Extract Residuals for APLMS fits
Usage
# S3 method for class 'aplms'
residuals(object, ...)Value
Returns a dataframe with the following columns
- res
the residual,
- res_pearson
the Pearson residual, and
- res_quant
the normal quantile of the standarized resiudals.
Examples
data(temperature)
temperature.df = data.frame(temperature,time=1:length(temperature))
model<-aplms::aplms(temperature ~ 1,
npc=c("time"), basis=c("cr"),Knot=c(60),
data=temperature.df,family=Powerexp(k=0.3),p=1,
control = list(tol = 0.001,
algorithm1 = c("P-GAM"),
algorithm2 = c("BFGS"),
Maxiter1 = 20,
Maxiter2 = 25),
lam=c(10))
residuals(model)
#> res res_pearson res_quant
#> 1 -1.332990e-02 -0.2106088686 -0.2423967282
#> 2 2.251732e-02 0.3557676419 0.4028831103
#> 3 -5.458648e-04 -0.0086245185 -0.0100997278
#> 4 -1.645237e-03 -0.0259943070 -0.0304174434
#> 5 -3.949526e-03 -0.0624014529 -0.0728448346
#> 6 8.222696e-03 0.1299164052 0.1507671860
#> 7 1.115841e-02 0.1762999242 0.2036461563
#> 8 -9.819812e-02 -1.5515041851 -1.5571538389
#> 9 1.667855e-02 0.2635165928 0.3015390072
#> 10 1.065490e-01 1.6834464095 1.6713382587
#> 11 -1.126152e-01 -1.7792901370 -1.7530875602
#> 12 -1.425071e-03 -0.0225157404 -0.0263516999
#> 13 3.085878e-02 0.4875604764 0.5438777952
#> 14 2.490774e-04 0.0039353560 0.0046090805
#> 15 -1.838830e-02 -0.2905302966 -0.3314480149
#> 16 -9.118167e-03 -0.1440646046 -0.1669555046
#> 17 4.749275e-02 0.7503728625 0.8129509105
#> 18 3.251822e-02 0.5137791643 0.5714189367
#> 19 -1.121446e-01 -1.7718549383 -1.7467802710
#> 20 -3.067120e-02 -0.4845967292 -0.5407542298
#> 21 9.054447e-02 1.4305785564 1.4507130418
#> 22 3.184321e-02 0.5031141736 0.5602357408
#> 23 -2.723337e-02 -0.4302800049 -0.4831302980
#> 24 -1.406319e-02 -0.2221946230 -0.2554116575
#> 25 -4.053083e-02 -0.6403763127 -0.7021699268
#> 26 8.149764e-02 1.2876410633 1.3225063086
#> 27 6.870109e-02 1.0854588670 1.1362795645
#> 28 -7.904023e-02 -1.2488145348 -1.2872049030
#> 29 -4.309613e-02 -0.6809074084 -0.7432836536
#> 30 3.455701e-03 0.0545991567 0.0637735841
#> 31 6.547899e-02 1.0345505813 1.0884062703
#> 32 8.257058e-03 0.1304593171 0.1513893215
#> 33 -2.896948e-02 -0.4577100189 -0.5123206660
#> 34 -1.030498e-01 -1.6281597621 -1.6237311838
#> 35 4.016129e-02 0.6345377140 0.6962183783
#> 36 1.263199e-01 1.9958203267 1.9343630380
#> 37 -2.225520e-02 -0.3516262158 -0.3983812895
#> 38 -1.159463e-01 -1.8319200642 -1.7975720645
#> 39 3.429044e-02 0.5417798371 0.6006529005
#> 40 3.461496e-02 0.5469071841 0.6059863132
#> 41 -4.436110e-02 -0.7008935334 -0.7634294642
#> 42 6.058323e-03 0.0957198914 0.1114334597
#> 43 -4.623123e-03 -0.0730441071 -0.0851977728
#> 44 3.656999e-02 0.5777961207 0.6379890527
#> 45 -7.991129e-03 -0.1262577097 -0.1465726653
#> 46 -5.805357e-02 -0.9172309071 -0.9764443535
#> 47 3.626706e-02 0.5730099163 0.6330444938
#> 48 3.477701e-03 0.0549467533 0.0641779770
#> 49 3.710601e-02 0.5862651338 0.6467255995
#> 50 -1.283167e-01 -2.0273696721 -1.9604041651
#> 51 -4.977260e-03 -0.0786393785 -0.0916823072
#> 52 7.459578e-02 1.1785934412 1.2228140612
#> 53 1.356213e-02 0.2142780231 0.2465223704
#> 54 -1.091969e-01 -1.7252824713 -1.7071417310
#> 55 3.969655e-02 0.6271948433 0.6887229490
#> 56 -2.646778e-03 -0.0418183915 -0.0488881310
#> 57 -1.183964e-02 -0.1870631742 -0.2158363981
#> 58 4.104975e-02 0.6485750502 0.7105148403
#> 59 1.159450e-03 0.0183189975 0.0214442662
#> 60 -8.181799e-02 -1.2927024448 -1.3270927292
#> 61 4.699839e-03 0.0742562096 0.0866031046
#> 62 8.105808e-02 1.2806961457 1.3162073509
#> 63 -2.799650e-02 -0.4423372557 -0.4959842058
#> 64 -4.639442e-02 -0.7330195013 -0.7956393545
#> 65 6.759635e-02 1.0680042858 1.1199123208
#> 66 1.689117e-02 0.2668759564 0.3052689990
#> 67 -9.884265e-02 -1.5616875226 -1.5660373538
#> 68 -1.470029e-03 -0.0232260685 -0.0271820743
#> 69 2.638877e-02 0.4169355195 0.4688619429
#> 70 5.790549e-02 0.9148913647 0.9741875045
#> 71 -4.004416e-02 -0.6326870043 -0.6943303230
#> 72 -5.081851e-02 -0.8029189501 -0.8650066908
#> 73 -8.760549e-04 -0.0138414342 -0.0162059281
#> 74 1.256502e-01 1.9852404267 1.9256098838
#> 75 3.778318e-03 0.0596964218 0.0697012769
#> 76 -3.407752e-02 -0.5384157243 -0.5971502794
#> 77 -1.148884e-01 -1.8152054053 -1.7834747329
#> 78 5.747674e-02 0.9081172084 0.9676473023
#> 79 7.232830e-02 1.1427678558 1.1896837522
#> 80 -7.910830e-05 -0.0012498901 -0.0014639371
#> 81 -9.037206e-02 -1.4278544024 -1.4482944557
#> 82 -8.481319e-03 -0.1340025866 -0.1554478068
#> 83 2.633800e-02 0.4161334388 0.4680029138
#> 84 1.017475e-01 1.6075827820 1.6059254797
#> 85 -8.441691e-02 -1.3337646866 -1.3641724897
#> 86 -2.782959e-02 -0.4397001573 -0.4931759428
#> 87 3.313923e-02 0.5235910228 0.5816838383
#> 88 2.700861e-02 0.4267288380 0.4793376144
#> 89 -8.952567e-02 -1.4144817404 -1.4364081787
#> 90 7.331074e-03 0.1158290185 0.1345982976
#> 91 5.261555e-02 0.8313116463 0.8929121918
#> 92 -4.336780e-02 -0.6851996189 -0.7476171893
#> 93 -1.645256e-02 -0.2599459946 -0.2975711767
#> 94 1.156409e-01 1.8270951692 1.7935055771
#> 95 -5.530952e-02 -0.8738756387 -0.9344622408
#> 96 5.674665e-03 0.0896582026 0.1044317110
#> 97 -5.308846e-02 -0.8387835610 -0.9002305220
#> 98 1.351486e-01 2.1353111653 2.0488279313
#> 99 -1.317415e-02 -0.2081480146 -0.2396277033
#> 100 -3.846711e-02 -0.6077700167 -0.6688376567
#> 101 9.246678e-03 0.1460950488 0.1692745354
#> 102 5.107930e-02 0.8070393902 0.8690659692
#> 103 -1.228722e-01 -1.9413484479 -1.8891853022
#> 104 3.789734e-02 0.5987678935 0.6595939033
#> 105 7.383692e-03 0.1166603648 0.1355538514
#> 106 -1.901836e-02 -0.3004850145 -0.3424208206
#> 107 -8.344586e-03 -0.1318422304 -0.1529736979
#> 108 3.381668e-02 0.5342945194 0.5928558145
#> 109 2.216149e-02 0.3501456144 0.3967707649
#> 110 -1.474052e-01 -2.3289635558 -2.2049952725
#> 111 6.374729e-03 0.1007190244 0.1172013434
#> 112 8.795271e-02 1.3896294414 1.4142576030
#> 113 -2.288394e-02 -0.3615600824 -0.4091721779
#> 114 -3.265638e-02 -0.5159621731 -0.5737047012
#> 115 1.231645e-02 0.1945967016 0.2243504904
#> 116 7.736491e-02 1.2223450257 1.2630167118
#> 117 -9.482132e-02 -1.4981515192 -1.5104107609
#> 118 -4.240388e-02 -0.6699699732 -0.7322233974
#> 119 1.562526e-01 2.4687489530 2.3158853171
#> 120 9.799069e-04 0.0154822673 0.0181258379
#> 121 -6.560148e-02 -1.0364859500 -1.0902339018
#> 122 -1.096286e-04 -0.0017321026 -0.0020287167
#> 123 3.361161e-02 0.5310545601 0.5894768688
#> 124 1.722294e-03 0.0272117939 0.0318400125
#> 125 -8.760028e-02 -1.3840611500 -1.4092837375
#> 126 2.716571e-02 0.4292109987 0.4819889184
#> 127 9.885247e-03 0.1561842761 0.1807819766
#> 128 1.905088e-02 0.3009987880 0.3429864271
#> 129 -1.024495e-01 -1.6186741322 -1.6155289976
#> 130 -7.109573e-03 -0.1123293542 -0.1305738978
#> 131 9.329788e-02 1.4740816159 1.4892106200
#> 132 2.923375e-05 0.0004618854 0.0005409887
#> 133 -5.104271e-03 -0.0806461185 -0.0940062774
#> 134 -1.033322e-02 -0.1632621443 -0.1888390022
#> 135 -1.779602e-02 -0.2811723481 -0.3211090558
#> 136 2.961651e-02 0.4679329521 0.5231524231
#> 137 7.773787e-02 1.2282376587 1.2684101402
#> 138 -3.813831e-02 -0.6025751513 -0.6635055484
#> 139 -1.200778e-01 -1.8971965571 -1.8523613250
#> 140 2.223155e-02 0.3512525968 0.3979749389
#> 141 1.118609e-01 1.7673721198 1.7429747279
#> 142 -4.422282e-02 -0.6987087071 -0.7612312280