Posterior summary of the fixed effects
Usage
# S3 method for class 'tulpa_fit'
summary(object, level = 0.95, ...)Value
Data frame: estimate, std.error, and lower/upper credible bounds, one row per fixed effect. Sampler tiers report empirical quantiles; the Laplace tier reports the Gaussian approximation.
On a nested-Laplace fit the estimate and standard error are the
hyperparameter-grid-marginalized moments, and the bounds invert the Gaussian
mixture sum_k w_k N(mu_kj, V_kjj) that grid defines, rather than reading
mu +/- z sigma off the single Gaussian matching those moments. An
interval_source attribute records which read produced them
("mixture_cdf", "gaussian_moment", "skew_map_cell", or
"skew_map_cell/mixture_cdf" when the two are in play on different
coefficients) and interval_declined says why, whenever the mixture read
did not run. A
retained_mass attribute gives the share of the grid weight whose cells
retained a fixed-effect block: 1 on a complete grid, and below 1 on one
that dropped a positive-weight cell, whose report is then the posterior
conditional on the cells that remain.
Where no per-cell block was retained the estimate falls back to the
grid-weighted average of the per-cell modes, restricted to the cells whose
inner solve reached a mode. A fit where none did reports NA with
interval_declined = "not_converged", rather than the vector its Newton
started from as an estimate.
With control$skew_correct = TRUE a coefficient whose inner-Laplace
gamma_3 is in the band it is valid on reports Cornish-Fisher quantiles
instead, and a skew_applied attribute names which coefficients took the
correction. That correction is measured at the MAP cell, so it is reported
on its own and is not composed with the mixture read; a coefficient it
declines keeps the mixture read rather than falling back further.
An axis_fields_dropped attribute carries the grid axes the fit's own
resolved path could not read, one row per dropped field (block, type,
field, path, integrates, reason). It is NULL whenever every supplied axis
was used, which is the ordinary case; diagnostic_summary() reads the same
record in sentences.
A beta_prior attribute carries the Gaussian fixed-effect prior the fit
ran under, as list(mean, sd). It is the engine default,
prior_normal(0, 2.5), whenever the caller supplied none, and NULL on
the paths that express no Gaussian prior on the fixed effects.