The raw posterior draws from a fitted model object.
See also
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(),
residuals.kb_fit_weight(),
summary.kb_fit(),
tidy.kb_fit_weight()
Examples
samples(fit_weight_sim_nereo)
#> # A draws_rvars: 400 iterations, 2 chains, and 10 variables
#> $bWeight: rvar<400,2>[1] mean ± sd:
#> [1] -1.5 ± 0.1
#>
#> $bDiameter: rvar<400,2>[1] mean ± sd:
#> [1] 2.5 ± 0.12
#>
#> $bDiameter2: rvar<400,2>[1] mean ± sd:
#> [1] 0.079 ± 0.12
#>
#> $sSite: rvar<400,2>[1] mean ± sd:
#> [1] 0.28 ± 0.079
#>
#> $sSiteDiameter: rvar<400,2>[1] mean ± sd:
#> [1] 0.33 ± 0.11
#>
#> $sSiteYear: rvar<400,2>[1] mean ± sd:
#> [1] 0.098 ± 0.025
#>
#> $sWeight: rvar<400,2>[1] mean ± sd:
#> [1] 0.16 ± 0.011
#>
#> $bSite: rvar<400,2>[10] mean ± sd:
#> [1] -0.257 ± 0.12 0.054 ± 0.11 -0.157 ± 0.12 -0.052 ± 0.12 0.108 ± 0.12
#> [6] 0.434 ± 0.12 0.054 ± 0.12 -0.179 ± 0.12 0.316 ± 0.12 -0.350 ± 0.12
#>
#> # ... with 2 more variables
