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 nonlinear mixed-effects estimation |
CovariateModel object | Define relationship between parameters and covariates |
groupedData | Table-like collection of data and metadata |
EstimatedInfo object | Object containing information about estimated model quantities |
Observable | Object containing expression for post-simulation calculations |
NLMEResults object | Results object containing estimation results from nonlinear mixed-effects modeling |
| SimBiology Model Builder | Build QSP, PK/PD, and mechanistic systems biology models interactively |
| SimBiology Model Analyzer | Analyze QSP, PK/PD, and mechanistic systems biology models |
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
A mixed-effects model is a statistical model that incorporates both fixed effects and random effects.
Supported Methods for Parameter Estimation in SimBiology
SimBiology® supports a variety of optimization methods for least-squares and mixed-effects estimation problems.
SimBiology supports the error models described in the following table.
Perform Data Fitting with PK/PD Models
SimBiology lets you estimate model parameters by fitting the model to experimental time-course data, using either nonlinear regression or mixed-effects (NLME) techniques.