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Nonlinear Regression

Least-squares estimation to fit grouped or pooled data, single or multiple experiments

Functions

groupedData Create groupedData object
sbiofit Perform nonlinear least-squares regression
sbionlinfit Perform nonlinear least-squares regression using SimBiology models (requires Statistics and Machine Learning Toolbox software)
sbioparamestim Perform parameter estimation
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

Classes

GroupedData object Table-like collection of data and metadata
EstimatedInfo object Object containing information about estimated model quantities
LeastSquaresResults object Results object containing estimation results from least-squares regression
OptimResults object Estimation results object, subclass of LeastSquaresResults
NLINResults object Estimation results object, subclass of LeastSquaresResults

Examples and How To

Desktop Workflow

Estimate Pharmacokinetic Parameters Using SimBiology Desktop

This example shows how to estimate one-compartment pharmacokinetic (PK) parameters given experimental drug concentration data.

Programmatic Workflow

Fit a One-Compartment Model to an Individual's PK Profile

This example shows how to fit an individual's PK profile data to one-compartment model and estimate pharmacokinetic parameters.

Fit a Two-Compartment Model to PK Profiles of Multiple Individuals

This example shows how to estimate pharmacokinetic parameters of multiple individuals using a two-compartment model.

Estimate Category-Specific PK Parameters for Multiple Individuals

This example shows how to estimate category-specific (such as young versus old, male versus female), individual-specific, and population-wide parameters using PK profile data from multiple individuals.

Concepts

Nonlinear Regression

The purpose of regression models is to describe a response variable as a function of independent variables.

Maximum Likelihood Estimation

SimBiology® estimates parameters by the method of maximum likelihood.

Supported Methods for Parameter Estimation

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

Progress Plot

The progress plot provides the live feedback on the status of parameter estimation while using sbiofit or the Fit Data task in the SimBiology desktop.

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