Function reference • nlmixr

Interfacing nlmixr models
`nlmixr`	nlmixr fits population PK and PKPD non-linear mixed effects models.
Adding residuals & covariance to fit object
`addCwres()`	Add CWRES
`addTable()`	Add table information to nlmixr fit object without tables
`addNpde()`	NPDE calculation for nlmixr
`reexports`	Objects exported from other packages
Model diagnostics
`vpc()`	Vpc function for nlmixr
`bootplot(<nlmixrFitCore>)` `traceplot()`	Produce trace-plot for fit if applicable
Datasets
`Bolus_1CPTMM`	1 Compartment Model w/ Michaelis-Menten Elimination
`Bolus_1CPT`	Bolus_1CPT -- 1 Compartment Model Simulated Data from ACOP 2016
`Bolus_2CPTMM`	2 Compartment Model with Michaelis-Menten Clearance
`Bolus_2CPT`	2 Compartment Model
`Infusion_1CPT`	Infusion_1CPT -- 1 Compartment Model Simulated Data from ACOP 2016
`Oral_1CPT`	Oral_1CPT -- 1 Compartment Model with Oral Absorption Simulated Data from ACOP 2016
`Wang2007`	Simulated Data Set for comparing objective functions
`pump`	Pump failure example dataset
`rats`	Pregnant Rat Diet Experiment
`warfarin`	Warfarin PK/PD data
Options for fitting and output
`foceiControl()`	Control Options for FOCEi
`reexports`	Objects exported from other packages
`nmDocx()` `nmSave()`	Create a run summary word document
`saemControl()`	Control Options for SAEM
`tableControl()`	Output table/data.frame options
Low level estimation routines
`as.dynmodel()`	Convert fit to classic dynmodel object
`as.focei(<dynmodel>)`	Output nlmixr format for dynmodel
`dynmodel.mcmc()`	Fit a non-population dynamic model using mcmc
`dynmodelControl()`	Control Options for dynmodel
`dynmodel()`	Fit a non-population dynamic model
`foceiFit()` `focei.fit()`	FOCEi fit
`gnlmm()` `gnlmm2()`	Fit a generalized nonlinear mixed-effect model
`nlme_lin_cmpt()` `nlmeLinCmpt()` `nlmeLinCmt()`	Fit nlme-based linear compartment mixed-effect model using closed form solution
`nlme_ode()` `nlmeOde()`	Fit nlme-based mixed-effect model using ODE implementation
`saem.fit()` `saem()`	Fit an SAEM model
Unit tests and validation functions
`nlmixrValidate()` `nmTest()`	Validate nlmixr
Utilities
`bootplot()`	Produce trace-plot for fit if applicable
`bootstrapFit()`	Bootstrap nlmixr fit
`preCondInv()`	Calculate the inverse preconditioning matrix
`preconditionFit()`	Linearly re-parameterize the model to be less sensitive to rounding errors
Covariate Search Functions
`addCovMultiple()`	Add multiple covariates to a given model, sequentially or all at once
`addCovVar()`	Adding covariate to a given variable in an nlmixr model expression
`addCovariate()`	Add covariate expression to a function string
`backwardSearch()`	Backward covariate search
`covarSearchAuto()`	Stepwise Covariate Model-selection (SCM) method
`forwardSearch()`	Forward covariate search
`initializeCovars()`	Initializing covariates before estimation
`makeDummies()`	Create categorical covariates
`makeHockeyStick()`	Creating Hockey-stick covariates
`performNorm()`	Perform normalization of the covariate
`removeCovMultiple()`	Removing multiple covariates
`removeCovVar()`	Remove covariate from function string
`removeCovariate()`	Remove covariate expression from a function string
ggplot2 functions
`reexports`	Objects exported from other packages
Internal functions
`VarCorr(<nlmixrNlme>)`	Return VarCorr for nlmixr nlme
`as.focei()`	Convert fit to FOCEi style fit
`bootdata()`	Bootstrap data
`calc.2LL()`	Log-likelihood using Gaussian Quadrature
`calc.COV()`	Covariance matrix by Fisher Information Matrix via linearization
`calcCov()`	Calculate gnlmm variance-covariance matrix of fixed effects
`cholSE()`	Generalized Cholesky Matrix Decomposition
`configsaem()`	Configure an SAEM model
`focei.eta()`	Get the FOCEi theta or eta specification for model.
`focei.theta()`	Get the FOCEi theta specification for the model
`frwd_selection()`	Forward covariate selection for nlme-base non-linear mixed effect models
`gauss.quad()`	Sets nodes and weights of Gauss-Hermite quadrature
`gen_saem_user_fn()`	Generate an SAEM model
`getOMEGA()`	Calculate gnlmm variance-covariance matrix of random effects
`ini()`	nlmixr ini block handling
`instant.stan.extension()`	instant.stan.extension.
`invgaussian`	Inverse Guassian absorption model
`lin_cmt()`	concentrations from a linear compartment model
`metabolite`	Parent/Metabolite dataset
`model()`	nlmixr model block
`nlme_gof()`	GOF plots for nlme-based mixed-effect models
`nlmixrAugPred()` `augPred(<nlmixrFitData>)`	Augmented Prediction for nlmixr fit
`nlmixrBounds.eta.names()`	Get ETA names
`nlmixrBounds.focei.upper.lower()`	Get upper/lower/names for THETAs
`nlmixrBoundsParser`	Functions to assist with setting initial conditions and boundaries
`nlmixrBounds()`	Extract the nlmixr bound information from a function.
`nlmixrDynmodelConvert()`	Converting nlmixr objects to dynmodel objects
`nlmixrEst()`	Generic for nlmixr estimation methods
`nlmixrEval_()` `nlmixrUnscaled_()` `nlmixrGrad_()` `nlmixrParHist_()` `nlmixrGradFun()`	Create a gradient function based on gill numerical differences
`nlmixrGill83()`	Get the optimal forward difference interval by Gill83 method
`nlmixrHess()`	Calculate Hessian
`nlmixrLogo()`	Messages the nlmixr logo...
`nlmixrPred()` `predict(<nlmixrFitData>)`	Predict a nlmixr solved system
`nlmixrSim()` `rxSolve(<nlmixrFitData>)` `simulate(<nlmixrFitData>)` `solve(<nlmixrFitData>)`	Simulate a nlmixr solved system
`nlmixrTest()`	nlmixTest function for testing
`nlmixrUI.dynmodelfun2()`	Return dynmodel variable translation function
`nlmixrUI.dynmodelfun()`	Return dynmodel variable translation function
`nlmixrUI.focei.fixed()`	Get parameters that are fixed
`nlmixrUI.focei.inits()`	Get the FOCEi initializations
`nlmixrUI.nlme.specs()`	Create the nlme specs list for nlmixr nlme solving
`nlmixrUI.rxode.pred()`	Return RxODE model with predictions appended
`nlmixrUI.saem.ares()`	Get initial estimate for ares SAEM.
`nlmixrUI.saem.bres()`	Get initial estimate for bres SAEM.
`nlmixrUI.saem.cres()`	Get initial estimate for bres SAEM.
`nlmixrUI.saem.distribution()`	Get SAEM distribution
`nlmixrUI.saem.eta.trans()`	Get the eta->eta.trans for SAEM
`nlmixrUI.saem.fit()`	Generate saem.fit user function.
`nlmixrUI.saem.fixed()`	Get parameters that are fixed for SAEM
`nlmixrUI.saem.init.omega()`	SAEM's init$omega
`nlmixrUI.saem.init.theta()`	Generate SAEM initial estimates for THETA.
`nlmixrUI.saem.init()`	Get saem initilization list
`nlmixrUI.saem.log.eta()`	Get model$log.eta for SAEM
`nlmixrUI.saem.model.omega()`	Get the SAEM model Omega
`nlmixrUI.saem.model()`	Generate SAEM model list
`nlmixrUI.saem.res.mod()`	Get the SAEM model$res.mod code
`nlmixrUI.saem.res.name()`	Get error names for SAEM
`nlmixrUI.saem.rx1()`	Return RxODE model with predictions appended
`nlmixrUI.saem.theta.name()`	Get THETA names for nlmixr's SAEM
`nlmixrUI.theta.pars()`	Get the Parameter function with THETA/ETAs defined
`nlmixrVersion()`	Display nlmixr's version
`nlmixr_fit()`	Fit a nlmixr model
`nlmixr`	nlmixr fits population PK and PKPD non-linear mixed effects models.
`nmLst()`	Create a large output based on a nlmixr fit
`nmsimplex()`	Nelder-Mead simplex search
`ofv()`	Return the objective function
`pheno_sd`	Single Dose Phenobarbitol PK/PD
`gof()` `plot(<dyn.ID>)`	Plot of a non-population dynamic model fit
`plot(<dyn.mcmc>)`	Plot of a non-population dynamic model fit using mcmc
`plot(<nlmixrFitData>)`	Plot a nlmixr data object
`plot(<saemFit>)`	Plot an SAEM model fit
`prediction()`	Prediction after a gnlmm fit
`print(<dyn.ID>)`	Print a non-population dynamic model fit object
`summary(<dyn.mcmc>)` `print(<dyn.mcmc>)`	Print summary of a non-population dynamic model fit using mcmc
`print(<gnlmm.fit>)`	Print a gnlmm fit
`print(<nlmixrUI>)`	Print UI function
`print(<saemFit>)`	Print an SAEM model fit summary
`residuals(<nlmixrFitData>)`	Extract residuals from the FOCEI fit
`setCov()`	Set the covariance type based on prior calculated covariances
`setOfv()` `getOfvType()`	Set/get Objective function type for a nlmixr object
`sqrtm()`	Return the square root of general square matrix A
`summary(<dyn.ID>)`	Summary of a non-population dynamic model fit
`summary(<saemFit>)`	Print an SAEM model fit summary
`theo_md`	Multiple dose theophylline PK data
`theo_sd`	Multiple dose theophylline PK data
`vpc_nlmixr_nlme()` `vpcNlmixrNlme()` `vpc(<nlmixrNlme>)`	Visual predictive check (VPC) for nlmixr nlme objects
`vpc_saemFit()` `vpc(<saemFit>)`	VPC for nlmixr saemFit objects
`vpc_ui()` `vpc(<nlmixrFitData>)` `vpc(<nlmixrVpc>)` `vpc(<ui>)`	VPC based on ui model
`boxCox()` `iBoxCox()` `yeoJohnson()` `iYeoJohnson()`	Cox Box, Yeo Johnson and inverse transformation

Reference

Interfacing nlmixr models

Adding residuals & covariance to fit object

Model diagnostics

Datasets

Options for fitting and output

Low level estimation routines

Unit tests and validation functions

Utilities

Covariate Search Functions

ggplot2 functions

Internal functions