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Perform Data Fitting with PKPD 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. You can perform both individual and population fits to grouped data.

  • Individual fit — Fit data using nonlinear regression (least-squares) methods, specify parameter transformations, estimate parameters, and calculate residuals and the estimated coefficient covariance matrix. For a command line workflow, see Fitting Workflow for sbiofit. For the SimBiology desktop, see Fit Data.

  • Population fit — Fit data, specify parameter transformations, and estimate the fixed effects and the random sources of variation on parameters using nonlinear mixed-effects models. For a command line workflow, see Nonlinear Mixed-Effects Modeling Workflow. For the desktop, see Fit Data.

  • Population fit using a stochastic algorithm — Fit data, specify parameter transformations, and estimate the fixed effects and the random sources of variation on parameters, using the Stochastic Approximation Expectation-Maximization (SAEM) algorithm. SAEM is more robust with respect to starting values. This functionality relaxes assumption of constant error variance. Specify nlmefitsa as the estimation function name when you run sbiofitmixed or in the Fit Data task of the desktop.

In addition, you can turn on the ProgressPlot option to get the live feedback on the status of parameter estimation.

See Also

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