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nlmixr

vpc_nlmixr_nlme

Visual predictive check (VPC) for nlmixr nlme objects

Description

Do visual predictive check (VPC) plots for nlme-based non-linear mixed effect models

Usage

vpc_nlmixr_nlme(fit, nsim = 100, condition = NULL, ...)

vpcNlmixrNlme(fit, nsim = 100, condition = NULL, ...)

## S3 method for class 'nlmixrNlme'
vpc(sim, ...)

Arguments

fit

nlme fit object
nsim

number of simulations
condition

conditional variable
...

Additional arguments
sim

this is usually a data.frame with observed data, containing the independent and dependent variable, a column indicating the individual, and possibly covariates. E.g. load in from NONMEM using read_table_nm. However it can also be an object like a nlmixr or xpose object

Value

Called for its side effects of creating a VPC

Examples

specs <- list(fixed=lKA+lCL+lV~1, random = pdDiag(lKA+lCL~1), start=c(lKA=0.5, lCL=-3.2, lV=-1))
fit <- nlme_lin_cmpt(theo_md, par_model=specs, ncmt=1, verbose=TRUE)
vpc_nlmixr_nlme(fit, nsim = 100, condition = NULL)
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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