This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

Nonlinear Mixed-Effects Modeling

Maximum likelihood estimation of population parameters


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


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

Modeling the Population Pharmacokinetics of Phenobarbital in Neonates

This example shows how to build a simple nonlinear mixed-effects model from clinical pharmacokinetic data.


Nonlinear Mixed-Effects Modeling

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

Supported Methods for Parameter Estimation

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

Was this topic helpful?