Function reference • tdmore

Evaluate precision dosing All these functions need a model, see section ‘Models’
Fit population data These functions perform posthoc and/or proseval on population data
`dataTibble()`	Utility function to create a data tibble.
`posthoc()` `proseval()`	Find the individual parameters for a set of virtual subjects in a population, either based on all available data (posthoc), or including data piece-wise as time progresses (proseval).
Add predictions for the population These functions can be used to append predictions to each tdmorefit instance in the population
`predict(<tdmorefit>)`	Predict new data using a model fit
`logLik(<tdmorefit>)`	Calculate the log-likelihood of the predicted values. If fixed values were used to obtain the tdmorefit, they are considered estimated and are included in the population log-likelihood.
`as.population()`	Create a typical value subject
`as.sample()`	Create a tiible of N random tdmorefit instances
Simulate precision dosing
`findDose()` `findDoses()` `doseSimulation()`	Simulate dose adaptation in a population
Models
Tdmore models Parameter estimation and dose recommendation needs a model. The methods below allow you to construct a model using `nlmixr`, `RxODE`, or algebraic equations.
`tdmore()`	Append TDM functionality to a pharmacometrics structural model.
`errorModel()`	Instantiate a new error model.
Metadata Model metadata allows to intelligently guess certain default settings
`metadata()`	Append metadata to a TDMore model
`getDosingInterval()`	Get the dosing interval for the given formulation
`covariate()`	Create a new covariate object.
`output()`	Create a new output object.
`formulation()`	Create a new formulation object.
`target()`	Create a new target.
`getMetadataByName()`	Get metadata (output, formulation or covariate) by name.
`getMetadataByClass()`	Get metadata by class.
`observed_variables()`	Create an 'observed variables' object. This metadata indicates that these variables are interesting outputs of the model, and should be included in a plot.
`getObservedVariables()`	Get the observed variables from the tdmore model.
Algebraic models Use an algebraic model in tdmore
`algebraic()`	Initialize a structural model using algebraic equations.
`tdmore(<algebraic>)`	Build a tdmore object based on an algebraic model
`linCmt()`	This function guesses which model should be calculated, based on the available variables in the caller environment
`pk1cptiv_()` `pk1cptinfusion_()` `pk1cptoral_()` `pk2cptiv_()` `pk2cptinfusion_()` `pk2cptoral_()` `pk3cptiv_()` `pk3cptinfusion_()` `pk3cptoral_()`	Algebraic equations for PK models
`pk1cptiv()` `pk1cptinfusion()` `pk1cptoral()` `pk2cptiv()` `pk2cptinfusion()` `pk2cptoral()` `pk3cptiv()` `pk3cptinfusion()` `pk3cptoral()`	Executes the requested PK model, and fetches the arguments from the caller's environment.
`pkmodel()`	Execute the appropriate PK model, based on the same rules as used by Monolix
Model-predictive control Replace empirical bayesian estimation with model-predictive control in a tdmore model
`mpc()`	MPC is a generic function to make a tdmore model compatible with MPC.
Mixture models A mixture model combines two subpopulations with an a priori probability of belonging to either model A or model B
`tdmore_mixture()`	Create a TDMore mixture model.
`tdmore_mixture_covariates()`	Create a TDMore mixture model, based on several options for a discrete covariate.
Example models Example datasets, with their respective population PK model as estimated by nlmixr.
`theopp`	Theophylline dataset, as included in the NONMEM distribution.
`theopp_nlmixr`	NlmixrUI object describing theophylline 1-compartment model with oral absorption
`pheno`	Phenobarbitol dataset, as included in the NONMEM distribution.
`pheno_nlmixr`	NlmixrUI object describing Phenobarbitol 1-compartment model with IV administration and allometric scaling on weight
`getModel()`	This searches for the given model name in a directory
`defaultModel()`	Get the first available model name in the given directory
Plotting These functions can be used to plot more detail for predictions
`autolayer(<tdmorefit>)` `autoplot(<tdmore>)`	Automatically plot a tdmorefit object
`autoplot(<tdmorefit>)`	Plot a tdmorefit object.
`autolayer(<recommendation>)`	Automatically create the required layers for a recommendation object
`autoplot(<tdmorefit_mixture>)`	Plot a tdmorefit_mixture object.
`parameterPlot.tdmorefit()`	Generate a plot of the parameters
`predictionLayer()`	This creates a special type of ggplot2 Layer object. The layer searches for a tdmorefit object in either the plot, or its own arguments. It then calls the `predict()` function and provides this data.frame instead of the original data

Reference

Evaluate precision dosing

Fit population data

Add predictions for the population

Simulate precision dosing

Models

Tdmore models

Metadata

Algebraic models

Model-predictive control

Mixture models

Example models

Plotting