| mfocepControl | R Documentation |
Mu-referenced-FOCEI-family closed-form-regression ('"lin"') variant of FOCE+ (no interaction, live conditional residual variance R); it is 'mfoce' with 'foce = "foce+"' forced (see 'foceiControl(foce=)').
mfocepControl(
sigdig = 3,
...,
interaction = FALSE,
muModel = c("lin", "irls", "none"),
foce = "foce+"
)
sigdig |
Optimization significant digits. One value drives, with a single
consistent formula, the inner/outer optimizer convergence tolerance
( |
... |
Parameters used in the default 'foceiControl()' |
interaction |
Interaction term for the model, in this case the default is 'FALSE'; it cannot be changed, use 'mfocei' instead |
muModel |
Selects the regression variant; for 'mfocepControl()' this is always '"lin"' and cannot be changed – use 'ifocepControl()' for the IRLS variant. |
foce |
FOCE residual-variance mode; for 'mfocepControl()' this is always '"foce+"' and cannot be changed – use 'mfoceControl()' for '"nonmem"' |
mfocepControl object
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.
Matthew L. Fidler
mfocepControl()
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