0.780302464 -0.758371945
174 -0.782057381 -0.780302464
175 -0.760087383 -0.782057381
176 -0.673412913 -0.760087383
177 0.401915612 -0.673412913
178 0.386513028 0.401915612
179 0.242480532 0.386513028
180 0.390626593 0.242480532
181 0.275479534 0.390626593
182 0.455392864 0.275479534
183 -0.806341497 0.455392864
184 -0.846662228 -0.806341497
185 -0.750927066 -0.846662228
186 -0.465812624 -0.750927066
187 -0.438236842 -0.465812624
188 -0.646859800 -0.438236842
189 -0.512013598 -0.646859800
190 -0.545608467 -0.512013598
191 -0.575621240 -0.545608467
192 -0.566052648 -0.575621240
193 -0.523303672 -0.566052648
194 -0.706742180 -0.523303672
195 NA -0.706742180
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.126385153 -0.016481652
[2,] -0.067749344 -0.126385153
[3,] -0.131476317 -0.067749344
[4,] -0.099772149 -0.131476317
[5,] -0.080603559 -0.099772149
[6,] 0.160079614 -0.080603559
[7,] 0.265045440 0.160079614
[8,] 0.028131189 0.265045440
[9,] -0.047627986 0.028131189
[10,] -0.087293896 -0.047627986
[11,] -0.098478515 -0.087293896
[12,] 0.427317411 -0.098478515
[13,] 0.140724589 0.427317411
[14,] 0.248906114 0.140724589
[15,] 0.162516141 0.248906114
[16,] 0.214880077 0.162516141
[17,] -0.363880975 0.214880077
[18,] -0.198813311 -0.363880975
[19,] 0.023478547 -0.198813311
[20,] -0.105058390 0.023478547
[21,] 0.095866847 -0.105058390
[22,] -0.028940891 0.095866847
[23,] 0.162355479 -0.028940891
[24,] 0.181148390 0.162355479
[25,] 0.135977998 0.181148390
[26,] 0.273437093 0.135977998
[27,] 0.305909007 0.273437093
[28,] 0.369237230 0.305909007
[29,] 0.345829326 0.369237230
[30,] -0.326146838 0.345829326
[31,] -0.271544756 -0.326146838
[32,] -0.358327231 -0.271544756
[33,] -0.263683616 -0.358327231
[34,] -0.212797173 -0.263683616
[35,] -0.247780603 -0.212797173
[36,] 0.411191377 -0.247780603
[37,] 0.397702847 0.411191377
[38,] 0.476376398 0.397702847
[39,] 0.367181708 0.476376398
[40,] 0.519480478 0.367181708
[41,] 0.676221679 0.519480478
[42,] -0.169957583 0.676221679
[43,] -0.281193241 -0.169957583
[44,] -0.184033096 -0.281193241
[45,] -0.207119433 -0.184033096
[46,] -0.194765440 -0.207119433
[47,] -0.302789339 -0.194765440
[48,] -0.732375960 -0.302789339
[49,] -0.725443999 -0.732375960
[50,] -0.684644558 -0.725443999
[51,] -0.717184801 -0.684644558
[52,] -0.584094135 -0.717184801
[53,] -0.663722698 -0.584094135
[54,] 0.035618003 -0.663722698
[55,] -0.024748961 0.035618003
[56,] -0.022696883 -0.024748961
[57,] -0.027796220 -0.022696883
[58,] -0.026181188 -0.027796220
[59,] 0.087282285 -0.026181188
[60,] -0.378186360 0.087282285
[61,] -0.364305381 -0.378186360
[62,] -0.264076911 -0.364305381
[63,] -0.125062610 -0.264076911
[64,] -0.116312250 -0.125062610
[65,] -0.267562634 -0.116312250
[66,] 0.105711529 -0.267562634
[67,] 0.111033097 0.105711529
[68,] 0.237509103 0.111033097
[69,] 0.325859577 0.237509103
[70,] 0.209090726 0.325859577
[71,] 0.081799283 0.209090726
[72,] 0.117735831 0.081799283
[73,] 0.279623300 0.117735831
[74,] 0.139556686 0.279623300
[75,] 0.060837194 0.139556686
[76,] 0.098520607 0.060837194
[77,] 0.107163025 0.098520607
[78,] 0.022689987 0.107163025
[79,] -0.017419860 0.022689987
[80,] 0.000553014 -0.017419860
[81,] 0.057055121 0.000553014
[82,] 0.111854994 0.057055121
[83,] 0.298641934 0.111854994
[84,] 0.046862434 0.298641934
[85,] 0.243445536 0.046862434
[86,] 0.306761610 0.243445536
[87,] 0.201850677 0.306761610
[88,] 0.155021703 0.201850677
[89,] 0.003353421 0.155021703
[90,] -0.107504215 0.003353421
[91,] 0.396596405 -0.107504215
[92,] 0.422764479 0.396596405
[93,] 0.281221220 0.422764479
[94,] 0.279959752 0.281221220
[95,] 0.402529130 0.279959752
[96,] 0.411720918 0.402529130
[97,] -0.071544111 0.411720918
[98,] 0.115866680 -0.071544111
[99,] -0.114663257 0.115866680
[100,] -0.114361182 -0.114663257
[101,] -0.138780139 -0.114361182
[102,] -0.040940127 -0.138780139
[103,] 0.451562040 -0.040940127
[104,] 0.522893269 0.451562040
[105,] 0.455400164 0.522893269
[106,] 0.495781623 0.455400164
[107,] 0.365230746 0.495781623
[108,] 0.459180743 0.365230746
[109,] 0.201421716 0.459180743
[110,] 0.161613905 0.201421716
[111,] 0.565363245 0.161613905
[112,] 0.295110175 0.565363245
[113,] 0.422059387 0.295110175
[114,] 0.446180169 0.422059387
[115,] 0.137887925 0.446180169
[116,] 0.311658052 0.137887925
[117,] 0.039673783 0.311658052
[118,] 0.073278273 0.039673783
[119,] 0.107394923 0.073278273
[120,] 0.348776133 0.107394923
[121,] 0.033101882 0.348776133
[122,] 0.207507471 0.033101882
[123,] 0.245266869 0.207507471
[124,] 0.300147278 0.245266869
[125,] 0.258239113 0.300147278
[126,] 0.280961258 0.258239113
[127,] 0.314668491 0.280961258
[128,] 0.416993439 0.314668491
[129,] 0.231007240 0.416993439
[130,] 0.252335947 0.231007240
[131,] 0.243146146 0.252335947
[132,] 0.085340708 0.243146146
[133,] 0.330113973 0.085340708
[134,] 0.005670056 0.330113973
[135,] 0.037112859 0.005670056
[136,] -0.074230448 0.037112859
[137,] -0.050070522 -0.074230448
[138,] 0.092751160 -0.050070522
[139,] 0.201835934 0.092751160
[140,] -0.038742946 0.201835934
[141,] 0.177956280 -0.038742946
[142,] 0.247390147 0.177956280
[143,] 0.290606057 0.247390147
[144,] 0.439477586 0.290606057
[145,] 0.145046302 0.439477586
[146,] -0.199116840 0.145046302
[147,] 0.028113409 -0.199116840
[148,] -0.074591391 0.028113409
[149,] 0.397924783 -0.074591391
[150,] 0.053229949 0.397924783
[151,] -0.298222896 0.053229949
[152,] -0.143325799 -0.298222896
[153,] 0.035482623 -0.143325799
[154,] -0.015710774 0.035482623
[155,] -0.128968250 -0.015710774
[156,] 0.128136571 -0.128968250
[157,] -0.219257915 0.128136571
[158,] 0.045377360 -0.219257915
[159,] 0.162073182 0.045377360
[160,] 0.027390306 0.162073182
[161,] 0.121413930 0.027390306
[162,] 0.113577491 0.121413930
[163,] 0.185808980 0.113577491
[164,] -0.401536195 0.185808980
[165,] -0.490384512 -0.401536195
[166,] -0.273963642 -0.490384512
[167,] -0.151496471 -0.273963642
[168,] -0.694333705 -0.151496471
[169,] -0.175971989 -0.694333705
[170,] -0.194857054 -0.175971989
[171,] -0.727659234 -0.194857054
[172,] -0.758371945 -0.727659234
[173,] -0.780302464 -0.758371945
[174,] -0.782057381 -0.780302464
[175,] -0.760087383 -0.782057381
[176,] -0.673412913 -0.760087383
[177,] 0.401915612 -0.673412913
[178,] 0.386513028 0.401915612
[179,] 0.242480532 0.386513028
[180,] 0.390626593 0.242480532
[181,] 0.275479534 0.390626593
[182,] 0.455392864 0.275479534
[183,] -0.806341497 0.455392864
[184,] -0.846662228 -0.806341497
[185,] -0.750927066 -0.846662228
[186,] -0.465812624 -0.750927066
[187,] -0.438236842 -0.465812624
[188,] -0.646859800 -0.438236842
[189,] -0.512013598 -0.646859800
[190,] -0.545608467 -0.512013598
[191,] -0.575621240 -0.545608467
[192,] -0.566052648 -0.575621240
[193,] -0.523303672 -0.566052648
[194,] -0.706742180 -0.523303672
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.126385153 -0.016481652
2 -0.067749344 -0.126385153
3 -0.131476317 -0.067749344
4 -0.099772149 -0.131476317
5 -0.080603559 -0.099772149
6 0.160079614 -0.080603559
7 0.265045440 0.160079614
8 0.028131189 0.265045440
9 -0.047627986 0.028131189
10 -0.087293896 -0.047627986
11 -0.098478515 -0.087293896
12 0.427317411 -0.098478515
13 0.140724589 0.427317411
14 0.248906114 0.140724589
15 0.162516141 0.248906114
16 0.214880077 0.162516141
17 -0.363880975 0.214880077
18 -0.198813311 -0.363880975
19 0.023478547 -0.198813311
20 -0.105058390 0.023478547
21 0.095866847 -0.105058390
22 -0.028940891 0.095866847
23 0.162355479 -0.028940891
24 0.181148390 0.162355479
25 0.135977998 0.181148390
26 0.273437093 0.135977998
27 0.305909007 0.273437093
28 0.369237230 0.305909007
29 0.345829326 0.369237230
30 -0.326146838 0.345829326
31 -0.271544756 -0.326146838
32 -0.358327231 -0.271544756
33 -0.263683616 -0.358327231
34 -0.212797173 -0.263683616
35 -0.247780603 -0.212797173
36 0.411191377 -0.247780603
37 0.397702847 0.411191377
38 0.476376398 0.397702847
39 0.367181708 0.476376398
40 0.519480478 0.367181708
41 0.676221679 0.519480478
42 -0.169957583 0.676221679
43 -0.281193241 -0.169957583
44 -0.184033096 -0.281193241
45 -0.207119433 -0.184033096
46 -0.194765440 -0.207119433
47 -0.302789339 -0.194765440
48 -0.732375960 -0.302789339
49 -0.725443999 -0.732375960
50 -0.684644558 -0.725443999
51 -0.717184801 -0.684644558
52 -0.584094135 -0.717184801
53 -0.663722698 -0.584094135
54 0.035618003 -0.663722698
55 -0.024748961 0.035618003
56 -0.022696883 -0.024748961
57 -0.027796220 -0.022696883
58 -0.026181188 -0.027796220
59 0.087282285 -0.026181188
60 -0.378186360 0.087282285
61 -0.364305381 -0.378186360
62 -0.264076911 -0.364305381
63 -0.125062610 -0.264076911
64 -0.116312250 -0.125062610
65 -0.267562634 -0.116312250
66 0.105711529 -0.267562634
67 0.111033097 0.105711529
68 0.237509103 0.111033097
69 0.325859577 0.237509103
70 0.209090726 0.325859577
71 0.081799283 0.209090726
72 0.117735831 0.081799283
73 0.279623300 0.117735831
74 0.139556686 0.279623300
75 0.060837194 0.139556686
76 0.098520607 0.060837194
77 0.107163025 0.098520607
78 0.022689987 0.107163025
79 -0.017419860 0.022689987
80 0.000553014 -0.017419860
81 0.057055121 0.000553014
82 0.111854994 0.057055121
83 0.298641934 0.111854994
84 0.046862434 0.298641934
85 0.243445536 0.046862434
86 0.306761610 0.243445536
87 0.201850677 0.306761610
88 0.155021703 0.201850677
89 0.003353421 0.155021703
90 -0.107504215 0.003353421
91 0.396596405 -0.107504215
92 0.422764479 0.396596405
93 0.281221220 0.422764479
94 0.279959752 0.281221220
95 0.402529130 0.279959752
96 0.411720918 0.402529130
97 -0.071544111 0.411720918
98 0.115866680 -0.071544111
99 -0.114663257 0.115866680
100 -0.114361182 -0.114663257
101 -0.138780139 -0.114361182
102 -0.040940127 -0.138780139
103 0.451562040 -0.040940127
104 0.522893269 0.451562040
105 0.455400164 0.522893269
106 0.495781623 0.455400164
107 0.365230746 0.495781623
108 0.459180743 0.365230746
109 0.201421716 0.459180743
110 0.161613905 0.201421716
111 0.565363245 0.161613905
112 0.295110175 0.565363245
113 0.422059387 0.295110175
114 0.446180169 0.422059387
115 0.137887925 0.446180169
116 0.311658052 0.137887925
117 0.039673783 0.311658052
118 0.073278273 0.039673783
119 0.107394923 0.073278273
120 0.348776133 0.107394923
121 0.033101882 0.348776133
122 0.207507471 0.033101882
123 0.245266869 0.207507471
124 0.300147278 0.245266869
125 0.258239113 0.300147278
126 0.280961258 0.258239113
127 0.314668491 0.280961258
128 0.416993439 0.314668491
129 0.231007240 0.416993439
130 0.252335947 0.231007240
131 0.243146146 0.252335947
132 0.085340708 0.243146146
133 0.330113973 0.085340708
134 0.005670056 0.330113973
135 0.037112859 0.005670056
136 -0.074230448 0.037112859
137 -0.050070522 -0.074230448
138 0.092751160 -0.050070522
139 0.201835934 0.092751160
140 -0.038742946 0.201835934
141 0.177956280 -0.038742946
142 0.247390147 0.177956280
143 0.290606057 0.247390147
144 0.439477586 0.290606057
145 0.145046302 0.439477586
146 -0.199116840 0.145046302
147 0.028113409 -0.199116840
148 -0.074591391 0.028113409
149 0.397924783 -0.074591391
150 0.053229949 0.397924783
151 -0.298222896 0.053229949
152 -0.143325799 -0.298222896
153 0.035482623 -0.143325799
154 -0.015710774 0.035482623
155 -0.128968250 -0.015710774
156 0.128136571 -0.128968250
157 -0.219257915 0.128136571
158 0.045377360 -0.219257915
159 0.162073182 0.045377360
160 0.027390306 0.162073182
161 0.121413930 0.027390306
162 0.113577491 0.121413930
163 0.185808980 0.113577491
164 -0.401536195 0.185808980
165 -0.490384512 -0.401536195
166 -0.273963642 -0.490384512
167 -0.151496471 -0.273963642
168 -0.694333705 -0.151496471
169 -0.175971989 -0.694333705
170 -0.194857054 -0.175971989
171 -0.727659234 -0.194857054
172 -0.758371945 -0.727659234
173 -0.780302464 -0.758371945
174 -0.782057381 -0.780302464
175 -0.760087383 -0.782057381
176 -0.673412913 -0.760087383
177 0.401915612 -0.673412913
178 0.386513028 0.401915612
179 0.242480532 0.386513028
180 0.390626593 0.242480532
181 0.275479534 0.390626593
182 0.455392864 0.275479534
183 -0.806341497 0.455392864
184 -0.846662228 -0.806341497
185 -0.750927066 -0.846662228
186 -0.465812624 -0.750927066
187 -0.438236842 -0.465812624
188 -0.646859800 -0.438236842
189 -0.512013598 -0.646859800
190 -0.545608467 -0.512013598
191 -0.575621240 -0.545608467
192 -0.566052648 -0.575621240
193 -0.523303672 -0.566052648
194 -0.706742180 -0.523303672
> 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/711xo1386789011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8duzq1386789011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9az1q1386789011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/106jyv1386789011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112klh1386789011.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/120jxv1386789011.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/13km491386789012.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/14gbna1386789012.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/158azw1386789012.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/164wkx1386789012.tab")
+ }
>
> try(system("convert tmp/1xqoh1386789011.ps tmp/1xqoh1386789011.png",intern=TRUE))
character(0)
> try(system("convert tmp/286h21386789011.ps tmp/286h21386789011.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yyqv1386789011.ps tmp/3yyqv1386789011.png",intern=TRUE))
character(0)
> try(system("convert tmp/4o0wk1386789011.ps tmp/4o0wk1386789011.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wsiv1386789011.ps tmp/5wsiv1386789011.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jqrq1386789011.ps tmp/6jqrq1386789011.png",intern=TRUE))
character(0)
> try(system("convert tmp/711xo1386789011.ps tmp/711xo1386789011.png",intern=TRUE))
character(0)
> try(system("convert tmp/8duzq1386789011.ps tmp/8duzq1386789011.png",intern=TRUE))
character(0)
> try(system("convert tmp/9az1q1386789011.ps tmp/9az1q1386789011.png",intern=TRUE))
character(0)
> try(system("convert tmp/106jyv1386789011.ps tmp/106jyv1386789011.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.403 2.951 18.274