mfoceiControl: Control options for the mfocei estimation method

In nlmixr2est: Nonlinear Mixed Effects Models in Population PK/PD, Estimation Routines

Control options for the mfocei estimation method

Description

Mu-referenced-FOCEI-family closed-form-regression ('"lin"') variant of FOCEI; see 'foceiControl(muModel=)'.

Usage

mfoceiControl(sigdig = 3, ..., muModel = c("lin", "irls", "none"))

Arguments

sigdig	Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 3 this is atol = 1e-6, rtol = 1e-3.
...	Parameters used in the default 'foceiControl()'
muModel	Selects the regression variant; for 'mfoceiControl()' this is always '"lin"' and cannot be changed – use 'ifoceiControl()' for the IRLS variant.

Value

mfoceiControl object

Difference from 'focei'

The 'mfocei'/'ifocei' (and related) methods apply the mu2+ covariate hooks, which expand algebraic mu-referenced covariate expressions (e.g. 'cl.wt*log(WT/70)') into estimable mu-referenced parameters and split covariates into non-time-varying (absorbed into the phi term) and time-varying (kept as 'beta' regressors). Calling 'focei' directly does NOT apply these hooks, so these methods can estimate more mu-referenced models than plain 'focei' – there is a genuine difference between calling e.g. 'est="mfocei"' and 'est="focei"'.

All mu-referenced population thetas – with or without covariates – are profiled out of the outer optimizer by the in-C++ regression (intercept-only for covariate-free pairs), so outer gradients are only calculated for the non-mu-referenced parameters (residual errors, omegas, non-mu thetas). Bounded mu-referenced parameters are regression-updated with the update clamped to the bounds (a clamp is reported once as a fit note); user-fixed ('fix()') mu thetas stay out of the regression.

Author(s)

Matthew L. Fidler

Examples

mfoceiControl()

nlmixr2est documentation built on Aug. 5, 2026, 1:11 a.m.