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Nonlinear Mixed-Effects Modeling

Maximum likelihood estimation of population parameters

Functions

groupedData Create groupedData object
sbiofitmixed Fit nonlinear mixed-effects model (requires Statistics and Machine Learning Toolbox software)
sbionlmefit Estimate nonlinear mixed effects using SimBiology models (requires Statistics and Machine Learning Toolbox software)
sbionlmefitsa Estimate nonlinear mixed effects with stochastic EM algorithm (requires Statistics and Machine Learning Toolbox software)
sbiosampleparameters Generate parameters by sampling covariate model (requires Statistics and Machine Learning Toolbox software)
sbiosampleerror Sample error based on error model and add noise to simulation data
sbiofitstatusplot Plot status of sbionlmefit or sbionlmefitsa

Classes

CovariateModel object Define relationship between parameters and covariates
GroupedData object Table-like collection of data and metadata
EstimatedInfo object Object containing information about estimated model quantities
NLMEResults object Results object containing estimation results from nonlinear mixed-effects modeling

Examples and How To

Nonlinear Mixed-Effects Modeling Workflow

SimBiology lets you estimate population parameters (fixed effects) while considering individual variations (random effects).

Concepts

Nonlinear Mixed-Effects Modeling

SimBiology lets you estimate population parameters (fixed effects) while considering individual variations (random effects).

Maximum Likelihood Estimation

SimBiology estimates the parameters of a nonlinear mixed-effects model by maximizing a likelihood function.

Supported Methods for Parameter Estimation

SimBiology® supports a variety of optimization methods for least-squares and mixed-effects estimation problems.

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