Diagnose GLMM, LMM, and GLM models — diagnose_gazepoint

Run a compact diagnostics bundle for model objects used in gp3tools workflows. The function combines convergence, singularity, overdispersion, and optional DHARMa simulation-based residual diagnostics.

Usage

diagnose_gazepoint_glmm(
  model,
  model_name = NULL,
  check_convergence = TRUE,
  check_singularity = TRUE,
  check_overdispersion = TRUE,
  use_dharma = TRUE,
  dharma_simulations = 250,
  seed = 123
)

Arguments

model: A fitted model object, a gp3tools fit object containing $model, or a named list of fitted model objects.
model_name: Optional model label used in returned tables.
check_convergence: Logical. If TRUE, run convergence diagnostics.
check_singularity: Logical. If TRUE, run singularity diagnostics.
check_overdispersion: Logical. If TRUE, run overdispersion diagnostics.
use_dharma: Logical. If TRUE, try to run optional DHARMa diagnostics.
dharma_simulations: Number of DHARMa simulations.
seed: Random seed used before DHARMa simulation.

Value

A list with overview, convergence, singularity, overdispersion, DHARMa diagnostics, and settings.

Details

The function accepts raw fitted models, gp3tools fit objects containing a $model element, or a named list of fitted models. DHARMa diagnostics are optional and are skipped cleanly when DHARMa is not installed.