Random-effect summaries

Usage

ranef(object, ...)

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

Arguments

object: A tulpa_fit object.
...: Ignored.

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).

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.

Examples

# \donttest{
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)
# }