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Confidence intervals for fixed effects of a mixeff GLMM

Source: R/inference.R
confint.mm_glmm.Rd

Two routes are available. method = "asymptotic" builds Wald intervals (estimate +/- z * SE) from the Rust fixed-effect inference table; it requires a certified fit (method = "joint_laplace") or the explicit working-Hessian opt-in, and refuses the profiled (pirls_profiled) estimator with a typed reason. method = "bootstrap" runs a parametric bootstrap — each replicate simulates a response from the fitted model under fresh random-effect draws and refits the same (effective) estimator — and returns percentile intervals; it works on profiled-estimator fits for every supported family, including negative binomial (fixed-theta fits condition replicates on that theta; estimated-theta fits re-estimate theta per replicate, propagating its uncertainty). On joint_laplace fits the bootstrap is refused at this engine pin — replicate refits cannot re-run the joint estimator — and those fits have certified Wald intervals instead. The bootstrap result carries replicate accounting as attributes: mm_bootstrap (requested, successful, failed, seed, per-parameter standard errors, Monte Carlo SE, and a reliability grade) and mm_estimator. Failed replicates are counted, never silently dropped. Profile intervals are not certified for GLMMs and are refused.

Usage

# S3 method for class 'mm_glmm'
confint(
  object,
  parm,
  level = 0.95,
  method = c("asymptotic", "wald", "profile", "bootstrap"),
  ...
)

Arguments

object

A fitted mm_glmm.

parm

Optional fixed-effect names or indices; defaults to all.

level

Confidence level.

method

"asymptotic" (the default; the package-wide name for the closed-form Wald interval), its synonyms "wald" and lme4's "Wald", or "bootstrap" (lme4's spelling "boot" is accepted). lme4 defaults to "profile"; mixeff keeps the Wald default because profile intervals are refused for GLMMs. The same spellings are accepted by the LMM method (confint.mm_lmm()), whose "profile" intervals use lme4's .sig01, ..., .sigma rows.

...

For method = "bootstrap": nsim (number of replicates, default 999), seed (non-negative integer; when omitted one is drawn from R's RNG so set.seed() governs reproducibility), and threads (worker threads for the replicate refits, default 1; the result is identical for every value – see bootstrap_control() for the threading contract and CRAN's two-thread default policy).

Value

An mm_confint matrix of lower/upper bounds ("asymptotic"), or a plain matrix of percentile bounds with bootstrap-accounting attributes ("bootstrap").