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sbionlinfit - Nonlinear least-squares regression using SimBiology models

Syntax

results = sbionlinfit(modelObj, modelMapObject, pkDataObj, Beta0) results = sbionlinfit(modelObj, modelMapObject, pkDataObj, Beta0, ...) [results, SimDataI] = sbionlinfit(...)

Arguments

Input Arguments

`modelObject`	SimBiology model object used to fit observed data.
`modelMapObject`	`PKModelMap` object that defines the roles of the model components in the estimation. For more information, see `PKModelMap object`.
`pkDataObj`	`PKData` object that defines the data to use in fitting, and the roles of the data columns used for estimation. For more information, see `PKData object`.
`Beta0`	A vector of initial estimates for the fixed effects. The length of `Beta0` must equal at least the length of `modelMapObject.Estimated`. The final estimates for the fixed effects defined in `Beta0` are log transformed in the results. For more information on specifying initial estimates, see Setting Initial Estimates in the SimBiology User's Guide.

Output Arguments

results

A structure with the following fields:

beta — Fitted coefficients
R — Residuals
J — Jacobian of the model
COVB — Estimated covariance matrix for the fitted coefficients
mse — Estimate of the error of the variance term

SimDataI SimDataI contains simulation results for individuals.

Description

Note This function requires nlinfit in Statistics Toolbox™ (Version 7.0 or later).

results = sbionlinfit(modelObj, modelMapObject, pkDataObj, Beta0) performs least-squares regression using the SimBiology model, modelObj and returns estimated results in the results structure.

results = sbionlinfit(modelObj, modelMapObject, pkDataObj, Beta0, ...) lets you supply additional arguments accepted by nlinfit. See nlinfit in the Statistics Toolbox User's Guide for information on the accepted properties.

[results, SimDataI] = sbionlinfit(...) returns simulations of the SimBiology model, modelObj, using the estimated values of the parameters.