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R
nlmixr

preconditionFit

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

Nonlinear Mixed Effects Models in Population PK/PD

v2.0.4
GPL (>= 2)
Authors
Matthew Fidler [aut] (<https://orcid.org/0000-0001-8538-6691>), Yuan Xiong [aut], Rik Schoemaker [aut] (<https://orcid.org/0000-0002-7538-3005>), Justin Wilkins [aut] (<https://orcid.org/0000-0002-7099-9396>), Wenping Wang [aut, cre], Robert Leary [ctb], Mason McComb [aut] (<https://orcid.org/0000-0001-9871-8616>), Mirjam Trame [ctb], Teun Post [ctb], Richard Hooijmaijers [aut], Hadley Wickham [ctb], Dirk Eddelbuettel [cph], Johannes Pfeifer [ctb], Robert B. Schnabel [ctb], Elizabeth Eskow [ctb], Emmanuelle Comets [ctb], Audrey Lavenu [ctb], Marc Lavielle [ctb], David Ardia [cph], Daniel C. Dillon [ctb], Katharine Mullen [cph], Ben Goodrich [ctb]
Initial release

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