Posterior-Predictive Weight Draws

Draws from the posterior predictive distribution: the expected weight plus Student-t observation noise (scale sWeight, 4 degrees of freedom, matching the Stan likelihood). With new_data = NULL the stored yrep at the observed data is returned, for use with bayesplot::pp_check().

Usage

# S3 method for class 'kb_fit_weight'
posterior_predict(
  object,
  new_data = NULL,
  ...,
  new_levels = "sample",
  representative_site = NULL
)

Arguments

object

A kb_fit_weight object.

new_data

A data frame with a diameter column (and optional site / year columns), or NULL for the stored yrep at the observed data.

...

Unused.

new_levels

A string, one of "sample" or "average", controlling how random effects that are not conditioned on are treated (factors absent from the prediction, and any new level not seen in the fit). "sample" draws a new random effect from Normal(0, sd), widening the interval to include between-group variation; "average" holds the random effects at zero, giving the typical group. Known levels are always conditioned on. "sample" draws fresh randomness on each call, so set a seed with set.seed() for a reproducible interval.

representative_site

A character vector of site levels present in the fit, or NULL (the default). When supplied, a new or absent site takes its site main effects (intercept and slope) from the named reference site (the per-draw average when several are named), instead of the new_levels treatment; the site:year interaction still follows new_levels.

Value

A draws-by-observations (D x N) matrix.

Details

For supplied new_data, conditioning is inferred from the grouping columns present (see posterior_epred()).

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(), predict.kb_fit_weight(), prior_summary.kb_fit(), residuals.kb_fit_weight(), samples(), summary.kb_fit(), tidy.kb_fit_weight()

Examples

pp <- posterior_predict(fit_weight_sim_nereo)
dim(pp)
#> [1] 800 234