Posterior point estimates of the deviance residual at each observed row, from
the Student-t log-weight likelihood, matching augment()'s residual column.
Usage
# S3 method for class 'kb_fit_weight'
residuals(object, ...)See also
fitted() for fitted values, and augment().
Other generics:
augment.kb_fit(),
coef.kb_fit(),
converged.kb_fit(),
fitted.kb_fit_weight(),
glance.kb_fit(),
kb_stancode(),
log_lik.kb_fit(),
posterior_epred.kb_fit_weight(),
posterior_linpred.kb_fit_weight(),
posterior_predict.kb_fit_weight(),
predict.kb_fit_weight(),
prior_summary.kb_fit(),
samples(),
summary.kb_fit(),
tidy.kb_fit_weight()
Examples
residuals(fit_weight_sim_nereo)
#> [1] -0.241872016 0.378468708 -0.053961040 1.110174664 -0.212433497
#> [6] 1.193768936 0.199290846 0.582267761 -2.527594201 -1.016118242
#> [11] -1.843456590 0.204411549 -1.790041397 0.694306845 -1.285383223
#> [16] 0.245072285 1.110719577 0.766485380 -1.336669911 0.295624372
#> [21] -0.409223226 -0.849536100 1.023095038 1.182671143 0.159868539
#> [26] 1.912864932 1.380385786 -0.445537039 -0.827131848 -1.812372166
#> [31] -0.695867690 -0.319060774 0.663160934 -0.306236391 0.049315588
#> [36] 1.474737581 0.239952350 0.688914716 -1.980827395 -0.288061983
#> [41] 2.211347139 -0.220151102 -0.121602787 1.720487118 -0.173367778
#> [46] -0.436893255 -1.800950287 -0.123768257 0.402209892 -0.130550309
#> [51] -1.892259958 0.441629272 -1.963935559 2.054910519 1.331997777
#> [56] 0.220606582 1.133594570 0.019719137 0.181318548 0.055389746
#> [61] 2.371458210 -0.420653222 0.657211642 0.110384505 -0.489410742
#> [66] -1.593492477 -0.252258900 -1.279715269 0.118566882 0.467840921
#> [71] -0.256698937 0.619688999 0.628629409 -0.152418996 -0.983832125
#> [76] -0.915632719 0.017234181 -0.493099144 -0.401754982 2.305075422
#> [81] -1.006846023 0.450167792 0.967352541 -1.099779125 -0.824609051
#> [86] 1.592852877 -2.417242153 0.381877218 -0.324231933 1.274185371
#> [91] -0.184372578 0.782041144 -0.637370496 0.013755466 1.877302789
#> [96] 0.025372364 -0.434512283 0.684272831 -0.551540607 1.472048660
#> [101] -0.133286662 0.977105235 0.550323507 -0.924129347 -0.142806102
#> [106] -0.386405610 1.029526486 -0.321446190 -2.142434526 1.008288927
#> [111] -1.825124096 1.827828180 0.037856301 -0.751846980 -0.913416659
#> [116] 0.707926765 0.854785340 -0.821131395 0.398682335 -1.390422838
#> [121] 0.373681855 -1.495852083 -0.249836840 -0.268698000 1.382285613
#> [126] -0.405588286 -1.140606112 0.053499396 1.731587346 0.651439444
#> [131] 0.320194610 -0.907916751 0.668877656 0.908250852 -2.597331920
#> [136] -0.876122929 2.072819660 -0.406443194 0.005729624 0.506393040
#> [141] -1.360098740 0.744840131 0.449599159 -0.100285212 -1.020774329
#> [146] -0.006802240 -1.154461061 -0.676672171 1.217112230 1.636706938
#> [151] -1.271749504 0.473698309 -0.541554628 -1.764897824 0.969337319
#> [156] 1.306201018 1.378560611 2.445252117 2.319449818 -1.078018299
#> [161] -2.597629841 -0.852012127 -0.706903822 0.903352535 0.502215220
#> [166] -0.682022444 0.080389845 0.575822516 1.773328252 -0.347287694
#> [171] 1.003479122 1.511676179 -0.659505124 -0.600336183 0.011147662
#> [176] -1.151996989 -1.699416301 -0.465912500 1.589002118 -1.034266707
#> [181] -0.543482346 -1.655101529 -0.272902396 -0.299644741 -0.378695206
#> [186] 1.502194547 -0.920482803 1.382730760 0.984248554 0.809036564
#> [191] 1.376209494 -0.876160398 0.517728810 -0.350657231 1.004341595
#> [196] 1.645960362 -0.468219791 -1.338251063 0.938656989 -1.918223248
#> [201] 0.360329068 0.093849546 -0.615099436 -0.403030806 0.173828157
#> [206] 0.112801213 -2.312432408 0.890602568 1.710223343 -1.410550079
#> [211] 2.043026548 0.668837930 -1.541149643 -0.074683436 -1.178358749
#> [216] 0.068448064 -1.005954505 0.353805976 -0.661900488 0.836190189
#> [221] -1.639347085 -1.120389274 0.206028856 -0.601606943 1.251302888
#> [226] 0.081821053 -0.223588383 -0.012913485 -0.566110479 -0.589070182
#> [231] 0.991299805 1.430676807 -1.112590371 0.714118340