MMAMixedEffects

Guides

MMAMixedEffects

Symbols

GeneralizedMixedEffectsModelFit
MixedEffectsFamily
MixedEffectsModelFit
MixedEffectsModel
NestedGrouping
NonlinearMixedEffectsModelFit

MMA MixedEffects

Native Gaussian, generalized, and nonlinear mixed-effects modeling with ordinary Wolfram expressions, structured random and residual effects, small-sample inference, progress reporting, and property-driven fitted-model objects.

Fit models

MixedEffectsModelFit

— fit Gaussian ML or REML models with crossed or nested random effects, residual correlation, and heteroscedasticity.

GeneralizedMixedEffectsModelFit

— fit GLMMs using Laplace or adaptive Gauss-Hermite quadrature and a broad response-family library.

NonlinearMixedEffectsModelFit

— fit nonlinear mixed models from symbolically differentiated Wolfram expressions.

Model structure

NestedGrouping

— construct explicit composite grouping levels for multilevel designs.

MixedEffectsFamily

— construct validated core, negative-binomial, beta, lognormal, Student-t, zero-inflated, and hurdle families.

Fitted results

MixedEffectsModel

— extract estimates, covariance components, predictions, residuals, likelihood criteria, inference, and convergence diagnostics.

Workflow

Specify fixed bases as ordinary expressions; add one or more random structures; fit; inspect convergence and singularity separately; select population, conditional, or marginal prediction; then validate residual and covariance assumptions.

Long fits can expose ProgressReporting, EvaluationMonitor, and StepMonitor without starting parallel kernels. R and Python are optional external validation tools, never runtime dependencies.

Metadata

New in:

1.0

| Modified in:

1.2.4

| Obsolete in:

Categorization

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Keywords

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