ranef: Random-effect summaries

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

Random-effect summaries

Description

Random-effect summaries

Usage

ranef(object, ...)

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

Arguments

object	A tulpa_fit object.
...	Ignored.

Details

What each backend reports for a group effect follows what it computes:

• Sampler tier and the RE-covariance Gibbs debias (tulpa_re_cov_gibbs(), reached by tulpa(..., control = list(re_cov = "gibbs"))) draw the random effects jointly with everything else, so estimate / sd / bounds are the empirical posterior summaries.

• The RE-covariance integrator (tulpa_re_cov_nested()) carries a Gaussian per-group posterior at each Sigma node; the reported summaries are the exact moments and quantiles of the weighted mixture of those, so they carry both the within-node curvature and the Sigma uncertainty. A group effect the subspace debias selected (control$subspace_debias) is moved by the Metropolis sampler at every node instead, and is reported from those draws.

• The Laplace tier reports the conditional mode with no spread (sd and the bounds are NA), which is the only per-group quantity it forms.

The source column says per row which of these produced it: "sampled" for a posterior draw summary, "mixture" for the node mixture, "mode" for a conditional mode. A fit whose backend never forms a per-group posterior at all (the adaptive Gauss-Hermite inner marginal integrates each group out by quadrature) errors with that reason rather than returning an empty table, which would be indistinguishable from a model with no random effects. A model that genuinely has none returns a zero-row data frame.

Value

Data frame with one row per random-effect coefficient: term (the group level, and the coefficient for a random slope), estimate, sd, the 2.5% / 97.5% bounds conf.low / conf.high, and source (which construction the row came from). sd and the bounds are NA on a backend that reports a point per group (see Details).

See Also

field_effects() for a spatial field, smooth_effects() for an s(x) term.

Examples

set.seed(1)
df <- data.frame(x = rnorm(100), g = factor(rep(1:10, 10)))
df$y <- rpois(100, exp(0.3 * df$x))
fit <- tulpa(y ~ x + (1 | g), data = df, family = "poisson")
ranef(fit)

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