Articles • gllvmTMB

Function map and cheat sheet: Find the documented function for preparing, fitting, checking, interpreting, or reporting a stacked-trait GLLVM.
Individual morphometrics: the simplest GLLVM: Use when continuous traits are measured once per individual; fit a Gaussian latent covariance and compare it with known simulation truth.
Behavioural syndromes in repeated measurements: Use repeated Gaussian behaviours to separate stable between-individual covariance from within-occasion covariance and point repeatability.
Behavioural reaction norms with random slopes: Use repeated Gaussian behaviours across an observed environment to estimate individual intercept-slope covariance and trait-specific reaction norms.
How many latent dimensions should I fit?: Compare predeclared Gaussian latent ranks with ML information criteria and fit-health checks, then refit the selected covariance model deliberately.
Joint species distribution models for binary occurrence data: Fit a binary latent-factor JSDM in long or wide form and interpret residual species co-occurrence on a declared liability scale.
Phylogenetic covariance among traits: Use replicated Gaussian species traits and a supplied tree to estimate phylogenetically structured covariance with explicit label and health checks.
Handling missing data: Choose among omitted, retained, or explicitly modelled missing values and verify how each choice changes the estimand and fitted rows.

gllvmTMB vocabulary: Look up the model terms, covariance pieces, grouping tiers, identifiability concepts, and uncertainty language used across the guides.
Covariance and correlation: the model behind Sigma: Use as a technical reference for Sigma, Lambda, Psi, liability-scale residual conventions, and the limits of covariance comparisons.
Cross-family trait correlations, including nominal responses: Fit the partially-validated ordinary-unit cross-family route, recover latent correlations across five response families, and compute the reference-invariant nominal summary on its admitted non-ordinal partner set.
Formula keyword grid: Choose a correlation source and covariance mode, then translate the intended model into matching long and traits(…) wide formulas.
Response-specific predictor effects: Specify which predictors affect which responses, including intentional zero coefficients, without confusing fixed-effect structure with covariance constraints.
Choose a response family: Match the response support and sampling process to a family before adding multivariate covariance, and stop at the documented specialist boundaries.
Unordered categories with multinomial(): Model an unordered categorical response as baseline-category log-odds, read its fixed-effect contrasts and probabilities, and distinguish the covered core from two partial covariance routes.

Screen binary traits before fitting: Check prevalence, sparse outcomes, duplicated cells, and usable denominators before fitting a binary multivariate model.
Can I trust this fit?: Inspect optimiser, gradient, Hessian, residual, and fitted-model predictive signals before interpreting covariance or uncertainty.
Convergence and hard fits: Diagnose a numerically difficult fit and compare start or optimiser strategies without turning a small criterion into a validity certificate.
Profile likelihood: routes and fallbacks: Choose and inspect target-specific profile, Wald, or bootstrap routes while keeping returned bounds separate from coverage claims.
Common pitfalls and how to avoid them: Troubleshoot six common data, formula, covariance, and interpretation mistakes with concrete long-format checks and examples.