Package 'PFIM'

Description

Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to Mentré F, Mallet A, Baccar D (1997) doi:10.1093/biomet/84.2.429, Retout S, Comets E, Samson A, Mentré F (2007) doi:10.1002/sim.2910, Bazzoli C, Retout S, Mentré F (2009) doi:10.1002/sim.3573, Le Nagard H, Chao L, Tenaillon O (2011) doi:10.1186/1471-2148-11-326, Combes FP, Retout S, Frey N, Mentré F (2013) doi:10.1007/s11095-013-1079-3 and Seurat J, Tang Y, Mentré F, Nguyen TT (2021) doi:10.1016/j.cmpb.2021.106126.

Description

Nonlinear mixed effects models (NLMEM) are widely used in model-based drug development and use to analyze longitudinal data. The use of the "population" Fisher Information Matrix (FIM) is a good alternative to clinical trial simulation to optimize the design of these studies. The present version, PFIM 6.1, is an R package that uses the S4 object system for evaluating and/or optimizing population designs based on FIM in NLMEMs.

This version of PFIM now includes a library of models implemented also using the object oriented system S4 of R. This library contains two libraries of pharmacokinetic (PK) and/or pharmacodynamic (PD) models. The PK library includes model with different administration routes (bolus, infusion, first-order absorption), different number of compartments (from 1 to 3), and different types of eliminations (linear or Michaelis-Menten). The PD model library, contains direct immediate models (e.g. Emax and Imax) with various baseline models, and turnover response models. The PK/PD models are obtained with combination of the models from the PK and PD model libraries. PFIM handles both analytical and ODE models and offers the possibility to the user to define his/her own model(s). In PFIM 6.1, the FIM is evaluated by first order linearization of the model assuming a block diagonal FIM as in [3]. The Bayesian FIM is also available to give shrinkage predictions [4]. PFIM 6.1 includes several algorithms to conduct design optimization based on the D-criterion, given design constraints : the simplex algorithm (Nelder-Mead) [5], the multiplicative algorithm [6], the Fedorov-Wynn algorithm [7], PSO (Particle Swarm Optimization) and PGBO (Population Genetics Based Optimizer) [9].

Documentation

Documentation and user guide are available at http://www.pfim.biostat.fr/

Validation

PFIM 6.1 also provides quality control with tests and validation using the evaluated FIM to assess the validity of the new version and its new features. Finally, PFIM 6.1 displays all the results with both clear graphical form and a data summary, while ensuring their easy manipulation in R. The standard data visualization package ggplot2 for R is used to display all the results with clear graphical form [10]. A quality control using the D-criterion is also provided.

Organization of the source files in the /R folder

PFIM 6.1 contains a hierarchy of S4 classes with corresponding methods and functions serving as constructors. All of the source code related to the specification of a certain class is contained in a file named [Name_of_the_class]-Class.R. These classes include:

• 1. all roxygen @include to insure the correctly generated collate for the DESCRIPTION file,

• 2. \setClass preceded by a roxygen documentation that describes the purpose and slots of the class,

• 3. specification of an initialize method,

• 4. all getter and setter, respectively returning attributes of the object and associated objects.

Content of the source code and files in the /R folder

Class Administration

• getOutcome

• setOutcome

• getTimeDose

• setTimeDose

• getDose

• setDose

• getTinf

• setTinf

• getTau

• setTau

Class AdministrationConstraints

• getOutcome

• getDose

Class Arm

• getName

• setName

• getSize

• setSize

• getAdministrations

• setAdministrations

• getSamplingTimes

• setSamplingTimes

• getInitialConditions

• setInitialConditions

• getAdministrationsConstraints

• getSamplingTimesConstraints

• getSamplingTime

• getSamplingTimeConstraint

• setSamplingTimesConstraints

• setSamplingTime

• getAdministration

• getAdministrationConstraint

• EvaluateArm

Class BayesianFim

• EvaluateFisherMatrix

• getRSE

• getConditionNumberVarianceEffects

• getShrinkage

• setShrinkage

• reportTablesFIM

• generateReportEvaluation

Class Combined1

• See class ModelError

Class Constant

• See class ModelError

Class Design

• getName

• setName

• getSize

• setSize

• setArms

• getOutcomesEvaluation

• setOutcomesEvaluation

• getOutcomesGradient

• setOutcomesGradient

• getFim

• setFim

• getNumberOfArms

• setNumberOfArms

• setArm

• EvaluateDesign

• plotOutcomesEvaluation

• plotOutcomesGradient

• reportTablesAdministration

• reportTablesDesign

Class Distribution

• getParameters

• setParameters

• getMu

• setMu

• getOmega

• setOmega

• getAdjustedGradient

Class Evaluation

• run

• reportTablesPlot

• generateTables

• Report

Class FedorovWynnAlgorithm

• FedorovWynnAlgorithm_Rcpp

• resizeFisherMatrix

• setParameters

• optimize

• generateReportOptimization

Class FedorovWynnAlgorithm

• FedorovWynnAlgorithm_Rcpp

• resizeFisherMatrix

• setParameters

• optimize

• generateReportOptimization

Class Fim

• EvaluateFisherMatrix

• EvaluateVarianceFIM

• getFisherMatrix

• setFisherMatrix

• getFixedEffects

• setFixedEffects

• getVarianceEffects

• setVarianceEffects

• getDeterminant

• getDcriterion

• getCorrelationMatrix

• getSE

• getRSE

• getShrinkage

• getEigenValues

• getConditionNumberFixedEffects

• getConditionNumberVarianceEffects

• getColumnAndParametersNamesFIM

• getColumnAndParametersNamesFIMInLatex

• reportTablesFIM

• generateReportEvaluation

• setFimTypeToString

Class GenericMethods

• getName

• getNames

• getSize

• setSize

• getOutcome

• setOutcome

• getFim

• getOdeSolverParameters

• getMu

• setMu

• getOmega

• setOmega

• getParameters

• setParameters

• getModelError

• getSamplings

• getFim

• setName

• setArms

• getArms

Class IndividualFim

• EvaluateFisherMatrix

• EvaluateVarianceFIM

• getRSE

• getShrinkage

• setShrinkage

• reportTablesFIM

• generateReportEvaluation

Class LibraryOfModels

• getName

• getContent

• setContent

• addModel

• addModels

• getLibraryPKModels

• getLibraryPDModels

Class LibraryOfPKPDModels

• getPKModel

• getPDModel

• getPKPDModel

Class LogNormal

• getAdjustedGradient

Class Model

• getName

• setName

• getDescription

• setDescription

• getEquations

• setEquations

• setModelFromLibrary

• getOutcomes

• setOutcomes

• getOutcomesForEvaluation

• setOutcomesForEvaluation

• getParameters

• setParameters

• getModelError

• setModelError

• getInitialConditions

• setInitialConditions

• getOdeSolverParameters

• setOdeSolverParameters

• getModelFromLibrary

• convertPKModelAnalyticToPKModelODE

• getNumberOfParameters

• isModelODE

• isModelAnalytic

• isDoseInEquations

• isModelInfusion

• isModelSteadyState

• isModelBolus

• definePKPDModel

• definePKModel

• defineModel

• defineModelFromLibraryOfModels

• defineModelUserDefined

• defineModelType

• EvaluateModel

• parametersForComputingGradient

• EvaluateVarianceModel

• getFixedParameters

• getModelErrorParametersValues

• reportTablesModelParameters

• reportTablesModelError

Class ModelAnalytic

• EvaluateModel

• definePKModel

• definePKPDModel

• convertPKModelAnalyticToPKModelODE

Class ModelAnalyticBolus

• See class ModelAnalytic

Class ModelAnalyticBolusSteadyState

• See class ModelAnalyticBolus

Class ModelBolus

• See class Model

Class ModelError

• getOutcome

• getEquation

• setEquation

• getDerivatives

• setDerivatives

• getSigmaInter

• setSigmaInter

• getSigmaSlope

• setSigmaSlope

• getcError

• setcError

• getParameters

• EvaluateErrorModelDerivatives

Class ModelInfusion

• getEquationsDuringInfusion

• getEquationsAfterInfusion

• setEquationsAfterInfusion

• setEquationsDuringInfusion

Class ModelODE

• See class Model

Class ModelODEBolus

• EvaluateModel

• definePKPDModel

Class ModelODEDoseInEquations

• EvaluateModel

• definePKModel

• definePKPDModel

Class ModelODEDoseNotInEquations

• EvaluateModel

• definePKModel

• definePKPDModel

Class ModelODEInfusion

• See class ModelInfusion

Class ModelODEInfusionDoseInEquations

• EvaluateModel

• definePKModel

• definePKPDModel

Class ModelParameter

• getName

• getDistribution

• setDistribution

• getFixedMu

• setFixedMu

• getFixedOmega

• setFixedOmega

• getMu

• setMu

• getOmega

• setOmega

Class MultiplicativeAlgorithm

• MultiplicativeAlgorithm_Rcpp

• getLambda

• getDelta

• getNumberOfIterations

• getOptimalWeights

• setOptimalWeights

• setParameters

• optimize

• getDataFrameResults

• plotWeights

• getWeightThreshold

• generateReportOptimization

Class Normal

• getAdjustedGradient

Class Optimization

• getProportionsOfSubjects

• getOptimizationResults

• setOptimizationResults

• getEvaluationFIMResults

• setEvaluationFIMResults

• setEvaluationInitialDesignResults

• getEvaluationInitialDesignResults

• getElementaryProtocols

• generateFimsFromConstraints

• run

• plotWeights

• Report

Class PFIMProject

• getName

• setModel

• getModel

• getModelEquations

• getModelParameters

• getModelError

• getDesigns

• getFim

• getOdeSolverParameters

• getOutcomes

• getOptimizer

• getOptimizerParameters

• run

• generateTables

• Report

Class PGBOAlgorithm

• setParameters

• optimize

• generateReportOptimization

Class PlotEvaluation

• plot

• plotSE

• plotRSE

• plotShrinkage

Class PopulationFim

• EvaluateFisherMatrix

• EvaluateVarianceFIM

• getRSE

• getShrinkage

• setShrinkage

• reportTablesFIM

• computeVMat

• generateReportEvaluation

Class Proportional

• See class ModelError

Class PSOAlgorithm

• setParameters

• optimize

• generateReportOptimization

Class SamplingTimeConstraints

• getOutcome

• getSamplings

• getFixedTimes

• getNumberOfTimesByWindows

• getMinSampling

• getSamplingsWindows

• getNumberOfsamplingsOptimisable

• checkSamplingTimeConstraintsForContinuousOptimization

• generateSamplingsFromSamplingConstraints

Class SamplingTimes

• getOutcome

• setOutcome

• getSamplings

• setSamplings

Class SimplexAlgorithm

• setParameters

• fun.amoeba

• fisher.simplex

• optimize

• generateReportOptimization

Author(s)

Maintainer: Romain Leroux [email protected]

Authors:

• France Mentré [email protected]

• Jérémy Seurat [email protected]

• Lucie Fayette [email protected]

References

[1] Dumont C, Lestini G, Le Nagard H, Mentré F, Comets E, Nguyen TT, et al. PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models. Comput Methods Programs Biomed. 2018;156:217-29.

[2] Chambers JM. Object-Oriented Programming, Functional Programming and R. Stat Sci. 2014;29:167-80.

[3] Mentré F, Mallet A, Baccar D. Optimal Design in Random-Effects Regression Models. Biometrika. 1997;84:429-42.

[4] Combes FP, Retout S, Frey N, Mentré F. Prediction of shrinkage of individual parameters using the Bayesian information matrix in nonlinear mixed effect models with evaluation in pharmacokinetics. Pharm Res. 2013;30:2355-67.

[5] Nelder JA, Mead R. A simplex method for function minimization. Comput J. 1965;7:308-13.

[6] Seurat J, Tang Y, Mentré F, Nguyen, TT. Finding optimal design in nonlinear mixed effect models using multiplicative algorithms. Computer Methods and Programs in Biomedicine, 2021.

[7] Fedorov VV. Theory of Optimal Experiments. Academic Press, New York, 1972.

[8] Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. Proc. of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, 4-6 October 1995, 39-43.

[9] Le Nagard H, Chao L, Tenaillon O. The emergence of complexity and restricted pleiotropy in adapting networks. BMC Evol Biol. 2011;11:326.

[10] Wickham H. ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag New York, 2016.

See Also

Useful links:

• http://www.pfim.biostat.fr/

Description

Add a model to a library of models.

Usage

addModel(object, model)

## S4 method for signature 'LibraryOfModels'
addModel(object, model)

Arguments

Value

The library of models with the added model.

Description

Add a models to a library of models.

Usage

addModels(object, models)

## S4 method for signature 'LibraryOfModels'
addModels(object, models)

Arguments

Value

The library of models with the added models.

Description

The class Administration defines information concerning the parametrization and the type of administration: single dose, multiple doses. Constraints can also be added on the allowed times, doses and infusion duration.

Objects from the class

Objects form the class Administration can be created by calls of the form Administration(...) where (...) are the parameters for the Administration objects.

Slots for Administration objects

outcome:

A character string giving the name for the response of the model.

timeDose:

A numeric vector giving the times when doses are given.

dose:

A numeric vector giving the amount of doses.

Tinf:

A numeric vector giving the infusion duration Tinf (Tinf can be null).

tau:

A numeric giving the frequency.

Description

The class AdministrationConstraints represents the constraint of an input to the system. The class stores information concerning the constraints for the dosage regimen: response of the model, amount of dose.

Objects from the class

Objects form the class AdministrationConstraints can be created by calls of the form AdministrationConstraints(...) where (...) are the parameters for the AdministrationConstraints objects.

Slots for AdministrationConstraints objects

outcome:

A character string giving the name for the response of the model.

doses:

A numeric vector giving the amount of doses.

Description

The class Arm combines the treatment and the sampling schedule.

Objects from the class

Objects form the class Arm can be created by calls of the form Arm(...) where (...) are the parameters for the Arm objects.

Slots for the Arm objects

name:

A string giving the name of the arm.

size:

An integer giving the number of subjects in the arm. By default set to 1.

administrations:

A list of the administrations.

initialConditions:

A list of the initial conditions.

samplingTimes:

A list of the sampling times.

administrationsConstraints:

A list of the administrations constraints.

samplingTimesConstraints:

A list of the sampling times constraints.

Description

The class BayesianFim represents the population Fisher information matrix. The class BayesianFim inherits from the class Fim.

Description

Check for the samplingTime constraints for continuous optimization

Usage

checkSamplingTimeConstraintsForContinuousOptimization(
  object,
  arm,
  newSamplings,
  outcome
)

## S4 method for signature 'SamplingTimeConstraints'
checkSamplingTimeConstraintsForContinuousOptimization(
  object,
  arm,
  newSamplings,
  outcome
)

Arguments

Value

A list of Boolean giving true if the minimal sampling times is in the vector of sampling times & the number of sampling for each windows is respected false otherwise.

Description

Check the validity of he sampling times constraints

Usage

checkValiditySamplingConstraint(object)

## S4 method for signature 'Design'
checkValiditySamplingConstraint(object)

Arguments

Value

An error message if a constraint is not valid.

Description

The class Combined1 defines the the residual error variance according to the formula g(sigmaInter, sigmaSlope, cError, f(x, theta)) = sigmaInter + sigmaSlope*f(x,theta)). The class Combined1 inherits from the class ModelError.

Objects from the class

Combined1 objects are typically created by calls to Combined1 and contain the following slots that are inherited from the class ModelError:

outcome:

A string giving the name of the outcome.

equation:

An symbolic expression of the model error.

derivatives:

A list containing the derivatives of the model error expression.

sigmaInter:

A numeric value giving the sigma inter of the error model.

sigmaSlope:

A numeric value giving the sigma slope of the error model.

cError:

A numeric value giving the exponant c of the error model.

Description

function computeVMat

Usage

computeVMat(varParam1, varParam2, invCholV)

Arguments

Value

VMat

Description

The class Constant defines the the residual error variance according to the formula g(sigma_inter, sigma_slope, c_error, f(x, theta)) = sigma_inter. The class Constant inherits from the class ModelError.

Objects from the class

Constant objects are typically created by calls to Constant and contain the following slots that are inherited from the class ModelError:

outcome:

A string giving the name of the outcome.

equation:

An symbolic expression of the model error.

derivatives:

A list containing the derivatives of the model error expression.

sigmaInter:

A numeric value giving the sigma inter of the error model.

sigmaSlope:

A numeric value giving the sigma slope of the error model.

cError:

A numeric value giving the exponant c of the error model.

Description

Convert an analytic model to a ode model.

Usage

convertPKModelAnalyticToPKModelODE(object)

## S4 method for signature 'ModelAnalytic'
convertPKModelAnalyticToPKModelODE(object)

## S4 method for signature 'ModelAnalyticSteadyState'
convertPKModelAnalyticToPKModelODE(object)

## S4 method for signature 'ModelAnalyticInfusion'
convertPKModelAnalyticToPKModelODE(object)

Arguments

Value

A ode model.

Description

dataForArmEvaluation

Usage

dataForArmEvaluation(object, arm, model)

## S4 method for signature 'Design'
dataForArmEvaluation(object, arm, model)

Arguments

Value

A list containing data for arm evaluation in the design.

Description

Define a model.

Usage

defineModel(object, designs)

## S4 method for signature 'Model'
defineModel(object, designs)

Arguments

Value

A model defined either from the library of models or user defined.

Description

defineModelEquationsFromStringToFunction

Usage

defineModelEquationsFromStringToFunction(
  object,
  parametersNames,
  outcomesWithAdministration,
  outcomesWithNoAdministration
)

## S4 method for signature 'ModelAnalytic'
defineModelEquationsFromStringToFunction(
  object,
  parametersNames,
  outcomesWithAdministration,
  outcomesWithNoAdministration
)

## S4 method for signature 'ModelAnalyticSteadyState'
defineModelEquationsFromStringToFunction(
  object,
  parametersNames,
  outcomesWithAdministration,
  outcomesWithNoAdministration
)

## S4 method for signature 'ModelAnalyticInfusion'
defineModelEquationsFromStringToFunction(
  object,
  parametersNames,
  outcomesWithAdministration,
  outcomesWithNoAdministration
)

## S4 method for signature 'ModelAnalyticInfusionSteadyState'
defineModelEquationsFromStringToFunction(
  object,
  parametersNames,
  outcomesWithAdministration,
  outcomesWithNoAdministration
)

## S4 method for signature 'ModelODEBolus'
defineModelEquationsFromStringToFunction(
  object,
  parametersNames,
  outcomesWithAdministration,
  outcomesWithNoAdministration
)

## S4 method for signature 'ModelODEDoseInEquations'
defineModelEquationsFromStringToFunction(
  object,
  parametersNames,
  outcomesWithAdministration,
  outcomesWithNoAdministration
)

## S4 method for signature 'ModelODEDoseNotInEquations'
defineModelEquationsFromStringToFunction(
  object,
  parametersNames,
  outcomesWithAdministration,
  outcomesWithNoAdministration
)

## S4 method for signature 'ModelODEInfusionDoseInEquations'
defineModelEquationsFromStringToFunction(
  object,
  parametersNames,
  outcomesWithAdministration,
  outcomesWithNoAdministration
)

Arguments

Value

....

Description

Define a model from the library of models.

Usage

defineModelFromLibraryOfModels(object, designs)

## S4 method for signature 'Model'
defineModelFromLibraryOfModels(object, designs)

Arguments

Value

A model defined from the library of models.

Description

Define the type of a model.

Usage

defineModelType(object, designs)

## S4 method for signature 'Model'
defineModelType(object, designs)

Arguments

Value

Return a model defined as analytic, ode, etc.

Description

Define a user defined model.

Usage

defineModelUserDefined(object, designs)

## S4 method for signature 'Model'
defineModelUserDefined(object, designs)

Arguments

Value

A model giving a user defined model.

Description

Define a PK model.

Usage

definePKModel(object, outcomes)

## S4 method for signature 'ModelAnalytic'
definePKModel(object, outcomes)

## S4 method for signature 'ModelAnalyticSteadyState'
definePKModel(object, outcomes)

## S4 method for signature 'ModelAnalyticInfusion'
definePKModel(object, outcomes)

## S4 method for signature 'ModelODEDoseInEquations'
definePKModel(object, outcomes)

## S4 method for signature 'ModelODE'
definePKModel(object, outcomes)

## S4 method for signature 'ModelODEInfusionDoseInEquations'
definePKModel(object, outcomes)

Arguments

Value

A model giving a PK model.

Description

Define a PKPD model.

Usage

definePKPDModel(PKModel, PDModel, outcomes)

## S4 method for signature 'ModelAnalytic,ModelAnalytic'
definePKPDModel(PKModel, PDModel, outcomes)

## S4 method for signature 'ModelAnalytic,ModelODE'
definePKPDModel(PKModel, PDModel, outcomes)

## S4 method for signature 'ModelAnalyticSteadyState,ModelAnalyticSteadyState'
definePKPDModel(PKModel, PDModel, outcomes)

## S4 method for signature 'ModelAnalyticSteadyState,ModelODE'
definePKPDModel(PKModel, PDModel, outcomes)

## S4 method for signature 'ModelAnalyticInfusion,ModelAnalytic'
definePKPDModel(PKModel, PDModel, outcomes)

## S4 method for signature 'ModelAnalyticInfusion,ModelODE'
definePKPDModel(PKModel, PDModel, outcomes)

## S4 method for signature 'ModelODEBolus,ModelODE'
definePKPDModel(PKModel, PDModel, outcomes)

## S4 method for signature 'ModelODEDoseInEquations,ModelODE'
definePKPDModel(PKModel, PDModel, outcomes)

## S4 method for signature 'ModelODEDoseNotInEquations,ModelODE'
definePKPDModel(PKModel, PDModel, outcomes)

## S4 method for signature 'ModelODEInfusion,ModelODE'
definePKPDModel(PKModel, PDModel, outcomes)

## S4 method for signature 'ModelODEInfusionDoseInEquations,ModelODE'
definePKPDModel(PKModel, PDModel, outcomes)

Arguments

Value

A model giving a PKPD model.

Description

The class Design defines information concerning the parametrization of the designs.

Objects from the class

Objects form the class Design can be created by calls of the form Design(...) where (...) are the parameters for the Design objects.

Slots for the Design objects

name:

A string giving the name of the design.

size:

An integer giving the number of subjects in the design.

arms:

A list of the arms.

outcomesEvaluation:

A list of the results of the design evaluation for the outcomes.

outcomesGradient:

A list of the results of the design evaluation for the sensitivity indices.

numberOfArms:

A numeric giving the number of arms in the design.

fim:

An object of the class Fim containing the Fisher Information Matrix of the design.

Description

The class defines all the required methods for a distribution object.

Objects from the class

Objects form the class Distribution can be created by calls of the form Distribution(...) where (...) are the parameters for the Distribution objects.

Slots for Distribution objects

parameters:

A list containing the distribution parameters.

Description

Evaluate an arm.

Usage

EvaluateArm(object, model, dataForModelEvaluation, fim)

## S4 method for signature 'Arm'
EvaluateArm(object, model, dataForModelEvaluation, fim)

Arguments

Value

The object fim containing the Fisher Information Matrix the two lists evaluationOutcomes, outcomesGradient containing the results of the evaluation of the outcome and the sensitivity indices.

Description

Evaluate an design

Usage

EvaluateDesign(object, model, fim)

## S4 method for signature 'Design'
EvaluateDesign(object, model, fim)

Arguments

Value

The object Design with its slot fim, evaluationOutcomes, outcomesGradient updated.

Description

Evaluate the error model derivatives.

Usage

EvaluateErrorModelDerivatives(object, evaluationOutcome)

## S4 method for signature 'ModelError'
EvaluateErrorModelDerivatives(object, evaluationOutcome)

Arguments

Value

A list giving the error variance and the Sigma derivatives.

Description

Evaluate the Fisher matrix ( population, individual and Bayesian )

Usage

EvaluateFisherMatrix(object, model, arm, modelEvaluation, modelVariance)

## S4 method for signature 'BayesianFim'
EvaluateFisherMatrix(object, model, arm, modelEvaluation, modelVariance)

## S4 method for signature 'IndividualFim'
EvaluateFisherMatrix(object, model, arm, modelEvaluation, modelVariance)

## S4 method for signature 'PopulationFim'
EvaluateFisherMatrix(object, model, arm, modelEvaluation, modelVariance)

Arguments

Value

An object from the class Fim containing the Fisher matrix.

Description

Evaluate a model.

Usage

EvaluateModel(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelAnalytic'
EvaluateModel(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelAnalyticSteadyState'
EvaluateModel(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelAnalyticInfusion'
EvaluateModel(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelAnalyticInfusionSteadyState'
EvaluateModel(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelODEBolus'
EvaluateModel(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelODEDoseInEquations'
EvaluateModel(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelODEDoseNotInEquations'
EvaluateModel(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelODEInfusionDoseInEquations'
EvaluateModel(object, dataForModelEvaluation, arm)

Arguments

Value

A list giving the results of the model evaluation.

Description

Evaluate model gradient.

Usage

EvaluateModelGradient(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelAnalytic'
EvaluateModelGradient(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelAnalyticSteadyState'
EvaluateModelGradient(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelAnalyticInfusion'
EvaluateModelGradient(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelAnalyticInfusionSteadyState'
EvaluateModelGradient(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelODEBolus'
EvaluateModelGradient(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelODEDoseInEquations'
EvaluateModelGradient(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelODEDoseNotInEquations'
EvaluateModelGradient(object, dataForModelEvaluation, arm)

## S4 method for signature 'ModelODEInfusionDoseInEquations'
EvaluateModelGradient(object, dataForModelEvaluation, arm)

Arguments

Value

A list giving the results of the model evaluation.

Description

Evaluate the variance of the Fisher information matrix.

Usage

EvaluateVarianceFIM(object, model, arm, modelEvaluation, modelVariance)

## S4 method for signature 'IndividualFim'
EvaluateVarianceFIM(object, model, arm, modelEvaluation, modelVariance)

## S4 method for signature 'PopulationFim'
EvaluateVarianceFIM(object, model, arm, modelEvaluation, modelVariance)

Arguments

Value

A list containing the matrices of the variance of the FIM.

Description

Evaluate the variance of a model.

Usage

EvaluateVarianceModel(object, arm, evaluationModel, data)

## S4 method for signature 'Model'
EvaluateVarianceModel(object, arm, evaluationModel, data)

Arguments

Value

Return a list giving the results of the evaluation of the model variance.

Description

A class storing information concerning the evaluation of a design.

Objects from the class

Objects form the class Evaluation can be created by calls of the form Evaluation(...) where (...) are the parameters for the Evaluation objects.

Slots for the Evaluation objects

name:

A string giving the name of the project.

model:

A object of class Model giving the model.

modelEquations:

A list giving the model equations.

modelParameters:

A list giving the model parameters.

modelError:

A list giving the model error for each outcome of the model.

outcomes:

A list giving the model outcomes.

designs:

A list giving the designs to be evaluated.

fim:

An object of the class Fim containing the Fisher Information Matrix of the design.

odeSolverParameters:

Description

Run the FedorovWynnAlgorithm in Rcpp

Usage

FedorovWynnAlgorithm_Rcpp(
  protocols_input,
  ndimen_input,
  nbprot_input,
  numprot_input,
  freq_input,
  nbdata_input,
  vectps_input,
  fisher_input,
  nok_input,
  protdep_input,
  freqdep_input
)

Arguments

Value

A list giving the results of the outputs of the FedorovWynn algorithm.

Description

Class FedorovWynnAlgorithm represents an initial variable for ODE model.

Objects from the class FedorovWynnAlgorithm

Objects form the class FedorovWynnAlgorithm can be created by calls of the form FedorovWynnAlgorithm(...) where (...) are the parameters for the FedorovWynnAlgorithm objects.

Slots for FedorovWynnAlgorithm objects

elementaryProtocols:

A list of vector for the initial elementary protocols.

numberOfSubjects:

A vector for the number of subjects.

proportionsOfSubjects:

A vector for the number of subjects.

OptimalDesign:

A object Design giving the optimal Design.

showProcess:

A boolean to show the process or not.

FisherMatrix:

A vector giving the Fisher Information

optimalFrequencies:

A vector of the optimal frequencies.

optimalSamplingTimes:

A list of vectors for the optimal sampling times.

optimalDoses:

A vector for the optimal doses.

Description

A class storing information regarding the Fisher matrix. Type of the Fisher information: population ("PopulationFIM"), individual ("IndividualFIM") or Bayesian ("BayesianFIM").

Objects from the class

Objects form the class Fim can be created by calls of the form Fim(...) where (...) are the parameters for the Fim objects.

Slots for Fim objects

fisherMatrix:

A matrix giving the Fisher matrix.

fixedEffects:

A matrix giving the fixed effects of the Fisher matrix.

varianceEffects:

A matrix giving the variance effects of the Fisher matrix.

shrinkage:

A vector giving the shrinkage value of the parameters.

Description

Compute the fisher.simplex

Usage

fisher.simplex(simplex, optimizationObject, outcomes)

Arguments

Value

A list giving the results of the optimization.

Description

function fun.amoeba

Usage

fun.amoeba(p, y, ftol, itmax, funk, outcomes, data, showProcess)

Arguments

Value

A list containing the components of the optimized simplex. 'getColumnAndParametersNamesFIMInLatex.

Description

Generate the fim from the constraints

Usage

generateFimsFromConstraints(object, fims)

## S4 method for signature 'Optimization'
generateFimsFromConstraints(object)

Arguments

Value

A list giving the arms with their fims.

Description

Generate the report for the evaluation

Usage

generateReportEvaluation(
  object,
  evaluationObject,
  outputPath,
  outputFile,
  plotOptions
)

## S4 method for signature 'BayesianFim'
generateReportEvaluation(
  object,
  evaluationObject,
  outputPath,
  outputFile,
  plotOptions
)

## S4 method for signature 'IndividualFim'
generateReportEvaluation(
  object,
  evaluationObject,
  outputPath,
  outputFile,
  plotOptions
)

## S4 method for signature 'PopulationFim'
generateReportEvaluation(
  object,
  evaluationObject,
  outputPath,
  outputFile,
  plotOptions
)

Arguments

Value

Return the report for the evaluation in html.

Description

Generate report for the optimization.

Usage

generateReportOptimization(
  object,
  optimizationObject,
  outputPath,
  outputFile,
  plotOptions
)

## S4 method for signature 'FedorovWynnAlgorithm'
generateReportOptimization(
  object,
  optimizationObject,
  outputPath,
  outputFile,
  plotOptions
)

## S4 method for signature 'MultiplicativeAlgorithm'
generateReportOptimization(
  object,
  optimizationObject,
  outputPath,
  outputFile,
  plotOptions
)

## S4 method for signature 'PGBOAlgorithm'
generateReportOptimization(
  object,
  optimizationObject,
  outputPath,
  outputFile,
  plotOptions
)

## S4 method for signature 'PSOAlgorithm'
generateReportOptimization(
  object,
  optimizationObject,
  outputPath,
  outputFile,
  plotOptions
)

## S4 method for signature 'SimplexAlgorithm'
generateReportOptimization(
  object,
  optimizationObject,
  outputPath,
  outputFile,
  plotOptions
)

Arguments

Value

The report for the optimization in html.

Description

Generate samplings from sampling constraints

Usage

generateSamplingsFromSamplingConstraints(object)

## S4 method for signature 'SamplingTimeConstraints'
generateSamplingsFromSamplingConstraints(object)

Arguments

Value

A list of sampling times generated from the sampling constraints.

Description

Generate the tables for the report.

Usage

generateTables(object, plotOptions)

## S4 method for signature 'Evaluation'
generateTables(object, plotOptions)

## S4 method for signature 'Optimization'
generateTables(object, plotOptions)

Arguments

Value

A list giving the kable able for the report ( evaluation and optimization).

Description

Get the adjusted gradient.

Usage

getAdjustedGradient(object, outcomesGradient)

## S4 method for signature 'LogNormal'
getAdjustedGradient(object, outcomesGradient)

## S4 method for signature 'Normal'
getAdjustedGradient(object, outcomesGradient)

Arguments

Value

A list giving the adjusted gradient.

Description

Get the administrations by outcome.

Usage

getAdministration(object, outcome)

## S4 method for signature 'Arm'
getAdministration(object, outcome)

Arguments

Value

The element of the list administrations containing the administration of the outcome outcome

Description

Get the administration constraints by outcome.

Usage

getAdministrationConstraint(object, outcome)

## S4 method for signature 'Arm'
getAdministrationConstraint(object, outcome)

Arguments

Value

The element of the list getAdministrationConstraint containing the administration constraints of the outcome outcome

Description

Get all the administration for an arm.

Usage

getAdministrations(object)

## S4 method for signature 'Arm'
getAdministrations(object)

Arguments

Value

A list administrations of objects from the class Administration class giving the parameters of the administration for the object Arm.

Description

Get the administrations constraints.

Usage

getAdministrationsConstraints(object)

## S4 method for signature 'Arm'
getAdministrationsConstraints(object)

Arguments

Value

The list administrationsConstraints.

Description

Get the arms of an object.

Usage

getArms(object)

## S4 method for signature 'Design'
getArms(object)

## S4 method for signature 'OptimizationAlgorithm'
getArms(object)

Arguments

Value

A list containing the arms of the object.

Description

Get the parameter c.

Usage

getcError(object)

## S4 method for signature 'ModelError'
getcError(object)

Arguments

Value

A numeric giving the parameter c.

Description

Get the names of the names of the parameters associated to each column of the fim.

Usage

getColumnAndParametersNamesFIM(object, model)

## S4 method for signature 'BayesianFim'
getColumnAndParametersNamesFIM(object, model)

## S4 method for signature 'IndividualFim'
getColumnAndParametersNamesFIM(object, model)

## S4 method for signature 'PopulationFim'
getColumnAndParametersNamesFIM(object, model)

Arguments

Value

A list giving the names of the parameters associated to each column of the fim.

Description

Get the names of the names of the parameters associated to each column of the fim in Latex format.

Usage

getColumnAndParametersNamesFIMInLatex(object, model)

## S4 method for signature 'BayesianFim'
getColumnAndParametersNamesFIMInLatex(object, model)

## S4 method for signature 'IndividualFim'
getColumnAndParametersNamesFIMInLatex(object, model)

## S4 method for signature 'PopulationFim'
getColumnAndParametersNamesFIMInLatex(object, model)

Arguments

Value

A list giving the names of the parameters associated to each column of the fim in Latex format.

Description

Get the condition number of the matrix of the fixed effects.

Usage

getConditionNumberFixedEffects(object)

## S4 method for signature 'Fim'
getConditionNumberFixedEffects(object)

Arguments

Value

A numeric giving the condition number of the matrix of the fixed effects.

Description

Get the condition number of the matrix of the variance effects.

Usage

getConditionNumberVarianceEffects(object)

## S4 method for signature 'Fim'
getConditionNumberVarianceEffects(object)

## S4 method for signature 'BayesianFim'
getConditionNumberVarianceEffects(object)

Arguments

Value

A numeric giving the condition number of the matrix of the variance effects.

Description

Get content of a library of models.

Usage

getContent(object)

## S4 method for signature 'LibraryOfModels'
getContent(object)

Arguments

Value

A list giving the content of the library of models.

Description

Get the correlation matrix.

Usage

getCorrelationMatrix(object)

## S4 method for signature 'Fim'
getCorrelationMatrix(object)

## S4 method for signature 'Evaluation'
getCorrelationMatrix(object)

## S4 method for signature 'Optimization'
getCorrelationMatrix(object)

Arguments

Value

The correlation matrix of the fim.

Description

getDataForArmEvaluation

Usage

getDataForArmEvaluation(object)

## S4 method for signature 'Arm'
getDataForArmEvaluation(object)

Arguments

Value

A list containing the data for arm evaluation.

Description

Get the dataframe of the results.

Usage

getDataFrameResults(object)

## S4 method for signature 'FedorovWynnAlgorithm'
getDataFrameResults(object)

## S4 method for signature 'MultiplicativeAlgorithm'
getDataFrameResults(object)

## S4 method for signature 'Optimization'
getDataFrameResults(object)

Arguments

Value

Return the dataframe of the results.

Description

Get the D criterion of the fim.

Usage

getDcriterion(object)

## S4 method for signature 'Fim'
getDcriterion(object)

## S4 method for signature 'Evaluation'
getDcriterion(object)

## S4 method for signature 'Optimization'
getDcriterion(object)

Arguments

Value

A numeric giving the D criterion of the fim.

Description

Get the parameter delta

Usage

getDelta(object)

## S4 method for signature 'MultiplicativeAlgorithm'
getDelta(object)

Arguments

Value

A numeric giving the parameter delta.

Description

Get the derivatives of the model error equation.

Usage

getDerivatives(object)

## S4 method for signature 'ModelError'
getDerivatives(object)

Arguments

Value

The derivatives of the model error equation.

Description

Get the description of a model.

Usage

getDescription(object)

## S4 method for signature 'Model'
getDescription(object)

Arguments

Value

A list giving the description of a model.

Description

Get the designs.

Usage

getDesigns(object)

## S4 method for signature 'PFIMProject'
getDesigns(object)

Arguments

Value

A list giving the designs of the object.

Description

Get the determinant of the fim.

Usage

getDeterminant(object)

## S4 method for signature 'Fim'
getDeterminant(object)

## S4 method for signature 'Evaluation'
getDeterminant(object)

## S4 method for signature 'Optimization'
getDeterminant(object)

Arguments

Value

A numeric giving the determinant of the fim.

Description

Get the distribution.

Usage

getDistribution(object)

## S4 method for signature 'ModelParameter'
getDistribution(object)

Arguments

Value

The parameter distribution.

Description

Get the amount of doses.

Usage

getDose(object)

## S4 method for signature 'Administration'
getDose(object)

## S4 method for signature 'AdministrationConstraints'
getDose(object)

Arguments

Value

The numeric amount_dose giving the amount of doses.

Description

Get the eigenvalues of the fim.

Usage

getEigenValues(object)

## S4 method for signature 'Fim'
getEigenValues(object)

Arguments

Value

A vector giving the eigenvalues of the fim.

Description

Get the elementary protocols.

Usage

getElementaryProtocols(object, fims)

## S4 method for signature 'Optimization'
getElementaryProtocols(object, fims)

Arguments

Value

A list containing the results of the evaluation of the elementary protocols giving the numberOfTimes, nbOfDimensions, totalCost, samplingTimes and the fisherMatrices

Description

Get the equation of a model error.

Usage

getEquation(object)

## S4 method for signature 'ModelError'
getEquation(object)

Arguments

Value

An expression giving the equation of a model error.

Description

Get the equations of a model.

Usage

getEquations(object)

## S4 method for signature 'Model'
getEquations(object)

Arguments

Value

The list giving the equations of the model.

Description

Get the equations after infusion.

Usage

getEquationsAfterInfusion(object)

## S4 method for signature 'Model'
getEquationsAfterInfusion(object)

Arguments

Value

A list giving the equations after the infusion.

Description

Get the equations during infusion.

Usage

getEquationsDuringInfusion(object)

## S4 method for signature 'Model'
getEquationsDuringInfusion(object)

Arguments

Value

A list giving the equations during the infusion.

Description

Get the results of the evaluation.

Usage

getEvaluationFIMResults(object)

## S4 method for signature 'Optimization'
getEvaluationFIMResults(object)

Arguments

Value

An object from the class Evaluation giving the evaluation results for the optimal design.

Description

Get the evaluation results of the initial design.

Usage

getEvaluationInitialDesignResults(object)

## S4 method for signature 'Optimization'
getEvaluationInitialDesignResults(object)

Arguments

Value

The object from the class Evaluation giving the results of the evaluation of the initial design.

Description

Get the fim of an of an object.

Usage

getFim(object)

## S4 method for signature 'Design'
getFim(object)

## S4 method for signature 'PFIMProject'
getFim(object)

## S4 method for signature 'OptimizationAlgorithm'
getFim(object)

Arguments

Value

The FIM of the object.

Description

Get the FIM.

Usage

getFisherMatrix(object)

## S4 method for signature 'Fim'
getFisherMatrix(object)

## S4 method for signature 'Evaluation'
getFisherMatrix(object)

## S4 method for signature 'Optimization'
getFisherMatrix(object)

Arguments

Value

A matrix giving the FIM.

Description

Get the matrix of fixed effects.

Usage

getFixedEffects(object)

## S4 method for signature 'Fim'
getFixedEffects(object)

Arguments

Value

The matrix of the fixed effects.

Description

Get the fixed effect.

Usage

getFixedMu(object)

## S4 method for signature 'ModelParameter'
getFixedMu(object)

Arguments

Value

A boolean giving the fixed mu.

Description

Get the fixed variance.

Usage

getFixedOmega(object)

## S4 method for signature 'ModelParameter'
getFixedOmega(object)

Arguments

Value

A boolean giving the fixed omega.

Description

Get the fixed parameters.

Usage

getFixedParameters(object)

## S4 method for signature 'Model'
getFixedParameters(object)

Arguments

Value

A list giving the fixed parameters of the model.

Description

Get the fixed sampling times.

Usage

getFixedTimes(object)

## S4 method for signature 'SamplingTimeConstraints'
getFixedTimes(object)

Arguments

Value

A vector giving the foxed sampling times.

Description

Get the initial condition for the evaluation of an ode model.

Usage

getInitialConditions(object)

## S4 method for signature 'Arm'
getInitialConditions(object)

## S4 method for signature 'Model'
getInitialConditions(object)

Arguments

Value

The list initialConditions for the object Arm.

Description

Get the iteration with the convergence criteria.

Usage

getIterationAndCriteria(object)

## S4 method for signature 'OptimizationAlgorithm'
getIterationAndCriteria(object)

Arguments

Value

A dataframe giving the iteration with the convergence criteria.

Description

Get the parameter lambda.

Usage

getLambda(object)

## S4 method for signature 'MultiplicativeAlgorithm'
getLambda(object)

Arguments

Value

A numeric giving the parameter lambda.

Description

Get the library of PD models.

Usage

getLibraryPDModels(object)

## S4 method for signature 'LibraryOfModels'
getLibraryPDModels(object)

Arguments

Value

A list giving the PD models.

Description

Get the library of PK models.

Usage

getLibraryPKModels(object)

## S4 method for signature 'LibraryOfModels'
getLibraryPKModels(object)

Arguments

Value

A list giving the PK models.

Description

Get the minimal sampling times.

Usage

getMinSampling(object)

## S4 method for signature 'SamplingTimeConstraints'
getMinSampling(object)

Arguments

Value

A numeric giving the minimal sampling times.

Description

Get the model.

Usage

getModel(object)

## S4 method for signature 'PFIMProject'
getModel(object)

Arguments

Value

The model of the object.

Description

Get the model equations.

Usage

getModelEquations(object)

## S4 method for signature 'PFIMProject'
getModelEquations(object)

Arguments

Value

A list giving the model equations.

Description

Get the model error.

Usage

getModelError(object)

## S4 method for signature 'Model'
getModelError(object)

## S4 method for signature 'PFIMProject'
getModelError(object)

Arguments

Value

The model error of the object.

Description

Get the values of the model error parameters.

Usage

getModelErrorParametersValues(object)

## S4 method for signature 'Model'
getModelErrorParametersValues(object)

Arguments

Value

A list giving the values of the model error parameters.

Description

Get a model from the library of models.

Usage

getModelFromLibrary(object)

## S4 method for signature 'Model'
getModelFromLibrary(object)

Arguments

Value

Return a model from the the library of models.

Description

Get the model parameters.

Usage

getModelParameters(object)

## S4 method for signature 'PFIMProject'
getModelParameters(object)

Arguments

Value

A list giving the model parameters.

Description

Get the values of the model parameters.

Usage

getModelParametersValues(object)

## S4 method for signature 'Model'
getModelParametersValues(object)

Arguments

Value

A list giving the values of the model parameters.

Description

Get the fixed effect of an object.

Usage

getMu(object)

## S4 method for signature 'Distribution'
getMu(object)

## S4 method for signature 'ModelParameter'
getMu(object)

Arguments

Value

The object with the updated fixed effect.

Description

Get the name of an object

Usage

getName(object)

## S4 method for signature 'Arm'
getName(object)

## S4 method for signature 'Design'
getName(object)

## S4 method for signature 'ModelParameter'
getName(object)

## S4 method for signature 'LibraryOfModels'
getName(object)

## S4 method for signature 'Model'
getName(object)

## S4 method for signature 'PFIMProject'
getName(object)

Arguments

Value

A character string name giving the name of the object.

Description

Get the names of an object.

Usage

getNames(object)

## S4 method for signature 'list'
getNames(object)

Arguments

Value

A vector giving the names of the object.

Description

Get the number of arms in a design.

Usage

getNumberOfArms(object)

## S4 method for signature 'Design'
getNumberOfArms(object)

Arguments

Value

A numeric numberOfArms giving the number of arms in the design.

Description

Get the number of iterations.

Usage

getNumberOfIterations(object)

## S4 method for signature 'MultiplicativeAlgorithm'
getNumberOfIterations(object)

Arguments

Value

A numeric giving the number of iterations.

Description

Get the number of parameters.

Usage

getNumberOfParameters(object)

## S4 method for signature 'Model'
getNumberOfParameters(object)

Arguments

Value

A numeric giving the number of parameters of the model.

Description

Get the number of sampling times that are optimisable.

Usage

getNumberOfsamplingsOptimisable(object)

## S4 method for signature 'SamplingTimeConstraints'
getNumberOfsamplingsOptimisable(object)

Arguments

Value

A vector giving the number of sampling times that are optimisable.

Description

Get the number of sampling times by windows.

Usage

getNumberOfTimesByWindows(object)

## S4 method for signature 'SamplingTimeConstraints'
getNumberOfTimesByWindows(object)

Arguments

Value

A vector giving the number of sampling times by windows.

Description

Get the parameters for the ode solvers of an object.

Usage

getOdeSolverParameters(object)

## S4 method for signature 'Model'
getOdeSolverParameters(object)

## S4 method for signature 'PFIMProject'
getOdeSolverParameters(object)

Arguments

Value

The list giving the parameters for the ode solvers.

Description

Get the matrix omega of an object.

Usage

getOmega(object)

## S4 method for signature 'Distribution'
getOmega(object)

## S4 method for signature 'ModelParameter'
getOmega(object)

Arguments

Value

The matrix omega of an object.

Description

Get the optimal design.

Usage

getOptimalDesign(object)

## S4 method for signature 'OptimizationAlgorithm'
getOptimalDesign(object)

Arguments

Value

The optimal design.

Description

Get the optimal frequencies

Usage

getOptimalFrequencies(object)

## S4 method for signature 'FedorovWynnAlgorithm'
getOptimalFrequencies(object)

Arguments

Value

A vector giving the optimal frequencies

Description

Get the optimal weights.

Usage

getOptimalWeights(object)

## S4 method for signature 'MultiplicativeAlgorithm'
getOptimalWeights(object)

Arguments

Value

A vector giving the optimal weights.

Description

Get the optimization results.

Usage

getOptimizationResults(object)

## S4 method for signature 'Optimization'
getOptimizationResults(object)

Arguments

Value

An object from the class OptimizationAlgorithm giving the optimization results.

Description

Get the optimization algorithm.

Usage

getOptimizer(object)

## S4 method for signature 'PFIMProject'
getOptimizer(object)

Arguments

Value

A string giving the name of the optimization algorithm.

Description

Get the optimization parameters.

Usage

getOptimizerParameters(object)

## S4 method for signature 'PFIMProject'
getOptimizerParameters(object)

Arguments

Value

A list giving the optimization parameters.

Description

Get the outcome of an object.

Usage

getOutcome(object)

## S4 method for signature 'Administration'
getOutcome(object)

## S4 method for signature 'AdministrationConstraints'
getOutcome(object)

## S4 method for signature 'ModelError'
getOutcome(object)

## S4 method for signature 'SamplingTimeConstraints'
getOutcome(object)

## S4 method for signature 'SamplingTimes'
getOutcome(object)

Arguments

Value

A string giving the outcome of the object.

Description

Get the outcomes of a model.

Usage

getOutcomes(object)

## S4 method for signature 'Model'
getOutcomes(object)

## S4 method for signature 'PFIMProject'
getOutcomes(object)

Arguments

Value

A list giving the outcomes of the model.

Description

Get the results of the evaluation of the outcomes.

Usage

getOutcomesEvaluation(object)

## S4 method for signature 'Design'
getOutcomesEvaluation(object)

Arguments

Value

The list outcomesEvaluation containing the results of the design evaluation for the outcomes.

Description

Get the outcomes of a model used for the evaluation (is scales outcomes).

Usage

getOutcomesForEvaluation(object)

## S4 method for signature 'Model'
getOutcomesForEvaluation(object)

Arguments

Value

A list giving the outcomes of a model used for the evaluation (is scales outcomes).

Description

Get the results of the evaluation of the outcome gradients.

Usage

getOutcomesGradient(object)

## S4 method for signature 'Design'
getOutcomesGradient(object)

Arguments

Value

The list outcomesGradient containing the results of the design evaluation for the outcome gradients.

Description

Get the parameters of an object.

Usage

getParameters(object)

## S4 method for signature 'ModelError'
getParameters(object)

## S4 method for signature 'Distribution'
getParameters(object)

## S4 method for signature 'Model'
getParameters(object)

Arguments

Value

Return the list of the parameters of the object.

Description

Get a PD model.

Usage

getPDModel(object, PDModelName)

## S4 method for signature 'LibraryOfPKPDModels'
getPDModel(object, PDModelName)

Arguments

Value

Return a PD model.

Description

Get a PK model.

Usage

getPKModel(object, PKModelName)

## S4 method for signature 'LibraryOfPKPDModels'
getPKModel(object, PKModelName)

Arguments

Value

Return a PK model.

Description

Get a PKPD model.

Usage

getPKPDModel(object, namesModel)

## S4 method for signature 'LibraryOfPKPDModels'
getPKPDModel(object, namesModel)

Arguments

Value

Return a PKPD model.

Description

Get the plot options for graphs responses and SI

Usage

getPlotOptions(plotOptions, outcomesNames)

Arguments

Value

The list containing the plot options.

Description

Get the proportion of subjects.

Usage

getProportionsOfSubjects(object)

## S4 method for signature 'Optimization'
getProportionsOfSubjects(object)

Arguments

Value

A vector giving the proportion of subjects.

Description

Get the RSE

Usage

getRSE(object, model)

## S4 method for signature 'BayesianFim'
getRSE(object, model)

## S4 method for signature 'Evaluation'
getRSE(object, model)

## S4 method for signature 'IndividualFim'
getRSE(object, model)

## S4 method for signature 'Optimization'
getRSE(object, model)

## S4 method for signature 'PopulationFim'
getRSE(object, model)

Arguments

Value

A vector giving the RSE.

Description

Get the sampling of an object.

Usage

getSamplings(object)

## S4 method for signature 'SamplingTimeConstraints'
getSamplings(object)

## S4 method for signature 'SamplingTimes'
getSamplings(object)

Arguments

Value

A list of the samplings of the object.

Description

Get the windows for the sampling times.

Usage

getSamplingsWindows(object)

## S4 method for signature 'SamplingTimeConstraints'
getSamplingsWindows(object)

Arguments

Value

A list giving the vector of the windows for the sampling times.

Description

Get the sampling times by outcome.

Usage

getSamplingTime(object, outcome)

## S4 method for signature 'Arm'
getSamplingTime(object, outcome)

Arguments

Value

The element of the list samplingTimes containing the sampling times of the outcome outcome

Description

Get the sampling times constraints by outcome.

Usage

getSamplingTimeConstraint(object, outcome)

## S4 method for signature 'Arm'
getSamplingTimeConstraint(object, outcome)

Arguments

Value

The element of the list samplingTimesConstraints containing the sampling times constraints of the outcome outcome

Description

Get the vectors of sampling times for an arm.

Usage

getSamplingTimes(object)

## S4 method for signature 'Arm'
getSamplingTimes(object)

Arguments

Value

The list samplingTimes for the object Arm.

Description

Get the sampling times constraints.

Usage

getSamplingTimesConstraints(object)

## S4 method for signature 'Arm'
getSamplingTimesConstraints(object)

Arguments

Value

The list getSamplingTimesConstraints.

Description

Get the SE.

Usage

getSE(object)

## S4 method for signature 'Fim'
getSE(object)

## S4 method for signature 'Evaluation'
getSE(object)

## S4 method for signature 'Optimization'
getSE(object)

Arguments

Value

A vector giving the SE.

Description

Get the shrinkage.

Usage

getShrinkage(object)

## S4 method for signature 'BayesianFim'
getShrinkage(object)

## S4 method for signature 'Evaluation'
getShrinkage(object)

## S4 method for signature 'IndividualFim'
getShrinkage(object)

## S4 method for signature 'Optimization'
getShrinkage(object)

## S4 method for signature 'PopulationFim'
getShrinkage(object)

Arguments

Value

A vector giving the shrinkage of the Bayesian fim.

Description

Get the parameter sigma inter.

Usage

getSigmaInter(object)

## S4 method for signature 'ModelError'
getSigmaInter(object)

Arguments

Value

A numeric giving the parameter sigma inter.

Description

Get the parameter sigma slope.

Usage

getSigmaSlope(object)

## S4 method for signature 'ModelError'
getSigmaSlope(object)

Arguments

Value

A numeric giving the parameter sigma slope.

Description

Get the size of an object.

Usage

getSize(object)

## S4 method for signature 'Arm'
getSize(object)

## S4 method for signature 'Design'
getSize(object)

Arguments

Value

A numeric giving the size of the object.

Description

Get the frequency tau.

Usage

getTau(object)

## S4 method for signature 'Administration'
getTau(object)

Arguments

Value

The numeric tau giving the frequency tau.

Description

Get the times vector when doses are given.

Usage

getTimeDose(object)

## S4 method for signature 'Administration'
getTimeDose(object)

Arguments

Value

The vector timeDose giving the times when the doses are given.

Description

Get the infusion duration.

Usage

getTinf(object)

## S4 method for signature 'Administration'
getTinf(object)

Arguments

Value

The numeric Tinf giving the infusion duration Tinf.

Description

The class ModelODEBolus defines information concerning the construction of an ode model bolus. The class ModelODEBolus inherits from the class ModelBolus.

Usage

getVariables(object)

## S4 method for signature 'ModelODE'
getVariables(object)

## S4 method for signature 'ModelODEBolus'
getVariables(object)

## S4 method for signature 'ModelInfusion'
getVariables(object)

Arguments

Value

Return the variable of an ode model

Description

Get the matrix of the variance effects.

Usage

getVarianceEffects(object)

## S4 method for signature 'Fim'
getVarianceEffects(object)

Arguments

Value

The matrix of the variance effects.

Description

Get the parameter weightThreshold

Usage

getWeightThreshold(object)

## S4 method for signature 'MultiplicativeAlgorithm'
getWeightThreshold(object)

Arguments

Value

A numeric giving the WeightThreshold.

Description

A class storing information regarding the individual Fisher matrix. The class IndividualFim inherits from the class Fim.

Description

initialize

Usage

## S4 method for signature 'Administration'
initialize(.Object, outcome, timeDose, dose, Tinf, tau)

Arguments

Value

Administration

Description

initialize

Usage

## S4 method for signature 'AdministrationConstraints'
initialize(.Object, outcome, doses)

Arguments

Description

initialize

Usage

## S4 method for signature 'Arm'
initialize(
  .Object,
  name,
  size,
  administrations,
  initialConditions,
  samplingTimes,
  administrationsConstraints,
  samplingTimesConstraints,
  dataForArmEvaluation
)

Arguments

Value

Arm

Description

initialize

Usage

## S4 method for signature 'Combined1'
initialize(
  .Object,
  outcome,
  equation,
  derivatives,
  sigmaInter,
  sigmaSlope,
  cError
)

Arguments

Value

Combined1

Description

initialize

Usage

## S4 method for signature 'Constant'
initialize(
  .Object,
  outcome,
  equation,
  derivatives,
  sigmaInter,
  sigmaSlope,
  cError
)

Arguments

Value

Constant

Description

initialize

Usage

## S4 method for signature 'Design'
initialize(
  .Object,
  name,
  size,
  arms,
  outcomesEvaluation,
  outcomesGradient,
  numberOfArms,
  fim
)

Arguments

Value

Design

Description

initialize

Usage

## S4 method for signature 'Distribution'
initialize(.Object, parameters)

Arguments

Value

Distribution

Description

initialize

Usage

## S4 method for signature 'Evaluation'
initialize(
  .Object,
  name,
  model,
  modelEquations,
  modelParameters,
  modelError,
  outcomes,
  designs,
  fim,
  odeSolverParameters
)

Arguments

Value

Evaluation

Description

initialize

Usage

## S4 method for signature 'FedorovWynnAlgorithm'
initialize(
  .Object,
  elementaryProtocols,
  numberOfSubjects,
  proportionsOfSubjects,
  showProcess
)

Arguments

Value

FedorovWynnAlgorithm

Description

initialize

Usage

## S4 method for signature 'Fim'
initialize(.Object, fisherMatrix, fixedEffects, varianceEffects, shrinkage)

Arguments

Value

Fim

Description

initialize

Usage

## S4 method for signature 'LibraryOfModels'
initialize(.Object, name, content)

Arguments

Value

LibraryOfModels

Description

initialize

Usage

## S4 method for signature 'LibraryOfPKPDModels'
initialize(.Object)

Arguments

Value

LibraryOfPKPDModels

Description

initialize

Usage

## S4 method for signature 'LogNormal'
initialize(.Object, ...)

Arguments

Value

LogNormal

Description

initialize

Usage

## S4 method for signature 'Model'
initialize(
  .Object,
  name,
  description,
  equations,
  outcomes,
  outcomesForEvaluation,
  parameters,
  modelError,
  initialConditions,
  odeSolverParameters,
  modelFromLibrary
)

Arguments

Value

Model

Description

initialize

Usage

## S4 method for signature 'ModelAnalytic'
initialize(
  .Object,
  name,
  description,
  equations,
  outcomes,
  parameters,
  modelError
)

Arguments

Value

ModelAnalytic

Description

initialize

Usage

## S4 method for signature 'ModelAnalyticBolus'
initialize(
  .Object,
  name,
  description,
  equations,
  outcomes,
  parameters,
  modelError
)

Arguments

Value

ModelAnalyticBolus

Description

initialize

Usage

## S4 method for signature 'ModelAnalyticBolusSteadyState'
initialize(
  .Object,
  name,
  description,
  equations,
  outcomes,
  parameters,
  modelError
)

Arguments

Value

ModelAnalyticBolusSteadyState

Description

initialize

Usage

## S4 method for signature 'ModelAnalyticInfusion'
initialize(
  .Object,
  name,
  description,
  equations,
  outcomes,
  parameters,
  modelError
)

Arguments

Value

ModelAnalyticInfusion

Description

initialize

Usage

## S4 method for signature 'ModelAnalyticInfusionSteadyState'
initialize(
  .Object,
  name,
  description,
  equations,
  outcomes,
  parameters,
  modelError
)

Arguments

Value

ModelAnalyticInfusionSteadyState

Description

initialize

Usage

## S4 method for signature 'ModelAnalyticSteadyState'
initialize(
  .Object,
  name,
  description,
  equations,
  outcomes,
  parameters,
  modelError
)

Arguments