preconditionFit: Linearly re-parameterize the model to be less sensitive to...

In nlmixr: Nonlinear Mixed Effects Models in Population PK/PD

Linearly re-parameterize the model to be less sensitive to rounding errors

Description

Linearly re-parameterize the model to be less sensitive to rounding errors

Usage

preconditionFit(fit, estType = c("full", "posthoc", "none"), ntry = 10L)

Arguments

fit	A nlmixr fit to be preconditioned
estType	Once the fit has been linearly reparametrized, should a "full" estimation, "posthoc" estimation or simply a estimation of the covariance matrix "none" before the fit is updated
ntry	number of tries before giving up on a pre-conditioned covariance estimate

Value

A nlmixr fit object that was preconditioned to stabilize the variance/covariance calculation

References

Aoki Y, Nordgren R, Hooker AC. Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations. AAPS J. 2016;18(2):505-518. doi:10.1208/s12248-016-9866-5

nlmixr documentation built on March 27, 2022, 5:05 p.m.