logLik.tulpa_fit: The fit's log-scale goodness quantity

In tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

The fit's log-scale goodness quantity

Description

What this returns depends on what the fit computed, and the returned object names it in a quantity attribute:

Usage

## S3 method for class 'tulpa_fit'
logLik(object, ...)

Arguments

object	A tulpa_fit object.
...	Ignored.

Details

"log_posterior_mean"

a sampler fit: the mean over the draws of the log joint posterior density, prior included, in that backend's own parameterization: the unconstrained coordinates with their Jacobians for the ModelData samplers (hmc, ess, sghmc, sgld, mclmc, smc, vi), and the natural scale each quantity is sampled on for the RE-covariance and Polya-Gamma Gibbs samplers. Values are therefore comparable across fits of the same backend only.

"log_evidence"

a deterministic fit that estimated no hyperparameter from the data: the log marginal probability of the data under the model as specified. A hyperparameter the fit integrated counts, and so does one the caller supplied (a phi, a sigma_re, an outer grid laid at one value), which is part of the model rather than an estimate. A Laplace fit of a model with nothing to integrate reports its log marginal likelihood here, which already is that quantity.

"log_marginal_likelihood"

a deterministic fit that estimated some hyperparameters by maximising over the same data (empirical Bayes, or estimate_phi = TRUE): the log marginal likelihood at those estimates. The conditioned_on attribute names them.

"log_likelihood"

a fit that maximised over every parameter it reports, with the random effects integrated out (agq_fit()): the maximised log-likelihood, with df the number of maximised parameters.

On a nested-Laplace fit the value is the log evidence of its outer grid, ⁠log sum_k exp(log_marginal_k + log_cell_k)⁠, where log_marginal already carries each axis's hyperprior density on its integration coordinate and log_cell_k is the cell's absolute volume there. Every default outer axis carries a proper prior (a PC prior on each scale and range, a uniform on each bounded axis), so the value is the evidence under that prior and does not move with where the nodes were laid or how many there are, provided the grid covers the posterior. A fit carrying an axis with no proper prior (a flat random-effect covariance prior, a dispersion whose family has no PC prior yet, an axis on a block that does not carry its coordinates) reports NA with declined = "improper_hyperprior" and names the axes in a declined_axes attribute. A central-composite (CCD) design reproduces moments and carries no cell volume, so a fit integrated on one reports NA with a declined attribute.

A sampler fit whose producer kept no per-draw log posterior reports NA with quantity = "log_posterior_mean" and declined = "no_log_posterior_recorded". The Polya-Gamma Gibbs routes that recentre a proper field every sweep (the NNGP and multiscale-GP fields, and the negative-binomial kernel's iid random-effect block) leave no written density invariant and record none; a fit carrying neither draws nor a log marginal declines with "no_goodness_quantity_recorded". A value of NA always carries a declined attribute.

Values with a different quantity or conditioned_on are not on one scale, and compare_models(criterion = "loglik") refuses such a set. Only a "log_likelihood" is what AIC and BIC penalise, so AIC.tulpa_fit() and BIC.tulpa_fit() refuse every other quantity; compare those fits by their evidence, or by compare_models(criterion = "waic") / "loo", which score the pointwise predictive density.

Value

A logLik object with quantity and conditioned_on attributes, and a declined attribute naming the reason when no value could be read.

tulpa documentation built on Oct. 19, 2026, 5:09 p.m.