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
+ ,74.997
+ ,0.00784
+ ,0.00007
+ ,0.0037
+ ,0.00554
+ ,122.4
+ ,148.65
+ ,113.819
+ ,0.00968
+ ,0.00008
+ ,0.00465
+ ,0.00696
+ ,116.682
+ ,131.111
+ ,111.555
+ ,0.0105
+ ,0.00009
+ ,0.00544
+ ,0.00781
+ ,116.676
+ ,137.871
+ ,111.366
+ ,0.00997
+ ,0.00009
+ ,0.00502
+ ,0.00698
+ ,116.014
+ ,141.781
+ ,110.655
+ ,0.01284
+ ,0.00011
+ ,0.00655
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+ ,120.552
+ ,131.162
+ ,113.787
+ ,0.00968
+ ,0.00008
+ ,0.00463
+ ,0.0075
+ ,120.267
+ ,137.244
+ ,114.82
+ ,0.00333
+ ,0.00003
+ ,0.00155
+ ,0.00202
+ ,107.332
+ ,113.84
+ ,104.315
+ ,0.0029
+ ,0.00003
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+ ,95.73
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+ ,0.00268
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+ ,84.072
+ ,0.00505
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+ ,115.871
+ ,86.292
+ ,0.0054
+ ,0.00006
+ ,0.00281
+ ,0.00336
+ ,136.926
+ ,159.866
+ ,131.276
+ ,0.00293
+ ,0.00002
+ ,0.00118
+ ,0.00153
+ ,139.173
+ ,179.139
+ ,76.556
+ ,0.0039
+ ,0.00003
+ ,0.00165
+ ,0.00208
+ ,152.845
+ ,163.305
+ ,75.836
+ ,0.00294
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+ ,0.00121
+ ,0.00149
+ ,142.167
+ ,217.455
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+ ,0.00369
+ ,0.00003
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+ ,0.00203
+ ,144.188
+ ,349.259
+ ,82.764
+ ,0.00544
+ ,0.00004
+ ,0.00211
+ ,0.00292
+ ,168.778
+ ,232.181
+ ,75.603
+ ,0.00718
+ ,0.00004
+ ,0.00284
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+ ,68.623
+ ,0.00742
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+ ,0.00432
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+ ,189.398
+ ,142.822
+ ,0.00768
+ ,0.00005
+ ,0.00372
+ ,0.00399
+ ,153.848
+ ,165.738
+ ,65.782
+ ,0.0084
+ ,0.00005
+ ,0.00428
+ ,0.0045
+ ,153.88
+ ,172.86
+ ,78.128
+ ,0.0048
+ ,0.00003
+ ,0.00232
+ ,0.00267
+ ,167.93
+ ,193.221
+ ,79.068
+ ,0.00442
+ ,0.00003
+ ,0.0022
+ ,0.00247
+ ,173.917
+ ,192.735
+ ,86.18
+ ,0.00476
+ ,0.00003
+ ,0.00221
+ ,0.00258
+ ,163.656
+ ,200.841
+ ,76.779
+ ,0.00742
+ ,0.00005
+ ,0.0038
+ ,0.0039
+ ,104.4
+ ,206.002
+ ,77.968
+ ,0.00633
+ ,0.00006
+ ,0.00316
+ ,0.00375
+ ,171.041
+ ,208.313
+ ,75.501
+ ,0.00455
+ ,0.00003
+ ,0.0025
+ ,0.00234
+ ,146.845
+ ,208.701
+ ,81.737
+ ,0.00496
+ ,0.00003
+ ,0.0025
+ ,0.00275
+ ,155.358
+ ,227.383
+ ,80.055
+ ,0.0031
+ ,0.00002
+ ,0.00159
+ ,0.00176
+ ,162.568
+ ,198.346
+ ,77.63
+ ,0.00502
+ ,0.00003
+ ,0.0028
+ ,0.00253
+ ,197.076
+ ,206.896
+ ,192.055
+ ,0.00289
+ ,0.00001
+ ,0.00166
+ ,0.00168
+ ,199.228
+ ,209.512
+ ,192.091
+ ,0.00241
+ ,0.00001
+ ,0.00134
+ ,0.00138
+ ,198.383
+ ,215.203
+ ,193.104
+ ,0.00212
+ ,0.00001
+ ,0.00113
+ ,0.00135
+ ,202.266
+ ,211.604
+ ,197.079
+ ,0.0018
+ ,0.000009
+ ,0.00093
+ ,0.00107
+ ,203.184
+ ,211.526
+ ,196.16
+ ,0.00178
+ ,0.000009
+ ,0.00094
+ ,0.00106
+ ,201.464
+ ,210.565
+ ,195.708
+ ,0.00198
+ ,0.00001
+ ,0.00105
+ ,0.00115
+ ,177.876
+ ,192.921
+ ,168.013
+ ,0.00411
+ ,0.00002
+ ,0.00233
+ ,0.00241
+ ,176.17
+ ,185.604
+ ,163.564
+ ,0.00369
+ ,0.00002
+ ,0.00205
+ ,0.00218
+ ,180.198
+ ,201.249
+ ,175.456
+ ,0.00284
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+ ,0.00166
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+ ,0.00182
+ ,186.163
+ ,197.724
+ ,177.584
+ ,0.00298
+ ,0.00002
+ ,0.00165
+ ,0.00175
+ ,184.055
+ ,196.537
+ ,166.977
+ ,0.00258
+ ,0.00001
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+ ,0.00147
+ ,237.226
+ ,247.326
+ ,225.227
+ ,0.00298
+ ,0.00001
+ ,0.00169
+ ,0.00182
+ ,241.404
+ ,248.834
+ ,232.483
+ ,0.00281
+ ,0.00001
+ ,0.00157
+ ,0.00173
+ ,243.439
+ ,250.912
+ ,232.435
+ ,0.0021
+ ,0.000009
+ ,0.00109
+ ,0.00137
+ ,242.852
+ ,255.034
+ ,227.911
+ ,0.00225
+ ,0.000009
+ ,0.00117
+ ,0.00139
+ ,245.51
+ ,262.09
+ ,231.848
+ ,0.00235
+ ,0.00001
+ ,0.00127
+ ,0.00148
+ ,252.455
+ ,261.487
+ ,182.786
+ ,0.00185
+ ,0.000007
+ ,0.00092
+ ,0.00113
+ ,122.188
+ ,128.611
+ ,115.765
+ ,0.00524
+ ,0.00004
+ ,0.00169
+ ,0.00203
+ ,122.964
+ ,130.049
+ ,114.676
+ ,0.00428
+ ,0.00003
+ ,0.00124
+ ,0.00155
+ ,124.445
+ ,135.069
+ ,117.495
+ ,0.00431
+ ,0.00003
+ ,0.00141
+ ,0.00167
+ ,126.344
+ ,134.231
+ ,112.773
+ ,0.00448
+ ,0.00004
+ ,0.00131
+ ,0.00169
+ ,128.001
+ ,138.052
+ ,122.08
+ ,0.00436
+ ,0.00003
+ ,0.00137
+ ,0.00166
+ ,129.336
+ ,139.867
+ ,118.604
+ ,0.0049
+ ,0.00004
+ ,0.00165
+ ,0.00183
+ ,108.807
+ ,134.656
+ ,102.874
+ ,0.00761
+ ,0.00007
+ ,0.00349
+ ,0.00486
+ ,109.86
+ ,126.358
+ ,104.437
+ ,0.00874
+ ,0.00008
+ ,0.00398
+ ,0.00539
+ ,110.417
+ ,131.067
+ ,103.37
+ ,0.00784
+ ,0.00007
+ ,0.00352
+ ,0.00514
+ ,117.274
+ ,129.916
+ ,110.402
+ ,0.00752
+ ,0.00006
+ ,0.00299
+ ,0.00469
+ ,116.879
+ ,131.897
+ ,108.153
+ ,0.00788
+ ,0.00007
+ ,0.00334
+ ,0.00493
+ ,114.847
+ ,271.314
+ ,104.68
+ ,0.00867
+ ,0.00008
+ ,0.00373
+ ,0.0052
+ ,209.144
+ ,237.494
+ ,109.379
+ ,0.00282
+ ,0.00001
+ ,0.00147
+ ,0.00152
+ ,223.365
+ ,238.987
+ ,98.664
+ ,0.00264
+ ,0.00001
+ ,0.00154
+ ,0.00151
+ ,222.236
+ ,231.345
+ ,205.495
+ ,0.00266
+ ,0.00001
+ ,0.00152
+ ,0.00144
+ ,228.832
+ ,234.619
+ ,223.634
+ ,0.00296
+ ,0.00001
+ ,0.00175
+ ,0.00155
+ ,229.401
+ ,252.221
+ ,221.156
+ ,0.00205
+ ,0.000009
+ ,0.00114
+ ,0.00113
+ ,228.969
+ ,239.541
+ ,113.201
+ ,0.00238
+ ,0.00001
+ ,0.00136
+ ,0.0014
+ ,140.341
+ ,159.774
+ ,67.021
+ ,0.00817
+ ,0.00006
+ ,0.0043
+ ,0.0044
+ ,136.969
+ ,166.607
+ ,66.004
+ ,0.00923
+ ,0.00007
+ ,0.00507
+ ,0.00463
+ ,143.533
+ ,162.215
+ ,65.809
+ ,0.01101
+ ,0.00008
+ ,0.00647
+ ,0.00467
+ ,148.09
+ ,162.824
+ ,67.343
+ ,0.00762
+ ,0.00005
+ ,0.00467
+ ,0.00354
+ ,142.729
+ ,162.408
+ ,65.476
+ ,0.00831
+ ,0.00006
+ ,0.00469
+ ,0.00419
+ ,136.358
+ ,176.595
+ ,65.75
+ ,0.00971
+ ,0.00007
+ ,0.00534
+ ,0.00478
+ ,120.08
+ ,139.71
+ ,111.208
+ ,0.00405
+ ,0.00003
+ ,0.0018
+ ,0.0022
+ ,112.014
+ ,588.518
+ ,107.024
+ ,0.00533
+ ,0.00005
+ ,0.00268
+ ,0.00329
+ ,110.793
+ ,128.101
+ ,107.316
+ ,0.00494
+ ,0.00004
+ ,0.0026
+ ,0.00283
+ ,110.707
+ ,122.611
+ ,105.007
+ ,0.00516
+ ,0.00005
+ ,0.00277
+ ,0.00289
+ ,112.876
+ ,148.826
+ ,106.981
+ ,0.005
+ ,0.00004
+ ,0.0027
+ ,0.00289
+ ,110.568
+ ,125.394
+ ,106.821
+ ,0.00462
+ ,0.00004
+ ,0.00226
+ ,0.0028
+ ,95.385
+ ,102.145
+ ,90.264
+ ,0.00608
+ ,0.00006
+ ,0.00331
+ ,0.00332
+ ,100.77
+ ,115.697
+ ,85.545
+ ,0.01038
+ ,0.0001
+ ,0.00622
+ ,0.00576
+ ,96.106
+ ,108.664
+ ,84.51
+ ,0.00694
+ ,0.00007
+ ,0.00389
+ ,0.00415
+ ,95.605
+ ,107.715
+ ,87.549
+ ,0.00702
+ ,0.00007
+ ,0.00428
+ ,0.00371
+ ,100.96
+ ,110.019
+ ,95.628
+ ,0.00606
+ ,0.00006
+ ,0.00351
+ ,0.00348
+ ,98.804
+ ,102.305
+ ,87.804
+ ,0.00432
+ ,0.00004
+ ,0.00247
+ ,0.00258
+ ,176.858
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+ ,75.344
+ ,0.00747
+ ,0.00004
+ ,0.00418
+ ,0.0042
+ ,180.978
+ ,200.125
+ ,155.495
+ ,0.00406
+ ,0.00002
+ ,0.0022
+ ,0.00244
+ ,178.222
+ ,202.45
+ ,141.047
+ ,0.00321
+ ,0.00002
+ ,0.00163
+ ,0.00194
+ ,176.281
+ ,227.381
+ ,125.61
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+ ,0.00003
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+ ,0.00312
+ ,173.898
+ ,211.35
+ ,74.677
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+ ,0.00003
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+ ,0.00254
+ ,179.711
+ ,225.93
+ ,144.878
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+ ,166.605
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+ ,0.00419
+ ,0.00003
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+ ,0.00227
+ ,148.272
+ ,164.989
+ ,142.299
+ ,0.00459
+ ,0.00003
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+ ,125.036
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+ ,116.187
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+ ,0.00623
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+ ,96.206
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+ ,0.00826
+ ,0.00655
+ ,126.512
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+ ,0.01936
+ ,0.00015
+ ,0.01159
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+ ,0.01872
+ ,0.0001
+ ,0.01075
+ ,0.01154
+ ,192.818
+ ,224.429
+ ,168.793
+ ,0.03107
+ ,0.00016
+ ,0.018
+ ,0.01958
+ ,198.116
+ ,233.099
+ ,174.478
+ ,0.02714
+ ,0.00014
+ ,0.01568
+ ,0.01699
+ ,121.345
+ ,139.644
+ ,98.25
+ ,0.00684
+ ,0.00006
+ ,0.00388
+ ,0.00332
+ ,119.1
+ ,128.442
+ ,88.833
+ ,0.00692
+ ,0.00006
+ ,0.00393
+ ,0.003
+ ,117.87
+ ,127.349
+ ,95.654
+ ,0.00647
+ ,0.00005
+ ,0.00356
+ ,0.003
+ ,122.336
+ ,142.369
+ ,94.794
+ ,0.00727
+ ,0.00006
+ ,0.00415
+ ,0.00339
+ ,117.963
+ ,134.209
+ ,100.757
+ ,0.01813
+ ,0.00015
+ ,0.01117
+ ,0.00718
+ ,126.144
+ ,154.284
+ ,97.543
+ ,0.00975
+ ,0.00008
+ ,0.00593
+ ,0.00454
+ ,127.93
+ ,138.752
+ ,112.173
+ ,0.00605
+ ,0.00005
+ ,0.00321
+ ,0.00318
+ ,114.238
+ ,124.393
+ ,77.022
+ ,0.00581
+ ,0.00005
+ ,0.00299
+ ,0.00316
+ ,115.322
+ ,135.738
+ ,107.802
+ ,0.00619
+ ,0.00005
+ ,0.00352
+ ,0.00329
+ ,114.554
+ ,126.778
+ ,91.121
+ ,0.00651
+ ,0.00006
+ ,0.00366
+ ,0.0034
+ ,112.15
+ ,131.669
+ ,97.527
+ ,0.00519
+ ,0.00005
+ ,0.00291
+ ,0.00284
+ ,102.273
+ ,142.83
+ ,85.902
+ ,0.00907
+ ,0.00009
+ ,0.00493
+ ,0.00461
+ ,236.2
+ ,244.663
+ ,102.137
+ ,0.00277
+ ,0.00001
+ ,0.00154
+ ,0.00153
+ ,237.323
+ ,243.709
+ ,229.256
+ ,0.00303
+ ,0.00001
+ ,0.00173
+ ,0.00159
+ ,260.105
+ ,264.919
+ ,237.303
+ ,0.00339
+ ,0.00001
+ ,0.00205
+ ,0.00186
+ ,197.569
+ ,217.627
+ ,90.794
+ ,0.00803
+ ,0.00004
+ ,0.0049
+ ,0.00448
+ ,240.301
+ ,245.135
+ ,219.783
+ ,0.00517
+ ,0.00002
+ ,0.00316
+ ,0.00283
+ ,244.99
+ ,272.21
+ ,239.17
+ ,0.00451
+ ,0.00002
+ ,0.00279
+ ,0.00237
+ ,112.547
+ ,133.374
+ ,105.715
+ ,0.00355
+ ,0.00003
+ ,0.00166
+ ,0.0019
+ ,110.739
+ ,113.597
+ ,100.139
+ ,0.00356
+ ,0.00003
+ ,0.0017
+ ,0.002
+ ,113.715
+ ,116.443
+ ,96.913
+ ,0.00349
+ ,0.00003
+ ,0.00171
+ ,0.00203
+ ,117.004
+ ,144.466
+ ,99.923
+ ,0.00353
+ ,0.00003
+ ,0.00176
+ ,0.00218
+ ,115.38
+ ,123.109
+ ,108.634
+ ,0.00332
+ ,0.00003
+ ,0.0016
+ ,0.00199
+ ,116.388
+ ,129.038
+ ,108.97
+ ,0.00346
+ ,0.00003
+ ,0.00169
+ ,0.00213
+ ,151.737
+ ,190.204
+ ,129.859
+ ,0.00314
+ ,0.00002
+ ,0.00135
+ ,0.00162
+ ,148.79
+ ,158.359
+ ,138.99
+ ,0.00309
+ ,0.00002
+ ,0.00152
+ ,0.00186
+ ,148.143
+ ,155.982
+ ,135.041
+ ,0.00392
+ ,0.00003
+ ,0.00204
+ ,0.00231
+ ,150.44
+ ,163.441
+ ,144.736
+ ,0.00396
+ ,0.00003
+ ,0.00206
+ ,0.00233
+ ,148.462
+ ,161.078
+ ,141.998
+ ,0.00397
+ ,0.00003
+ ,0.00202
+ ,0.00235
+ ,149.818
+ ,163.417
+ ,144.786
+ ,0.00336
+ ,0.00002
+ ,0.00174
+ ,0.00198
+ ,117.226
+ ,123.925
+ ,106.656
+ ,0.00417
+ ,0.00004
+ ,0.00186
+ ,0.0027
+ ,116.848
+ ,217.552
+ ,99.503
+ ,0.00531
+ ,0.00005
+ ,0.0026
+ ,0.00346
+ ,116.286
+ ,177.291
+ ,96.983
+ ,0.00314
+ ,0.00003
+ ,0.00134
+ ,0.00192
+ ,116.556
+ ,592.03
+ ,86.228
+ ,0.00496
+ ,0.00004
+ ,0.00254
+ ,0.00263
+ ,116.342
+ ,581.289
+ ,94.246
+ ,0.00267
+ ,0.00002
+ ,0.00115
+ ,0.00148
+ ,114.563
+ ,119.167
+ ,86.647
+ ,0.00327
+ ,0.00003
+ ,0.00146
+ ,0.00184
+ ,201.774
+ ,262.707
+ ,78.228
+ ,0.00694
+ ,0.00003
+ ,0.00412
+ ,0.00396
+ ,174.188
+ ,230.978
+ ,94.261
+ ,0.00459
+ ,0.00003
+ ,0.00263
+ ,0.00259
+ ,209.516
+ ,253.017
+ ,89.488
+ ,0.00564
+ ,0.00003
+ ,0.00331
+ ,0.00292
+ ,174.688
+ ,240.005
+ ,74.287
+ ,0.0136
+ ,0.00008
+ ,0.00624
+ ,0.00564
+ ,198.764
+ ,396.961
+ ,74.904
+ ,0.0074
+ ,0.00004
+ ,0.0037
+ ,0.0039
+ ,214.289
+ ,260.277
+ ,77.973
+ ,0.00567
+ ,0.00003
+ ,0.00295
+ ,0.00317)
+ ,dim=c(7
+ ,195)
+ ,dimnames=list(c('MDVP:Fo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Flo(Hz)'
+ ,'MDVP:Jitter(%)'
+ ,'MDVP:Jitter(Abs)'
+ ,'MDVP:RAP'
+ ,'MDVP:PPQ')
+ ,1:195))
> y <- array(NA,dim=c(7,195),dimnames=list(c('MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ'),1:195))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '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:Flo(Hz) MDVP:Jitter(%) MDVP:Jitter(Abs)
1 119.992 157.302 74.997 0.00784 7.0e-05
2 122.400 148.650 113.819 0.00968 8.0e-05
3 116.682 131.111 111.555 0.01050 9.0e-05
4 116.676 137.871 111.366 0.00997 9.0e-05
5 116.014 141.781 110.655 0.01284 1.1e-04
6 120.552 131.162 113.787 0.00968 8.0e-05
7 120.267 137.244 114.820 0.00333 3.0e-05
8 107.332 113.840 104.315 0.00290 3.0e-05
9 95.730 132.068 91.754 0.00551 6.0e-05
10 95.056 120.103 91.226 0.00532 6.0e-05
11 88.333 112.240 84.072 0.00505 6.0e-05
12 91.904 115.871 86.292 0.00540 6.0e-05
13 136.926 159.866 131.276 0.00293 2.0e-05
14 139.173 179.139 76.556 0.00390 3.0e-05
15 152.845 163.305 75.836 0.00294 2.0e-05
16 142.167 217.455 83.159 0.00369 3.0e-05
17 144.188 349.259 82.764 0.00544 4.0e-05
18 168.778 232.181 75.603 0.00718 4.0e-05
19 153.046 175.829 68.623 0.00742 5.0e-05
20 156.405 189.398 142.822 0.00768 5.0e-05
21 153.848 165.738 65.782 0.00840 5.0e-05
22 153.880 172.860 78.128 0.00480 3.0e-05
23 167.930 193.221 79.068 0.00442 3.0e-05
24 173.917 192.735 86.180 0.00476 3.0e-05
25 163.656 200.841 76.779 0.00742 5.0e-05
26 104.400 206.002 77.968 0.00633 6.0e-05
27 171.041 208.313 75.501 0.00455 3.0e-05
28 146.845 208.701 81.737 0.00496 3.0e-05
29 155.358 227.383 80.055 0.00310 2.0e-05
30 162.568 198.346 77.630 0.00502 3.0e-05
31 197.076 206.896 192.055 0.00289 1.0e-05
32 199.228 209.512 192.091 0.00241 1.0e-05
33 198.383 215.203 193.104 0.00212 1.0e-05
34 202.266 211.604 197.079 0.00180 9.0e-06
35 203.184 211.526 196.160 0.00178 9.0e-06
36 201.464 210.565 195.708 0.00198 1.0e-05
37 177.876 192.921 168.013 0.00411 2.0e-05
38 176.170 185.604 163.564 0.00369 2.0e-05
39 180.198 201.249 175.456 0.00284 2.0e-05
40 187.733 202.324 173.015 0.00316 2.0e-05
41 186.163 197.724 177.584 0.00298 2.0e-05
42 184.055 196.537 166.977 0.00258 1.0e-05
43 237.226 247.326 225.227 0.00298 1.0e-05
44 241.404 248.834 232.483 0.00281 1.0e-05
45 243.439 250.912 232.435 0.00210 9.0e-06
46 242.852 255.034 227.911 0.00225 9.0e-06
47 245.510 262.090 231.848 0.00235 1.0e-05
48 252.455 261.487 182.786 0.00185 7.0e-06
49 122.188 128.611 115.765 0.00524 4.0e-05
50 122.964 130.049 114.676 0.00428 3.0e-05
51 124.445 135.069 117.495 0.00431 3.0e-05
52 126.344 134.231 112.773 0.00448 4.0e-05
53 128.001 138.052 122.080 0.00436 3.0e-05
54 129.336 139.867 118.604 0.00490 4.0e-05
55 108.807 134.656 102.874 0.00761 7.0e-05
56 109.860 126.358 104.437 0.00874 8.0e-05
57 110.417 131.067 103.370 0.00784 7.0e-05
58 117.274 129.916 110.402 0.00752 6.0e-05
59 116.879 131.897 108.153 0.00788 7.0e-05
60 114.847 271.314 104.680 0.00867 8.0e-05
61 209.144 237.494 109.379 0.00282 1.0e-05
62 223.365 238.987 98.664 0.00264 1.0e-05
63 222.236 231.345 205.495 0.00266 1.0e-05
64 228.832 234.619 223.634 0.00296 1.0e-05
65 229.401 252.221 221.156 0.00205 9.0e-06
66 228.969 239.541 113.201 0.00238 1.0e-05
67 140.341 159.774 67.021 0.00817 6.0e-05
68 136.969 166.607 66.004 0.00923 7.0e-05
69 143.533 162.215 65.809 0.01101 8.0e-05
70 148.090 162.824 67.343 0.00762 5.0e-05
71 142.729 162.408 65.476 0.00831 6.0e-05
72 136.358 176.595 65.750 0.00971 7.0e-05
73 120.080 139.710 111.208 0.00405 3.0e-05
74 112.014 588.518 107.024 0.00533 5.0e-05
75 110.793 128.101 107.316 0.00494 4.0e-05
76 110.707 122.611 105.007 0.00516 5.0e-05
77 112.876 148.826 106.981 0.00500 4.0e-05
78 110.568 125.394 106.821 0.00462 4.0e-05
79 95.385 102.145 90.264 0.00608 6.0e-05
80 100.770 115.697 85.545 0.01038 1.0e-04
81 96.106 108.664 84.510 0.00694 7.0e-05
82 95.605 107.715 87.549 0.00702 7.0e-05
83 100.960 110.019 95.628 0.00606 6.0e-05
84 98.804 102.305 87.804 0.00432 4.0e-05
85 176.858 205.560 75.344 0.00747 4.0e-05
86 180.978 200.125 155.495 0.00406 2.0e-05
87 178.222 202.450 141.047 0.00321 2.0e-05
88 176.281 227.381 125.610 0.00520 3.0e-05
89 173.898 211.350 74.677 0.00448 3.0e-05
90 179.711 225.930 144.878 0.00709 4.0e-05
91 166.605 206.008 78.032 0.00742 4.0e-05
92 151.955 163.335 147.226 0.00419 3.0e-05
93 148.272 164.989 142.299 0.00459 3.0e-05
94 152.125 161.469 76.596 0.00382 3.0e-05
95 157.821 172.975 68.401 0.00358 2.0e-05
96 157.447 163.267 149.605 0.00369 2.0e-05
97 159.116 168.913 144.811 0.00342 2.0e-05
98 125.036 143.946 116.187 0.01280 1.0e-04
99 125.791 140.557 96.206 0.01378 1.1e-04
100 126.512 141.756 99.770 0.01936 1.5e-04
101 125.641 141.068 116.346 0.03316 2.6e-04
102 128.451 150.449 75.632 0.01551 1.2e-04
103 139.224 586.567 66.157 0.03011 2.2e-04
104 150.258 154.609 75.349 0.00248 2.0e-05
105 154.003 160.267 128.621 0.00183 1.0e-05
106 149.689 160.368 133.608 0.00257 2.0e-05
107 155.078 163.736 144.148 0.00168 1.0e-05
108 151.884 157.765 133.751 0.00258 2.0e-05
109 151.989 157.339 132.857 0.00174 1.0e-05
110 193.030 208.900 80.297 0.00766 4.0e-05
111 200.714 223.982 89.686 0.00621 3.0e-05
112 208.519 220.315 199.020 0.00609 3.0e-05
113 204.664 221.300 189.621 0.00841 4.0e-05
114 210.141 232.706 185.258 0.00534 3.0e-05
115 206.327 226.355 92.020 0.00495 2.0e-05
116 151.872 492.892 69.085 0.00856 6.0e-05
117 158.219 442.557 71.948 0.00476 3.0e-05
118 170.756 450.247 79.032 0.00555 3.0e-05
119 178.285 442.824 82.063 0.00462 3.0e-05
120 217.116 233.481 93.978 0.00404 2.0e-05
121 128.940 479.697 88.251 0.00581 5.0e-05
122 176.824 215.293 83.961 0.00460 3.0e-05
123 138.190 203.522 83.340 0.00704 5.0e-05
124 182.018 197.173 79.187 0.00842 5.0e-05
125 156.239 195.107 79.820 0.00694 4.0e-05
126 145.174 198.109 80.637 0.00733 5.0e-05
127 138.145 197.238 81.114 0.00544 4.0e-05
128 166.888 198.966 79.512 0.00638 4.0e-05
129 119.031 127.533 109.216 0.00440 4.0e-05
130 120.078 126.632 105.667 0.00270 2.0e-05
131 120.289 128.143 100.209 0.00492 4.0e-05
132 120.256 125.306 104.773 0.00407 3.0e-05
133 119.056 125.213 86.795 0.00346 3.0e-05
134 118.747 123.723 109.836 0.00331 3.0e-05
135 106.516 112.777 93.105 0.00589 6.0e-05
136 110.453 127.611 105.554 0.00494 4.0e-05
137 113.400 133.344 107.816 0.00451 4.0e-05
138 113.166 130.270 100.673 0.00502 4.0e-05
139 112.239 126.609 104.095 0.00472 4.0e-05
140 116.150 131.731 109.815 0.00381 3.0e-05
141 170.368 268.796 79.543 0.00571 3.0e-05
142 208.083 253.792 91.802 0.00757 4.0e-05
143 198.458 219.290 148.691 0.00376 2.0e-05
144 202.805 231.508 86.232 0.00370 2.0e-05
145 202.544 241.350 164.168 0.00254 1.0e-05
146 223.361 263.872 87.638 0.00352 2.0e-05
147 169.774 191.759 151.451 0.01568 9.0e-05
148 183.520 216.814 161.340 0.01466 8.0e-05
149 188.620 216.302 165.982 0.01719 9.0e-05
150 202.632 565.740 177.258 0.01627 8.0e-05
151 186.695 211.961 149.442 0.01872 1.0e-04
152 192.818 224.429 168.793 0.03107 1.6e-04
153 198.116 233.099 174.478 0.02714 1.4e-04
154 121.345 139.644 98.250 0.00684 6.0e-05
155 119.100 128.442 88.833 0.00692 6.0e-05
156 117.870 127.349 95.654 0.00647 5.0e-05
157 122.336 142.369 94.794 0.00727 6.0e-05
158 117.963 134.209 100.757 0.01813 1.5e-04
159 126.144 154.284 97.543 0.00975 8.0e-05
160 127.930 138.752 112.173 0.00605 5.0e-05
161 114.238 124.393 77.022 0.00581 5.0e-05
162 115.322 135.738 107.802 0.00619 5.0e-05
163 114.554 126.778 91.121 0.00651 6.0e-05
164 112.150 131.669 97.527 0.00519 5.0e-05
165 102.273 142.830 85.902 0.00907 9.0e-05
166 236.200 244.663 102.137 0.00277 1.0e-05
167 237.323 243.709 229.256 0.00303 1.0e-05
168 260.105 264.919 237.303 0.00339 1.0e-05
169 197.569 217.627 90.794 0.00803 4.0e-05
170 240.301 245.135 219.783 0.00517 2.0e-05
171 244.990 272.210 239.170 0.00451 2.0e-05
172 112.547 133.374 105.715 0.00355 3.0e-05
173 110.739 113.597 100.139 0.00356 3.0e-05
174 113.715 116.443 96.913 0.00349 3.0e-05
175 117.004 144.466 99.923 0.00353 3.0e-05
176 115.380 123.109 108.634 0.00332 3.0e-05
177 116.388 129.038 108.970 0.00346 3.0e-05
178 151.737 190.204 129.859 0.00314 2.0e-05
179 148.790 158.359 138.990 0.00309 2.0e-05
180 148.143 155.982 135.041 0.00392 3.0e-05
181 150.440 163.441 144.736 0.00396 3.0e-05
182 148.462 161.078 141.998 0.00397 3.0e-05
183 149.818 163.417 144.786 0.00336 2.0e-05
184 117.226 123.925 106.656 0.00417 4.0e-05
185 116.848 217.552 99.503 0.00531 5.0e-05
186 116.286 177.291 96.983 0.00314 3.0e-05
187 116.556 592.030 86.228 0.00496 4.0e-05
188 116.342 581.289 94.246 0.00267 2.0e-05
189 114.563 119.167 86.647 0.00327 3.0e-05
190 201.774 262.707 78.228 0.00694 3.0e-05
191 174.188 230.978 94.261 0.00459 3.0e-05
192 209.516 253.017 89.488 0.00564 3.0e-05
193 174.688 240.005 74.287 0.01360 8.0e-05
194 198.764 396.961 74.904 0.00740 4.0e-05
195 214.289 260.277 77.973 0.00567 3.0e-05
MDVP:RAP MDVP:PPQ
1 0.00370 0.00554
2 0.00465 0.00696
3 0.00544 0.00781
4 0.00502 0.00698
5 0.00655 0.00908
6 0.00463 0.00750
7 0.00155 0.00202
8 0.00144 0.00182
9 0.00293 0.00332
10 0.00268 0.00332
11 0.00254 0.00330
12 0.00281 0.00336
13 0.00118 0.00153
14 0.00165 0.00208
15 0.00121 0.00149
16 0.00157 0.00203
17 0.00211 0.00292
18 0.00284 0.00387
19 0.00364 0.00432
20 0.00372 0.00399
21 0.00428 0.00450
22 0.00232 0.00267
23 0.00220 0.00247
24 0.00221 0.00258
25 0.00380 0.00390
26 0.00316 0.00375
27 0.00250 0.00234
28 0.00250 0.00275
29 0.00159 0.00176
30 0.00280 0.00253
31 0.00166 0.00168
32 0.00134 0.00138
33 0.00113 0.00135
34 0.00093 0.00107
35 0.00094 0.00106
36 0.00105 0.00115
37 0.00233 0.00241
38 0.00205 0.00218
39 0.00153 0.00166
40 0.00168 0.00182
41 0.00165 0.00175
42 0.00134 0.00147
43 0.00169 0.00182
44 0.00157 0.00173
45 0.00109 0.00137
46 0.00117 0.00139
47 0.00127 0.00148
48 0.00092 0.00113
49 0.00169 0.00203
50 0.00124 0.00155
51 0.00141 0.00167
52 0.00131 0.00169
53 0.00137 0.00166
54 0.00165 0.00183
55 0.00349 0.00486
56 0.00398 0.00539
57 0.00352 0.00514
58 0.00299 0.00469
59 0.00334 0.00493
60 0.00373 0.00520
61 0.00147 0.00152
62 0.00154 0.00151
63 0.00152 0.00144
64 0.00175 0.00155
65 0.00114 0.00113
66 0.00136 0.00140
67 0.00430 0.00440
68 0.00507 0.00463
69 0.00647 0.00467
70 0.00467 0.00354
71 0.00469 0.00419
72 0.00534 0.00478
73 0.00180 0.00220
74 0.00268 0.00329
75 0.00260 0.00283
76 0.00277 0.00289
77 0.00270 0.00289
78 0.00226 0.00280
79 0.00331 0.00332
80 0.00622 0.00576
81 0.00389 0.00415
82 0.00428 0.00371
83 0.00351 0.00348
84 0.00247 0.00258
85 0.00418 0.00420
86 0.00220 0.00244
87 0.00163 0.00194
88 0.00287 0.00312
89 0.00237 0.00254
90 0.00391 0.00419
91 0.00387 0.00453
92 0.00224 0.00227
93 0.00250 0.00256
94 0.00191 0.00226
95 0.00196 0.00196
96 0.00201 0.00197
97 0.00178 0.00184
98 0.00743 0.00623
99 0.00826 0.00655
100 0.01159 0.00990
101 0.02144 0.01522
102 0.00905 0.00909
103 0.01854 0.01628
104 0.00105 0.00136
105 0.00076 0.00100
106 0.00116 0.00134
107 0.00068 0.00092
108 0.00115 0.00122
109 0.00075 0.00096
110 0.00450 0.00389
111 0.00371 0.00337
112 0.00368 0.00339
113 0.00502 0.00485
114 0.00321 0.00280
115 0.00302 0.00246
116 0.00404 0.00385
117 0.00214 0.00207
118 0.00244 0.00261
119 0.00157 0.00194
120 0.00127 0.00128
121 0.00241 0.00314
122 0.00209 0.00221
123 0.00406 0.00398
124 0.00506 0.00449
125 0.00403 0.00395
126 0.00414 0.00422
127 0.00294 0.00327
128 0.00368 0.00351
129 0.00214 0.00192
130 0.00116 0.00135
131 0.00269 0.00238
132 0.00224 0.00205
133 0.00169 0.00170
134 0.00168 0.00171
135 0.00291 0.00319
136 0.00244 0.00315
137 0.00219 0.00283
138 0.00257 0.00312
139 0.00238 0.00290
140 0.00181 0.00232
141 0.00232 0.00269
142 0.00428 0.00428
143 0.00182 0.00215
144 0.00189 0.00211
145 0.00100 0.00133
146 0.00169 0.00188
147 0.00863 0.00946
148 0.00849 0.00819
149 0.00996 0.01027
150 0.00919 0.00963
151 0.01075 0.01154
152 0.01800 0.01958
153 0.01568 0.01699
154 0.00388 0.00332
155 0.00393 0.00300
156 0.00356 0.00300
157 0.00415 0.00339
158 0.01117 0.00718
159 0.00593 0.00454
160 0.00321 0.00318
161 0.00299 0.00316
162 0.00352 0.00329
163 0.00366 0.00340
164 0.00291 0.00284
165 0.00493 0.00461
166 0.00154 0.00153
167 0.00173 0.00159
168 0.00205 0.00186
169 0.00490 0.00448
170 0.00316 0.00283
171 0.00279 0.00237
172 0.00166 0.00190
173 0.00170 0.00200
174 0.00171 0.00203
175 0.00176 0.00218
176 0.00160 0.00199
177 0.00169 0.00213
178 0.00135 0.00162
179 0.00152 0.00186
180 0.00204 0.00231
181 0.00206 0.00233
182 0.00202 0.00235
183 0.00174 0.00198
184 0.00186 0.00270
185 0.00260 0.00346
186 0.00134 0.00192
187 0.00254 0.00263
188 0.00115 0.00148
189 0.00146 0.00184
190 0.00412 0.00396
191 0.00263 0.00259
192 0.00331 0.00292
193 0.00624 0.00564
194 0.00370 0.00390
195 0.00295 0.00317
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `MDVP:Fhi(Hz)` `MDVP:Flo(Hz)` `MDVP:Jitter(%)`
1.211e+02 8.013e-02 2.872e-01 7.471e+03
`MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ`
-1.983e+06 1.461e+04 -6.857e+03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-65.292 -14.417 -1.565 14.205 57.561
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.211e+02 7.239e+00 16.721 < 2e-16 ***
`MDVP:Fhi(Hz)` 8.013e-02 1.768e-02 4.532 1.04e-05 ***
`MDVP:Flo(Hz)` 2.872e-01 4.041e-02 7.106 2.41e-11 ***
`MDVP:Jitter(%)` 7.471e+03 3.394e+03 2.201 0.028941 *
`MDVP:Jitter(Abs)` -1.983e+06 1.438e+05 -13.787 < 2e-16 ***
`MDVP:RAP` 1.461e+04 3.953e+03 3.696 0.000287 ***
`MDVP:PPQ` -6.857e+03 2.588e+03 -2.650 0.008740 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 20.74 on 188 degrees of freedom
Multiple R-squared: 0.7567, Adjusted R-squared: 0.7489
F-statistic: 97.45 on 6 and 188 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,] 1.208914e-02 2.417827e-02 0.98791086
[2,] 2.115518e-03 4.231036e-03 0.99788448
[3,] 3.299734e-04 6.599469e-04 0.99967003
[4,] 4.804118e-05 9.608236e-05 0.99995196
[5,] 1.101170e-05 2.202340e-05 0.99998899
[6,] 8.433055e-05 1.686611e-04 0.99991567
[7,] 1.832268e-05 3.664537e-05 0.99998168
[8,] 1.701085e-05 3.402169e-05 0.99998299
[9,] 2.872696e-05 5.745392e-05 0.99997127
[10,] 1.352156e-05 2.704311e-05 0.99998648
[11,] 4.534008e-06 9.068016e-06 0.99999547
[12,] 1.342578e-05 2.685157e-05 0.99998657
[13,] 5.379286e-06 1.075857e-05 0.99999462
[14,] 5.327619e-05 1.065524e-04 0.99994672
[15,] 1.898414e-04 3.796827e-04 0.99981016
[16,] 1.001254e-04 2.002508e-04 0.99989987
[17,] 1.015978e-04 2.031957e-04 0.99989840
[18,] 6.497470e-05 1.299494e-04 0.99993503
[19,] 1.416906e-04 2.833812e-04 0.99985831
[20,] 6.743820e-05 1.348764e-04 0.99993256
[21,] 3.137589e-05 6.275178e-05 0.99996862
[22,] 3.893583e-05 7.787167e-05 0.99996106
[23,] 4.953922e-05 9.907845e-05 0.99995046
[24,] 4.569896e-05 9.139792e-05 0.99995430
[25,] 6.117296e-05 1.223459e-04 0.99993883
[26,] 5.918442e-05 1.183688e-04 0.99994082
[27,] 3.984612e-05 7.969224e-05 0.99996015
[28,] 6.439691e-05 1.287938e-04 0.99993560
[29,] 4.316761e-05 8.633522e-05 0.99995683
[30,] 2.332417e-05 4.664833e-05 0.99997668
[31,] 1.394020e-05 2.788041e-05 0.99998606
[32,] 7.911368e-06 1.582274e-05 0.99999209
[33,] 4.707482e-06 9.414965e-06 0.99999529
[34,] 4.522209e-06 9.044417e-06 0.99999548
[35,] 5.690795e-06 1.138159e-05 0.99999431
[36,] 2.121514e-05 4.243029e-05 0.99997878
[37,] 3.752872e-05 7.505743e-05 0.99996247
[38,] 5.342525e-05 1.068505e-04 0.99994657
[39,] 1.030167e-03 2.060333e-03 0.99896983
[40,] 7.579145e-04 1.515829e-03 0.99924209
[41,] 5.315836e-04 1.063167e-03 0.99946842
[42,] 3.959332e-04 7.918664e-04 0.99960407
[43,] 4.202405e-04 8.404809e-04 0.99957976
[44,] 3.200784e-04 6.401568e-04 0.99967992
[45,] 2.334317e-04 4.668634e-04 0.99976657
[46,] 1.694456e-04 3.388912e-04 0.99983055
[47,] 1.819389e-04 3.638778e-04 0.99981806
[48,] 1.502417e-04 3.004834e-04 0.99984976
[49,] 1.058723e-04 2.117446e-04 0.99989413
[50,] 1.314623e-04 2.629246e-04 0.99986854
[51,] 2.709913e-04 5.419826e-04 0.99972901
[52,] 3.276857e-04 6.553714e-04 0.99967231
[53,] 1.411547e-03 2.823094e-03 0.99858845
[54,] 1.015839e-03 2.031678e-03 0.99898416
[55,] 7.116634e-04 1.423327e-03 0.99928834
[56,] 6.493553e-04 1.298711e-03 0.99935064
[57,] 3.642600e-03 7.285200e-03 0.99635740
[58,] 3.021032e-03 6.042064e-03 0.99697897
[59,] 2.379710e-03 4.759420e-03 0.99762029
[60,] 1.879126e-03 3.758252e-03 0.99812087
[61,] 1.745248e-03 3.490496e-03 0.99825475
[62,] 1.205726e-03 2.411452e-03 0.99879427
[63,] 8.319711e-04 1.663942e-03 0.99916803
[64,] 1.120867e-03 2.241733e-03 0.99887913
[65,] 6.504301e-02 1.300860e-01 0.93495699
[66,] 9.195479e-02 1.839096e-01 0.90804521
[67,] 7.686369e-02 1.537274e-01 0.92313631
[68,] 1.078442e-01 2.156884e-01 0.89215579
[69,] 1.092719e-01 2.185438e-01 0.89072811
[70,] 9.061920e-02 1.812384e-01 0.90938080
[71,] 1.078908e-01 2.157816e-01 0.89210918
[72,] 9.841052e-02 1.968210e-01 0.90158948
[73,] 8.119183e-02 1.623837e-01 0.91880817
[74,] 6.789241e-02 1.357848e-01 0.93210759
[75,] 7.790961e-02 1.558192e-01 0.92209039
[76,] 6.637699e-02 1.327540e-01 0.93362301
[77,] 6.347236e-02 1.269447e-01 0.93652764
[78,] 5.367123e-02 1.073425e-01 0.94632877
[79,] 4.645680e-02 9.291361e-02 0.95354320
[80,] 5.052928e-02 1.010586e-01 0.94947072
[81,] 5.187519e-02 1.037504e-01 0.94812481
[82,] 4.626260e-02 9.252521e-02 0.95373740
[83,] 4.128537e-02 8.257074e-02 0.95871463
[84,] 4.586496e-02 9.172993e-02 0.95413504
[85,] 4.435984e-02 8.871969e-02 0.95564016
[86,] 3.577426e-02 7.154852e-02 0.96422574
[87,] 4.413071e-02 8.826143e-02 0.95586929
[88,] 4.229308e-02 8.458616e-02 0.95770692
[89,] 3.380438e-02 6.760876e-02 0.96619562
[90,] 2.743757e-02 5.487515e-02 0.97256243
[91,] 2.585982e-02 5.171964e-02 0.97414018
[92,] 2.059354e-02 4.118708e-02 0.97940646
[93,] 3.860775e-02 7.721551e-02 0.96139225
[94,] 9.305126e-02 1.861025e-01 0.90694874
[95,] 8.403786e-02 1.680757e-01 0.91596214
[96,] 8.134040e-02 1.626808e-01 0.91865960
[97,] 6.885360e-02 1.377072e-01 0.93114640
[98,] 6.731608e-02 1.346322e-01 0.93268392
[99,] 5.619940e-02 1.123988e-01 0.94380060
[100,] 5.744454e-02 1.148891e-01 0.94255546
[101,] 4.723586e-02 9.447171e-02 0.95276414
[102,] 3.892255e-02 7.784511e-02 0.96107745
[103,] 3.478470e-02 6.956940e-02 0.96521530
[104,] 4.319815e-02 8.639629e-02 0.95680185
[105,] 3.551872e-02 7.103744e-02 0.96448128
[106,] 2.986600e-02 5.973199e-02 0.97013400
[107,] 2.334597e-02 4.669195e-02 0.97665403
[108,] 2.001625e-02 4.003249e-02 0.97998375
[109,] 1.713680e-02 3.427360e-02 0.98286320
[110,] 1.661922e-02 3.323845e-02 0.98338078
[111,] 5.082697e-02 1.016539e-01 0.94917303
[112,] 4.876980e-02 9.753961e-02 0.95123020
[113,] 5.383920e-02 1.076784e-01 0.94616080
[114,] 4.561257e-02 9.122515e-02 0.95438743
[115,] 3.684663e-02 7.369325e-02 0.96315337
[116,] 3.438905e-02 6.877809e-02 0.96561095
[117,] 2.773881e-02 5.547762e-02 0.97226119
[118,] 2.210500e-02 4.421000e-02 0.97789500
[119,] 1.715385e-02 3.430771e-02 0.98284615
[120,] 1.409895e-02 2.819790e-02 0.98590105
[121,] 2.269925e-02 4.539851e-02 0.97730075
[122,] 2.320456e-02 4.640911e-02 0.97679544
[123,] 4.163794e-02 8.327587e-02 0.95836206
[124,] 4.010837e-02 8.021674e-02 0.95989163
[125,] 4.107165e-02 8.214329e-02 0.95892835
[126,] 4.937627e-02 9.875254e-02 0.95062373
[127,] 4.716584e-02 9.433168e-02 0.95283416
[128,] 3.965875e-02 7.931750e-02 0.96034125
[129,] 3.713780e-02 7.427561e-02 0.96286220
[130,] 3.227792e-02 6.455584e-02 0.96772208
[131,] 3.650389e-02 7.300779e-02 0.96349611
[132,] 2.836916e-02 5.673832e-02 0.97163084
[133,] 2.888410e-02 5.776820e-02 0.97111590
[134,] 2.609709e-02 5.219419e-02 0.97390291
[135,] 4.151969e-02 8.303939e-02 0.95848031
[136,] 3.346319e-02 6.692638e-02 0.96653681
[137,] 1.814385e-01 3.628771e-01 0.81856147
[138,] 2.110759e-01 4.221518e-01 0.78892412
[139,] 2.465547e-01 4.931093e-01 0.75344535
[140,] 2.784836e-01 5.569672e-01 0.72151642
[141,] 3.800358e-01 7.600715e-01 0.61996424
[142,] 3.458922e-01 6.917844e-01 0.65410781
[143,] 3.537043e-01 7.074085e-01 0.64629573
[144,] 6.635102e-01 6.729795e-01 0.33648975
[145,] 6.171376e-01 7.657247e-01 0.38286235
[146,] 5.717477e-01 8.565045e-01 0.42825226
[147,] 5.624965e-01 8.750070e-01 0.43750350
[148,] 5.110885e-01 9.778230e-01 0.48891149
[149,] 4.614424e-01 9.228849e-01 0.53855756
[150,] 4.312435e-01 8.624871e-01 0.56875647
[151,] 3.789846e-01 7.579692e-01 0.62101539
[152,] 3.259505e-01 6.519009e-01 0.67404953
[153,] 3.742687e-01 7.485373e-01 0.62573134
[154,] 3.193451e-01 6.386902e-01 0.68065491
[155,] 2.714526e-01 5.429051e-01 0.72854744
[156,] 4.725701e-01 9.451402e-01 0.52742990
[157,] 8.434183e-01 3.131634e-01 0.15658172
[158,] 8.145131e-01 3.709738e-01 0.18548690
[159,] 8.396441e-01 3.207117e-01 0.16035586
[160,] 9.131366e-01 1.737267e-01 0.08686336
[161,] 8.908316e-01 2.183368e-01 0.10916838
[162,] 8.984392e-01 2.031217e-01 0.10156084
[163,] 8.725036e-01 2.549927e-01 0.12749636
[164,] 8.591101e-01 2.817798e-01 0.14088991
[165,] 8.394781e-01 3.210438e-01 0.16052191
[166,] 8.295099e-01 3.409803e-01 0.17049015
[167,] 7.968833e-01 4.062335e-01 0.20311674
[168,] 7.952747e-01 4.094506e-01 0.20472528
[169,] 7.316957e-01 5.366086e-01 0.26830431
[170,] 6.494132e-01 7.011736e-01 0.35058681
[171,] 5.516794e-01 8.966412e-01 0.44832059
[172,] 4.605224e-01 9.210449e-01 0.53947757
[173,] 3.783931e-01 7.567863e-01 0.62160687
[174,] 2.741743e-01 5.483487e-01 0.72582566
[175,] 1.739434e-01 3.478868e-01 0.82605660
[176,] 9.934414e-02 1.986883e-01 0.90065586
> postscript(file="/var/wessaorg/rcomp/tmp/18rfg1386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2tcf11386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3y6gy1386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/43phb1386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ay3s1386167226.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
28.9681605 22.8613659 27.1888789 31.1005518 40.5933544 26.4190634
7 8 9 10 11 12
-18.9364688 -23.5310425 -4.4791520 1.0296847 0.9168305 -2.5892800
13 14 15 16 17 18
-23.6118946 2.2922123 7.1645009 2.7148866 -0.7429827 18.1327106
19 20 21 22 23 24
18.3547645 -6.0551033 5.3421781 4.5827902 19.9523539 22.0039727
25 26 27 28 29 30
19.4005020 -4.3130024 16.6321134 -9.6374021 -1.5649121 1.7542858
31 32 33 34 35 36
-10.2009562 -2.0641726 1.3733209 5.8132181 6.9361039 4.9216236
37 38 39 40 41 42
-15.4451444 -9.6352426 0.1070196 4.7714709 3.5611717 -9.6386319
43 44 45 46 47 48
17.0329322 21.4127985 31.1615409 29.3908990 30.7449065 52.3273866
49 50 51 52 53 54
-13.0112561 -21.4125612 -23.0285732 0.4527019 -20.8859038 -5.8272320
55 56 57 58 59 60
11.7163536 20.8485183 13.2348340 5.3830159 19.1480482 17.0236090
61 62 63 64 65 66
25.3587946 42.7905355 11.2587611 7.5358083 18.2547677 47.9939788
67 68 69 70 71 72
12.5286045 11.1386994 4.4606026 -7.0847045 6.9646044 3.2975564
73 74 75 76 77 78
-26.0817245 -44.1938244 -27.5085983 -10.3769317 -28.4880764 -20.2214746
79 80 81 82 83 84
-11.8096490 15.2537791 0.6637988 -9.9473771 -10.0816761 -27.0101285
85 86 87 88 89 90
8.9367055 -6.8468739 5.6103146 1.0408683 23.2763735 -3.0944475
91 92 93 94 95 96
5.0420546 -13.4379388 -20.6375285 14.6815664 0.9832717 -23.4154730
97 98 99 100 101 102
-16.3357439 -4.0806483 5.2594378 16.8102336 18.8270423 25.8150407
103 104 105 106 107 108
4.2315975 10.2973338 -14.9146601 -9.8798107 -16.8353732 -8.2687785
109 110 111 112 113 114
-17.3662332 15.1976751 17.9568001 -3.8690042 -12.1741902 9.1371518
115 116 117 118 119 120
16.1341356 -6.1117254 -12.0995039 -8.7957689 13.5236677 50.0675767
121 122 123 124 125 126
-13.8268245 24.1522210 -8.5758082 15.5287303 -7.6928824 -2.0716944
127 128 129 130 131 132
-3.8585862 9.0161037 -15.2550316 -29.6666463 -20.2265474 -30.5108626
133 134 135 136 137 138
-16.3469588 -21.8177165 4.0263713 -22.7711125 -16.2618474 -21.5724806
139 140 141 142 143 144
-19.6798215 -26.5024999 6.3155554 29.9110935 16.8565137 37.3115699
145 146 147 148 149 150
10.3730136 57.5605065 -10.0332099 -20.0080910 -22.4872961 -45.8084689
151 152 153 154 155 156
-13.7492519 -38.2682172 -29.4587150 -5.1545162 -7.3203428 -21.4840254
157 158 159 160 161 162
-10.0672162 5.2570278 -4.9930985 -7.5950244 -5.1718532 -23.5276342
163 164 165 166 167 168
-2.6387135 -10.1244117 15.3996989 53.3392963 13.7275517 26.9853044
169 170 171 172 173 174
11.4598315 10.7624172 14.8976658 -27.8055972 -26.4011140 -22.1441842
175 176 177 178 179 180
-21.9658268 -21.7760545 -22.7406227 -14.2607259 -17.7427798 -7.9477932
181 182 183 184 185 186
-9.4864564 -9.8419536 -23.1934013 -4.8773660 -4.9908790 -17.2019959
187 188 189 190 191 192
-53.5081870 -65.2923811 -14.5727302 11.8051607 12.0917788 31.5066015
193 194 195
17.6097187 21.1117409 45.7549912
> postscript(file="/var/wessaorg/rcomp/tmp/6ng3z1386167226.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 28.9681605 NA
1 22.8613659 28.9681605
2 27.1888789 22.8613659
3 31.1005518 27.1888789
4 40.5933544 31.1005518
5 26.4190634 40.5933544
6 -18.9364688 26.4190634
7 -23.5310425 -18.9364688
8 -4.4791520 -23.5310425
9 1.0296847 -4.4791520
10 0.9168305 1.0296847
11 -2.5892800 0.9168305
12 -23.6118946 -2.5892800
13 2.2922123 -23.6118946
14 7.1645009 2.2922123
15 2.7148866 7.1645009
16 -0.7429827 2.7148866
17 18.1327106 -0.7429827
18 18.3547645 18.1327106
19 -6.0551033 18.3547645
20 5.3421781 -6.0551033
21 4.5827902 5.3421781
22 19.9523539 4.5827902
23 22.0039727 19.9523539
24 19.4005020 22.0039727
25 -4.3130024 19.4005020
26 16.6321134 -4.3130024
27 -9.6374021 16.6321134
28 -1.5649121 -9.6374021
29 1.7542858 -1.5649121
30 -10.2009562 1.7542858
31 -2.0641726 -10.2009562
32 1.3733209 -2.0641726
33 5.8132181 1.3733209
34 6.9361039 5.8132181
35 4.9216236 6.9361039
36 -15.4451444 4.9216236
37 -9.6352426 -15.4451444
38 0.1070196 -9.6352426
39 4.7714709 0.1070196
40 3.5611717 4.7714709
41 -9.6386319 3.5611717
42 17.0329322 -9.6386319
43 21.4127985 17.0329322
44 31.1615409 21.4127985
45 29.3908990 31.1615409
46 30.7449065 29.3908990
47 52.3273866 30.7449065
48 -13.0112561 52.3273866
49 -21.4125612 -13.0112561
50 -23.0285732 -21.4125612
51 0.4527019 -23.0285732
52 -20.8859038 0.4527019
53 -5.8272320 -20.8859038
54 11.7163536 -5.8272320
55 20.8485183 11.7163536
56 13.2348340 20.8485183
57 5.3830159 13.2348340
58 19.1480482 5.3830159
59 17.0236090 19.1480482
60 25.3587946 17.0236090
61 42.7905355 25.3587946
62 11.2587611 42.7905355
63 7.5358083 11.2587611
64 18.2547677 7.5358083
65 47.9939788 18.2547677
66 12.5286045 47.9939788
67 11.1386994 12.5286045
68 4.4606026 11.1386994
69 -7.0847045 4.4606026
70 6.9646044 -7.0847045
71 3.2975564 6.9646044
72 -26.0817245 3.2975564
73 -44.1938244 -26.0817245
74 -27.5085983 -44.1938244
75 -10.3769317 -27.5085983
76 -28.4880764 -10.3769317
77 -20.2214746 -28.4880764
78 -11.8096490 -20.2214746
79 15.2537791 -11.8096490
80 0.6637988 15.2537791
81 -9.9473771 0.6637988
82 -10.0816761 -9.9473771
83 -27.0101285 -10.0816761
84 8.9367055 -27.0101285
85 -6.8468739 8.9367055
86 5.6103146 -6.8468739
87 1.0408683 5.6103146
88 23.2763735 1.0408683
89 -3.0944475 23.2763735
90 5.0420546 -3.0944475
91 -13.4379388 5.0420546
92 -20.6375285 -13.4379388
93 14.6815664 -20.6375285
94 0.9832717 14.6815664
95 -23.4154730 0.9832717
96 -16.3357439 -23.4154730
97 -4.0806483 -16.3357439
98 5.2594378 -4.0806483
99 16.8102336 5.2594378
100 18.8270423 16.8102336
101 25.8150407 18.8270423
102 4.2315975 25.8150407
103 10.2973338 4.2315975
104 -14.9146601 10.2973338
105 -9.8798107 -14.9146601
106 -16.8353732 -9.8798107
107 -8.2687785 -16.8353732
108 -17.3662332 -8.2687785
109 15.1976751 -17.3662332
110 17.9568001 15.1976751
111 -3.8690042 17.9568001
112 -12.1741902 -3.8690042
113 9.1371518 -12.1741902
114 16.1341356 9.1371518
115 -6.1117254 16.1341356
116 -12.0995039 -6.1117254
117 -8.7957689 -12.0995039
118 13.5236677 -8.7957689
119 50.0675767 13.5236677
120 -13.8268245 50.0675767
121 24.1522210 -13.8268245
122 -8.5758082 24.1522210
123 15.5287303 -8.5758082
124 -7.6928824 15.5287303
125 -2.0716944 -7.6928824
126 -3.8585862 -2.0716944
127 9.0161037 -3.8585862
128 -15.2550316 9.0161037
129 -29.6666463 -15.2550316
130 -20.2265474 -29.6666463
131 -30.5108626 -20.2265474
132 -16.3469588 -30.5108626
133 -21.8177165 -16.3469588
134 4.0263713 -21.8177165
135 -22.7711125 4.0263713
136 -16.2618474 -22.7711125
137 -21.5724806 -16.2618474
138 -19.6798215 -21.5724806
139 -26.5024999 -19.6798215
140 6.3155554 -26.5024999
141 29.9110935 6.3155554
142 16.8565137 29.9110935
143 37.3115699 16.8565137
144 10.3730136 37.3115699
145 57.5605065 10.3730136
146 -10.0332099 57.5605065
147 -20.0080910 -10.0332099
148 -22.4872961 -20.0080910
149 -45.8084689 -22.4872961
150 -13.7492519 -45.8084689
151 -38.2682172 -13.7492519
152 -29.4587150 -38.2682172
153 -5.1545162 -29.4587150
154 -7.3203428 -5.1545162
155 -21.4840254 -7.3203428
156 -10.0672162 -21.4840254
157 5.2570278 -10.0672162
158 -4.9930985 5.2570278
159 -7.5950244 -4.9930985
160 -5.1718532 -7.5950244
161 -23.5276342 -5.1718532
162 -2.6387135 -23.5276342
163 -10.1244117 -2.6387135
164 15.3996989 -10.1244117
165 53.3392963 15.3996989
166 13.7275517 53.3392963
167 26.9853044 13.7275517
168 11.4598315 26.9853044
169 10.7624172 11.4598315
170 14.8976658 10.7624172
171 -27.8055972 14.8976658
172 -26.4011140 -27.8055972
173 -22.1441842 -26.4011140
174 -21.9658268 -22.1441842
175 -21.7760545 -21.9658268
176 -22.7406227 -21.7760545
177 -14.2607259 -22.7406227
178 -17.7427798 -14.2607259
179 -7.9477932 -17.7427798
180 -9.4864564 -7.9477932
181 -9.8419536 -9.4864564
182 -23.1934013 -9.8419536
183 -4.8773660 -23.1934013
184 -4.9908790 -4.8773660
185 -17.2019959 -4.9908790
186 -53.5081870 -17.2019959
187 -65.2923811 -53.5081870
188 -14.5727302 -65.2923811
189 11.8051607 -14.5727302
190 12.0917788 11.8051607
191 31.5066015 12.0917788
192 17.6097187 31.5066015
193 21.1117409 17.6097187
194 45.7549912 21.1117409
195 NA 45.7549912
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 22.8613659 28.9681605
[2,] 27.1888789 22.8613659
[3,] 31.1005518 27.1888789
[4,] 40.5933544 31.1005518
[5,] 26.4190634 40.5933544
[6,] -18.9364688 26.4190634
[7,] -23.5310425 -18.9364688
[8,] -4.4791520 -23.5310425
[9,] 1.0296847 -4.4791520
[10,] 0.9168305 1.0296847
[11,] -2.5892800 0.9168305
[12,] -23.6118946 -2.5892800
[13,] 2.2922123 -23.6118946
[14,] 7.1645009 2.2922123
[15,] 2.7148866 7.1645009
[16,] -0.7429827 2.7148866
[17,] 18.1327106 -0.7429827
[18,] 18.3547645 18.1327106
[19,] -6.0551033 18.3547645
[20,] 5.3421781 -6.0551033
[21,] 4.5827902 5.3421781
[22,] 19.9523539 4.5827902
[23,] 22.0039727 19.9523539
[24,] 19.4005020 22.0039727
[25,] -4.3130024 19.4005020
[26,] 16.6321134 -4.3130024
[27,] -9.6374021 16.6321134
[28,] -1.5649121 -9.6374021
[29,] 1.7542858 -1.5649121
[30,] -10.2009562 1.7542858
[31,] -2.0641726 -10.2009562
[32,] 1.3733209 -2.0641726
[33,] 5.8132181 1.3733209
[34,] 6.9361039 5.8132181
[35,] 4.9216236 6.9361039
[36,] -15.4451444 4.9216236
[37,] -9.6352426 -15.4451444
[38,] 0.1070196 -9.6352426
[39,] 4.7714709 0.1070196
[40,] 3.5611717 4.7714709
[41,] -9.6386319 3.5611717
[42,] 17.0329322 -9.6386319
[43,] 21.4127985 17.0329322
[44,] 31.1615409 21.4127985
[45,] 29.3908990 31.1615409
[46,] 30.7449065 29.3908990
[47,] 52.3273866 30.7449065
[48,] -13.0112561 52.3273866
[49,] -21.4125612 -13.0112561
[50,] -23.0285732 -21.4125612
[51,] 0.4527019 -23.0285732
[52,] -20.8859038 0.4527019
[53,] -5.8272320 -20.8859038
[54,] 11.7163536 -5.8272320
[55,] 20.8485183 11.7163536
[56,] 13.2348340 20.8485183
[57,] 5.3830159 13.2348340
[58,] 19.1480482 5.3830159
[59,] 17.0236090 19.1480482
[60,] 25.3587946 17.0236090
[61,] 42.7905355 25.3587946
[62,] 11.2587611 42.7905355
[63,] 7.5358083 11.2587611
[64,] 18.2547677 7.5358083
[65,] 47.9939788 18.2547677
[66,] 12.5286045 47.9939788
[67,] 11.1386994 12.5286045
[68,] 4.4606026 11.1386994
[69,] -7.0847045 4.4606026
[70,] 6.9646044 -7.0847045
[71,] 3.2975564 6.9646044
[72,] -26.0817245 3.2975564
[73,] -44.1938244 -26.0817245
[74,] -27.5085983 -44.1938244
[75,] -10.3769317 -27.5085983
[76,] -28.4880764 -10.3769317
[77,] -20.2214746 -28.4880764
[78,] -11.8096490 -20.2214746
[79,] 15.2537791 -11.8096490
[80,] 0.6637988 15.2537791
[81,] -9.9473771 0.6637988
[82,] -10.0816761 -9.9473771
[83,] -27.0101285 -10.0816761
[84,] 8.9367055 -27.0101285
[85,] -6.8468739 8.9367055
[86,] 5.6103146 -6.8468739
[87,] 1.0408683 5.6103146
[88,] 23.2763735 1.0408683
[89,] -3.0944475 23.2763735
[90,] 5.0420546 -3.0944475
[91,] -13.4379388 5.0420546
[92,] -20.6375285 -13.4379388
[93,] 14.6815664 -20.6375285
[94,] 0.9832717 14.6815664
[95,] -23.4154730 0.9832717
[96,] -16.3357439 -23.4154730
[97,] -4.0806483 -16.3357439
[98,] 5.2594378 -4.0806483
[99,] 16.8102336 5.2594378
[100,] 18.8270423 16.8102336
[101,] 25.8150407 18.8270423
[102,] 4.2315975 25.8150407
[103,] 10.2973338 4.2315975
[104,] -14.9146601 10.2973338
[105,] -9.8798107 -14.9146601
[106,] -16.8353732 -9.8798107
[107,] -8.2687785 -16.8353732
[108,] -17.3662332 -8.2687785
[109,] 15.1976751 -17.3662332
[110,] 17.9568001 15.1976751
[111,] -3.8690042 17.9568001
[112,] -12.1741902 -3.8690042
[113,] 9.1371518 -12.1741902
[114,] 16.1341356 9.1371518
[115,] -6.1117254 16.1341356
[116,] -12.0995039 -6.1117254
[117,] -8.7957689 -12.0995039
[118,] 13.5236677 -8.7957689
[119,] 50.0675767 13.5236677
[120,] -13.8268245 50.0675767
[121,] 24.1522210 -13.8268245
[122,] -8.5758082 24.1522210
[123,] 15.5287303 -8.5758082
[124,] -7.6928824 15.5287303
[125,] -2.0716944 -7.6928824
[126,] -3.8585862 -2.0716944
[127,] 9.0161037 -3.8585862
[128,] -15.2550316 9.0161037
[129,] -29.6666463 -15.2550316
[130,] -20.2265474 -29.6666463
[131,] -30.5108626 -20.2265474
[132,] -16.3469588 -30.5108626
[133,] -21.8177165 -16.3469588
[134,] 4.0263713 -21.8177165
[135,] -22.7711125 4.0263713
[136,] -16.2618474 -22.7711125
[137,] -21.5724806 -16.2618474
[138,] -19.6798215 -21.5724806
[139,] -26.5024999 -19.6798215
[140,] 6.3155554 -26.5024999
[141,] 29.9110935 6.3155554
[142,] 16.8565137 29.9110935
[143,] 37.3115699 16.8565137
[144,] 10.3730136 37.3115699
[145,] 57.5605065 10.3730136
[146,] -10.0332099 57.5605065
[147,] -20.0080910 -10.0332099
[148,] -22.4872961 -20.0080910
[149,] -45.8084689 -22.4872961
[150,] -13.7492519 -45.8084689
[151,] -38.2682172 -13.7492519
[152,] -29.4587150 -38.2682172
[153,] -5.1545162 -29.4587150
[154,] -7.3203428 -5.1545162
[155,] -21.4840254 -7.3203428
[156,] -10.0672162 -21.4840254
[157,] 5.2570278 -10.0672162
[158,] -4.9930985 5.2570278
[159,] -7.5950244 -4.9930985
[160,] -5.1718532 -7.5950244
[161,] -23.5276342 -5.1718532
[162,] -2.6387135 -23.5276342
[163,] -10.1244117 -2.6387135
[164,] 15.3996989 -10.1244117
[165,] 53.3392963 15.3996989
[166,] 13.7275517 53.3392963
[167,] 26.9853044 13.7275517
[168,] 11.4598315 26.9853044
[169,] 10.7624172 11.4598315
[170,] 14.8976658 10.7624172
[171,] -27.8055972 14.8976658
[172,] -26.4011140 -27.8055972
[173,] -22.1441842 -26.4011140
[174,] -21.9658268 -22.1441842
[175,] -21.7760545 -21.9658268
[176,] -22.7406227 -21.7760545
[177,] -14.2607259 -22.7406227
[178,] -17.7427798 -14.2607259
[179,] -7.9477932 -17.7427798
[180,] -9.4864564 -7.9477932
[181,] -9.8419536 -9.4864564
[182,] -23.1934013 -9.8419536
[183,] -4.8773660 -23.1934013
[184,] -4.9908790 -4.8773660
[185,] -17.2019959 -4.9908790
[186,] -53.5081870 -17.2019959
[187,] -65.2923811 -53.5081870
[188,] -14.5727302 -65.2923811
[189,] 11.8051607 -14.5727302
[190,] 12.0917788 11.8051607
[191,] 31.5066015 12.0917788
[192,] 17.6097187 31.5066015
[193,] 21.1117409 17.6097187
[194,] 45.7549912 21.1117409
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 22.8613659 28.9681605
2 27.1888789 22.8613659
3 31.1005518 27.1888789
4 40.5933544 31.1005518
5 26.4190634 40.5933544
6 -18.9364688 26.4190634
7 -23.5310425 -18.9364688
8 -4.4791520 -23.5310425
9 1.0296847 -4.4791520
10 0.9168305 1.0296847
11 -2.5892800 0.9168305
12 -23.6118946 -2.5892800
13 2.2922123 -23.6118946
14 7.1645009 2.2922123
15 2.7148866 7.1645009
16 -0.7429827 2.7148866
17 18.1327106 -0.7429827
18 18.3547645 18.1327106
19 -6.0551033 18.3547645
20 5.3421781 -6.0551033
21 4.5827902 5.3421781
22 19.9523539 4.5827902
23 22.0039727 19.9523539
24 19.4005020 22.0039727
25 -4.3130024 19.4005020
26 16.6321134 -4.3130024
27 -9.6374021 16.6321134
28 -1.5649121 -9.6374021
29 1.7542858 -1.5649121
30 -10.2009562 1.7542858
31 -2.0641726 -10.2009562
32 1.3733209 -2.0641726
33 5.8132181 1.3733209
34 6.9361039 5.8132181
35 4.9216236 6.9361039
36 -15.4451444 4.9216236
37 -9.6352426 -15.4451444
38 0.1070196 -9.6352426
39 4.7714709 0.1070196
40 3.5611717 4.7714709
41 -9.6386319 3.5611717
42 17.0329322 -9.6386319
43 21.4127985 17.0329322
44 31.1615409 21.4127985
45 29.3908990 31.1615409
46 30.7449065 29.3908990
47 52.3273866 30.7449065
48 -13.0112561 52.3273866
49 -21.4125612 -13.0112561
50 -23.0285732 -21.4125612
51 0.4527019 -23.0285732
52 -20.8859038 0.4527019
53 -5.8272320 -20.8859038
54 11.7163536 -5.8272320
55 20.8485183 11.7163536
56 13.2348340 20.8485183
57 5.3830159 13.2348340
58 19.1480482 5.3830159
59 17.0236090 19.1480482
60 25.3587946 17.0236090
61 42.7905355 25.3587946
62 11.2587611 42.7905355
63 7.5358083 11.2587611
64 18.2547677 7.5358083
65 47.9939788 18.2547677
66 12.5286045 47.9939788
67 11.1386994 12.5286045
68 4.4606026 11.1386994
69 -7.0847045 4.4606026
70 6.9646044 -7.0847045
71 3.2975564 6.9646044
72 -26.0817245 3.2975564
73 -44.1938244 -26.0817245
74 -27.5085983 -44.1938244
75 -10.3769317 -27.5085983
76 -28.4880764 -10.3769317
77 -20.2214746 -28.4880764
78 -11.8096490 -20.2214746
79 15.2537791 -11.8096490
80 0.6637988 15.2537791
81 -9.9473771 0.6637988
82 -10.0816761 -9.9473771
83 -27.0101285 -10.0816761
84 8.9367055 -27.0101285
85 -6.8468739 8.9367055
86 5.6103146 -6.8468739
87 1.0408683 5.6103146
88 23.2763735 1.0408683
89 -3.0944475 23.2763735
90 5.0420546 -3.0944475
91 -13.4379388 5.0420546
92 -20.6375285 -13.4379388
93 14.6815664 -20.6375285
94 0.9832717 14.6815664
95 -23.4154730 0.9832717
96 -16.3357439 -23.4154730
97 -4.0806483 -16.3357439
98 5.2594378 -4.0806483
99 16.8102336 5.2594378
100 18.8270423 16.8102336
101 25.8150407 18.8270423
102 4.2315975 25.8150407
103 10.2973338 4.2315975
104 -14.9146601 10.2973338
105 -9.8798107 -14.9146601
106 -16.8353732 -9.8798107
107 -8.2687785 -16.8353732
108 -17.3662332 -8.2687785
109 15.1976751 -17.3662332
110 17.9568001 15.1976751
111 -3.8690042 17.9568001
112 -12.1741902 -3.8690042
113 9.1371518 -12.1741902
114 16.1341356 9.1371518
115 -6.1117254 16.1341356
116 -12.0995039 -6.1117254
117 -8.7957689 -12.0995039
118 13.5236677 -8.7957689
119 50.0675767 13.5236677
120 -13.8268245 50.0675767
121 24.1522210 -13.8268245
122 -8.5758082 24.1522210
123 15.5287303 -8.5758082
124 -7.6928824 15.5287303
125 -2.0716944 -7.6928824
126 -3.8585862 -2.0716944
127 9.0161037 -3.8585862
128 -15.2550316 9.0161037
129 -29.6666463 -15.2550316
130 -20.2265474 -29.6666463
131 -30.5108626 -20.2265474
132 -16.3469588 -30.5108626
133 -21.8177165 -16.3469588
134 4.0263713 -21.8177165
135 -22.7711125 4.0263713
136 -16.2618474 -22.7711125
137 -21.5724806 -16.2618474
138 -19.6798215 -21.5724806
139 -26.5024999 -19.6798215
140 6.3155554 -26.5024999
141 29.9110935 6.3155554
142 16.8565137 29.9110935
143 37.3115699 16.8565137
144 10.3730136 37.3115699
145 57.5605065 10.3730136
146 -10.0332099 57.5605065
147 -20.0080910 -10.0332099
148 -22.4872961 -20.0080910
149 -45.8084689 -22.4872961
150 -13.7492519 -45.8084689
151 -38.2682172 -13.7492519
152 -29.4587150 -38.2682172
153 -5.1545162 -29.4587150
154 -7.3203428 -5.1545162
155 -21.4840254 -7.3203428
156 -10.0672162 -21.4840254
157 5.2570278 -10.0672162
158 -4.9930985 5.2570278
159 -7.5950244 -4.9930985
160 -5.1718532 -7.5950244
161 -23.5276342 -5.1718532
162 -2.6387135 -23.5276342
163 -10.1244117 -2.6387135
164 15.3996989 -10.1244117
165 53.3392963 15.3996989
166 13.7275517 53.3392963
167 26.9853044 13.7275517
168 11.4598315 26.9853044
169 10.7624172 11.4598315
170 14.8976658 10.7624172
171 -27.8055972 14.8976658
172 -26.4011140 -27.8055972
173 -22.1441842 -26.4011140
174 -21.9658268 -22.1441842
175 -21.7760545 -21.9658268
176 -22.7406227 -21.7760545
177 -14.2607259 -22.7406227
178 -17.7427798 -14.2607259
179 -7.9477932 -17.7427798
180 -9.4864564 -7.9477932
181 -9.8419536 -9.4864564
182 -23.1934013 -9.8419536
183 -4.8773660 -23.1934013
184 -4.9908790 -4.8773660
185 -17.2019959 -4.9908790
186 -53.5081870 -17.2019959
187 -65.2923811 -53.5081870
188 -14.5727302 -65.2923811
189 11.8051607 -14.5727302
190 12.0917788 11.8051607
191 31.5066015 12.0917788
192 17.6097187 31.5066015
193 21.1117409 17.6097187
194 45.7549912 21.1117409
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7fvsp1386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/855871386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9hsmw1386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10qov11386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/117pch1386167226.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12cpuf1386167226.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/137pdb1386167226.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14x20v1386167226.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15zwki1386167226.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16agig1386167226.tab")
+ }
>
> try(system("convert tmp/18rfg1386167226.ps tmp/18rfg1386167226.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tcf11386167226.ps tmp/2tcf11386167226.png",intern=TRUE))
character(0)
> try(system("convert tmp/3y6gy1386167226.ps tmp/3y6gy1386167226.png",intern=TRUE))
character(0)
> try(system("convert tmp/43phb1386167226.ps tmp/43phb1386167226.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ay3s1386167226.ps tmp/5ay3s1386167226.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ng3z1386167226.ps tmp/6ng3z1386167226.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fvsp1386167226.ps tmp/7fvsp1386167226.png",intern=TRUE))
character(0)
> try(system("convert tmp/855871386167226.ps tmp/855871386167226.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hsmw1386167226.ps tmp/9hsmw1386167226.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qov11386167226.ps tmp/10qov11386167226.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.519 2.999 18.621