Package 'PFIM'

Title: Population Fisher Information Matrix
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>.
Authors: France Mentré [aut], Romain Leroux [aut, cre], Jérémy Seurat [aut], Lucie Fayette [aut]
Maintainer: Romain Leroux <[email protected]>
License: GPL (>= 2)
Version: 6.1
Built: 2024-10-24 04:24:46 UTC
Source: CRAN

Help Index


Fisher Information matrix for design evaluation/optimization for nonlinear mixed effects models.

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

Class AdministrationConstraints

Class Arm

Class BayesianFim

Class Combined1

Class Constant

Class Design

Class Distribution

Class Evaluation

Class FedorovWynnAlgorithm

Class FedorovWynnAlgorithm

Class Fim

Class GenericMethods

Class IndividualFim

Class LibraryOfModels

Class LibraryOfPKPDModels

Class LogNormal

Class Model

Class ModelAnalytic

Class ModelAnalyticBolus

Class ModelAnalyticBolusSteadyState

Class ModelBolus

Class ModelError

Class ModelInfusion

Class ModelODE

Class ModelODEBolus

Class ModelODEDoseInEquations

Class ModelODEDoseNotInEquations

Class ModelODEInfusion

Class ModelODEInfusionDoseInEquations

Class ModelParameter

Class MultiplicativeAlgorithm

Class Normal

Class Optimization

Class PFIMProject

Class PGBOAlgorithm

Class PlotEvaluation

Class PopulationFim

Class Proportional

Class PSOAlgorithm

Class SamplingTimeConstraints

Class SamplingTimes

Class SimplexAlgorithm

Author(s)

Maintainer: Romain Leroux [email protected]

Authors:

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:


Add a model to a library of models.

Description

Add a model to a library of models.

Usage

addModel(object, model)

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

Arguments

object

An object from the class LibraryOfModels.

model

An object from the class Model.

Value

The library of models with the added model.


Add a models to a library of models.

Description

Add a models to a library of models.

Usage

addModels(object, models)

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

Arguments

object

An object from the class LibraryOfModels.

models

A list of object from the class Model.

Value

The library of models with the added models.


Class "Administration"

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.


Class "AdministrationConstraints"

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.


Class "Arm"

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.


Class "BayesianFim"

Description

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


Check for the samplingTime constraints for continuous optimization

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

object

An object from the class SamplingTimeConstraints.

arm

An object from the class Arm.

newSamplings

A vector giving the new sampling.

outcome

The outcomes for the model.

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.


checkValiditySamplingConstraint

Description

Check the validity of he sampling times constraints

Usage

checkValiditySamplingConstraint(object)

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

Arguments

object

An object from the class Design.

Value

An error message if a constraint is not valid.


Class "Combined1"

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.


function computeVMat

Description

function computeVMat

Usage

computeVMat(varParam1, varParam2, invCholV)

Arguments

varParam1

varParam1

varParam2

varParam2

invCholV

invCholV

Value

VMat


Class "Constant"

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.


Convert an analytic model to a ode 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

object

An object from the class Model.

Value

A ode model.


dataForArmEvaluation

Description

dataForArmEvaluation

Usage

dataForArmEvaluation(object, arm, model)

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

Arguments

object

An object Design from the class Design.

arm

...

model

An object Model from the class Model.

Value

A list containing data for arm evaluation in the design.


Define a model.

Description

Define a model.

Usage

defineModel(object, designs)

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

Arguments

object

An object from the class Model.

designs

A list of objects from the class Design.

Value

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


defineModelEquationsFromStringToFunction

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

object

An object from the class Model.

parametersNames

Vector of parameter names.

outcomesWithAdministration

Vector of the name of the outcome with administration.

outcomesWithNoAdministration

Vector of the name of the outcome with no administration.

Value

....


Define a model from the library of models.

Description

Define a model from the library of models.

Usage

defineModelFromLibraryOfModels(object, designs)

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

Arguments

object

An object from the class Model.

designs

A list of objects from the class Design.

Value

A model defined from the library of models.


Define the type of a model.

Description

Define the type of a model.

Usage

defineModelType(object, designs)

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

Arguments

object

An object from the class Model.

designs

A list of objects from the class Design.

Value

Return a model defined as analytic, ode, etc.


Define a user defined model.

Description

Define a user defined model.

Usage

defineModelUserDefined(object, designs)

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

Arguments

object

An object from the class Model.

designs

A list of objects from the class Design.

Value

A model giving a user defined model.


Define a PK 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

object

An object from the class Model.

outcomes

A list giving the outcomes of the PK model.

Value

A model giving a PK model.


Define a PKPD 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

PKModel

An object from the class Model.

PDModel

An object from the class Model.

outcomes

A list giving the outcomes of the PKPD model.

Value

A model giving a PKPD model.


Class "Design"

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.


Class "Distribution"

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.


EvaluateArm

Description

Evaluate an arm.

Usage

EvaluateArm(object, model, dataForModelEvaluation, fim)

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

Arguments

object

An object arm from the class Arm.

model

An object model from the class Model.

dataForModelEvaluation

....

fim

An object fim from the class Fim.

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.


EvaluateDesign

Description

Evaluate an design

Usage

EvaluateDesign(object, model, fim)

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

Arguments

object

An object Design from the class Design.

model

An object model from the class Model.

fim

An object fim from the class Fim.

Value

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


Evaluate the error model derivatives.

Description

Evaluate the error model derivatives.

Usage

EvaluateErrorModelDerivatives(object, evaluationOutcome)

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

Arguments

object

An object from the class ModelError.

evaluationOutcome

A list giving the results of the model evaluation.

Value

A list giving the error variance and the Sigma derivatives.


Evaluate the Fisher matrix ( population, individual and Bayesian )

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

object

An object from the class Fim.

model

An object from the class Model.

arm

An object from the class Arm.

modelEvaluation

A list containing the evaluation results.

modelVariance

A list containing the model variance.

Value

An object from the class Fim containing the Fisher matrix.


Evaluate a model.

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

object

An object from the class Model.

dataForModelEvaluation

...

arm

An object from the class Arm.

Value

A list giving the results of the model evaluation.


Evaluate model gradient.

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

object

An object from the class Model.

dataForModelEvaluation

...

arm

An object from the class Arm.

Value

A list giving the results of the model evaluation.


Evaluate the variance of the Fisher information matrix.

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

object

An object from the class Fim.

model

An object from the class Model.

arm

An object from the class Arm.

modelEvaluation

A list containing the evaluation results.

modelVariance

A list containing the model variance.

Value

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


Evaluate the variance of a model.

Description

Evaluate the variance of a model.

Usage

EvaluateVarianceModel(object, arm, evaluationModel, data)

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

Arguments

object

An object from the class Model.

arm

An object from the class Arm.

evaluationModel

A list giving the outputs of the model evaluation.

data

...

Value

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


Class "Evaluation"

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:

Fedorov-Wynn algorithm in Rcpp.

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

protocols_input

parameter protocols_input

ndimen_input

parameter ndimen_input

nbprot_input

parameter nbprot_input

numprot_input

parameter numprot_input

freq_input

parameter freq_input

nbdata_input

parameter nbdata_input

vectps_input

parameter vectps_input

fisher_input

parameter fisher_input

nok_input

parameter nok_input

protdep_input

parameter protdep_input

freqdep_input

parameter freqdep_input

Value

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


Class "FedorovWynnAlgorithm"

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.


Class "Fim"

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.


Compute the fisher.simplex

Description

Compute the fisher.simplex

Usage

fisher.simplex(simplex, optimizationObject, outcomes)

Arguments

simplex

A list giving the parameters of the simplex.

optimizationObject

An object from the class Optimization.

outcomes

A vector giving the outcomes of the arms.

Value

A list giving the results of the optimization.


function fun.amoeba

Description

function fun.amoeba

Usage

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

Arguments

p

input is a matrix p whose ndim+1 rows are ndim-dimensional vectors which are the vertices of the starting simplex.

y

vector whose components must be pre-initialized to the values of funk evaluated at the ndim+1 vertices (rows) of p.

ftol

the fractional convergence tolerance to be achieved in the function value.

itmax

maximal number of iterations.

funk

multidimensional function to be optimized.

outcomes

A vector giving the outcomes.

data

a fixed set of data.

showProcess

A boolean for showing the process or not.

Value

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


Generate the fim from the constraints

Description

Generate the fim from the constraints

Usage

generateFimsFromConstraints(object, fims)

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

Arguments

object

An object from the class Optimization.

fims

A list of object from the class Fim.

Value

A list giving the arms with their fims.


Generate the report for the evaluation

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

object

An object from the class Fim.

evaluationObject

A list giving the results of the evaluation of the model.

outputPath

A string giving the output path.

outputFile

A string giving the name of the output file.

plotOptions

A list giving the plot options.

Value

Return the report for the evaluation in html.


Generate report for the optimization.

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

object

An object from the class OptimizationAlgorithm.

optimizationObject

An object from the class Optimization.

outputPath

A string giving the output path.

outputFile

A string giving the name of the output file.

plotOptions

A list giving the plot options.

Value

The report for the optimization in html.


Generate samplings from sampling constraints

Description

Generate samplings from sampling constraints

Usage

generateSamplingsFromSamplingConstraints(object)

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

Arguments

object

An object from the class SamplingTimeConstraints.

Value

A list of sampling times generated from the sampling constraints.


Generate the tables for the report.

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

object

An object from the class PFIMProject.

plotOptions

A list giving the plot options.

Value

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


getAdjustedGradient

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

object

An object distribution from the class Distribution.

outcomesGradient

A list containing the evaluation of the outcome gradients.

Value

A list giving the adjusted gradient.


getAdministration

Description

Get the administrations by outcome.

Usage

getAdministration(object, outcome)

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

Arguments

object

An object Arm from the class Arm.

outcome

A string giving the name of the outcome.

Value

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


getAdministrationConstraint

Description

Get the administration constraints by outcome.

Usage

getAdministrationConstraint(object, outcome)

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

Arguments

object

An object Arm from the class Arm.

outcome

A string giving the name of the outcome.

Value

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


getAdministrations

Description

Get all the administration for an arm.

Usage

getAdministrations(object)

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

Arguments

object

An object Arm from the class Arm.

Value

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


getAdministrationsConstraints

Description

Get the administrations constraints.

Usage

getAdministrationsConstraints(object)

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

Arguments

object

An object Arm from the class Arm.

Value

The list administrationsConstraints.


Get the arms of an object.

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

object

An object defined form a class of PFIM.

Value

A list containing the arms of the object.


Get the parameter c.

Description

Get the parameter c.

Usage

getcError(object)

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

Arguments

object

An object from the class ModelError.

Value

A numeric giving the parameter c.


Get the names of 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.

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

object

An object from the class Fim.

model

An object from the class Model.

Value

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


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

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

object

An object from the class Fim.

model

An object from the class Model.

Value

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


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

Description

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

Usage

getConditionNumberFixedEffects(object)

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

Arguments

object

An object from the class Fim.

Value

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


Get the condition number of the matrix of the variance 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

object

An object from the class Fim..

Value

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


Get content of a library of models.

Description

Get content of a library of models.

Usage

getContent(object)

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

Arguments

object

An object from the class LibraryOfModels.

Value

A list giving the content of the library of models.


Get the correlation matrix.

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

object

An object from the class Fim.

Value

The correlation matrix of the fim.


getDataForArmEvaluation

Description

getDataForArmEvaluation

Usage

getDataForArmEvaluation(object)

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

Arguments

object

An object Arm from the class Arm.

Value

A list containing the data for arm evaluation.


Get the dataframe of the results.

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

object

An object from the class OptimizationAlgorithm.

Value

Return the dataframe of the results.


Get the D criterion of the fim.

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

object

An object from the class Fim.

Value

A numeric giving the D criterion of the fim.


Get the parameter delta

Description

Get the parameter delta

Usage

getDelta(object)

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

Arguments

object

An object from the class MultiplicativeAlgorithm.

Value

A numeric giving the parameter delta.


Get the derivatives of the model error equation.

Description

Get the derivatives of the model error equation.

Usage

getDerivatives(object)

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

Arguments

object

An object from the class ModelError.

Value

The derivatives of the model error equation.


Get the description of a model.

Description

Get the description of a model.

Usage

getDescription(object)

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

Arguments

object

An object from the class Model.

Value

A list giving the description of a model.


Get the designs.

Description

Get the designs.

Usage

getDesigns(object)

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

Arguments

object

An object from the class PFIMProject.

Value

A list giving the designs of the object.


Get the determinant of the fim.

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

object

An object from the class Fim.

Value

A numeric giving the determinant of the fim.


Get the distribution.

Description

Get the distribution.

Usage

getDistribution(object)

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

Arguments

object

An object from the class ModelParameter.

Value

The parameter distribution.


getDose

Description

Get the amount of doses.

Usage

getDose(object)

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

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

Arguments

object

An object Administration from the class Administration.

Value

The numeric amount_dose giving the amount of doses.


Get the eigenvalues of the fim.

Description

Get the eigenvalues of the fim.

Usage

getEigenValues(object)

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

Arguments

object

An object from the class Fim.

Value

A vector giving the eigenvalues of the fim.


Get the elementary protocols.

Description

Get the elementary protocols.

Usage

getElementaryProtocols(object, fims)

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

Arguments

object

An object from the class Optimization.

fims

A list of object from the class Fim.

Value

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


Get the equation of a model error.

Description

Get the equation of a model error.

Usage

getEquation(object)

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

Arguments

object

An object from the class ModelError.

Value

An expression giving the equation of a model error.


Get the equations of a model.

Description

Get the equations of a model.

Usage

getEquations(object)

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

Arguments

object

An object from the class Model.

Value

The list giving the equations of the model.


Get the equations after infusion.

Description

Get the equations after infusion.

Usage

getEquationsAfterInfusion(object)

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

Arguments

object

An object from the class Model.

Value

A list giving the equations after the infusion.


Get the equations during infusion.

Description

Get the equations during infusion.

Usage

getEquationsDuringInfusion(object)

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

Arguments

object

An object from the class Model.

Value

A list giving the equations during the infusion.


Get the results of the evaluation.

Description

Get the results of the evaluation.

Usage

getEvaluationFIMResults(object)

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

Arguments

object

An object from the class Optimization.

Value

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


Get the evaluation results of the initial design.

Description

Get the evaluation results of the initial design.

Usage

getEvaluationInitialDesignResults(object)

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

Arguments

object

An object from the class Optimization.

Value

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


getFim

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

object

An object defined form a class of PFIM.

Value

The FIM of the object.


Get the FIM.

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

object

An object from the class Fim.

Value

A matrix giving the FIM.


Get the matrix of fixed effects.

Description

Get the matrix of fixed effects.

Usage

getFixedEffects(object)

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

Arguments

object

An object from the class Fim.

Value

The matrix of the fixed effects.


Get the fixed effect.

Description

Get the fixed effect.

Usage

getFixedMu(object)

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

Arguments

object

An object from the class ModelParameter.

Value

A boolean giving the fixed mu.


Get the fixed variance.

Description

Get the fixed variance.

Usage

getFixedOmega(object)

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

Arguments

object

An object from the class ModelParameter.

Value

A boolean giving the fixed omega.


Get the fixed parameters.

Description

Get the fixed parameters.

Usage

getFixedParameters(object)

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

Arguments

object

An object from the class Model.

Value

A list giving the fixed parameters of the model.


Get the fixed sampling times.

Description

Get the fixed sampling times.

Usage

getFixedTimes(object)

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

Arguments

object

An object from the class SamplingTimeConstraints.

Value

A vector giving the foxed sampling times.


getInitialConditions

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

object

An object Arm from the class Arm.

Value

The list initialConditions for the object Arm.


Get the iteration with the convergence criteria.

Description

Get the iteration with the convergence criteria.

Usage

getIterationAndCriteria(object)

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

Arguments

object

An object from the class OptimizationAlgorithm.

Value

A dataframe giving the iteration with the convergence criteria.


Get the parameter lambda.

Description

Get the parameter lambda.

Usage

getLambda(object)

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

Arguments

object

An object from the class MultiplicativeAlgorithm.

Value

A numeric giving the parameter lambda.


Get the library of PD models.

Description

Get the library of PD models.

Usage

getLibraryPDModels(object)

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

Arguments

object

An object from the class LibraryOfModels.

Value

A list giving the PD models.


Get the library of PK models.

Description

Get the library of PK models.

Usage

getLibraryPKModels(object)

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

Arguments

object

An object from the class LibraryOfModels.

Value

A list giving the PK models.


Get the minimal sampling times.

Description

Get the minimal sampling times.

Usage

getMinSampling(object)

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

Arguments

object

An object from the class SamplingTimeConstraints.

Value

A numeric giving the minimal sampling times.


Get the model.

Description

Get the model.

Usage

getModel(object)

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

Arguments

object

An object from the class PFIMProject.

Value

The model of the object.


Get the model equations.

Description

Get the model equations.

Usage

getModelEquations(object)

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

Arguments

object

An object from the class PFIMProject.

Value

A list giving the model equations.


Get the model error.

Description

Get the model error.

Usage

getModelError(object)

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

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

Arguments

object

An object defined form a class of PFIM.

Value

The model error of the object.


Get the values of the model error parameters.

Description

Get the values of the model error parameters.

Usage

getModelErrorParametersValues(object)

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

Arguments

object

An object from the class Model.

Value

A list giving the values of the model error parameters.


Get a model from the library of models.

Description

Get a model from the library of models.

Usage

getModelFromLibrary(object)

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

Arguments

object

An object from the class Model.

Value

Return a model from the the library of models.


Get the model parameters.

Description

Get the model parameters.

Usage

getModelParameters(object)

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

Arguments

object

An object from the class PFIMProject.

Value

A list giving the model parameters.


Get the values of the model parameters.

Description

Get the values of the model parameters.

Usage

getModelParametersValues(object)

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

Arguments

object

An object from the class Model.

Value

A list giving the values of the model parameters.


getMu

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

object

An object defined form a class of PFIM.

Value

The object with the updated fixed effect.


getName

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

object

An object defined form a class of PFIM.

Value

A character string name giving the name of the object.


getNames

Description

Get the names of an object.

Usage

getNames(object)

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

Arguments

object

An object defined form a class of PFIM.

Value

A vector giving the names of the object.


getNumberOfArms

Description

Get the number of arms in a design.

Usage

getNumberOfArms(object)

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

Arguments

object

An object Design from the class Design.

Value

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


Get the number of iterations.

Description

Get the number of iterations.

Usage

getNumberOfIterations(object)

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

Arguments

object

An object from the class MultiplicativeAlgorithm.

Value

A numeric giving the number of iterations.


Get the number of parameters.

Description

Get the number of parameters.

Usage

getNumberOfParameters(object)

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

Arguments

object

An object from the class Model.

Value

A numeric giving the number of parameters of the model.


Get the number of sampling times that are optimisable.

Description

Get the number of sampling times that are optimisable.

Usage

getNumberOfsamplingsOptimisable(object)

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

Arguments

object

An object from the class SamplingTimeConstraints.

Value

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


Get the number of sampling times by windows.

Description

Get the number of sampling times by windows.

Usage

getNumberOfTimesByWindows(object)

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

Arguments

object

An object from the class SamplingTimeConstraints.

Value

A vector giving the number of sampling times by windows.


getOdeSolverParameters

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

object

An object defined form a class of PFIM.

Value

The list giving the parameters for the ode solvers.


Get the matrix omega of an object.

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

object

An object defined form a class of PFIM.

Value

The matrix omega of an object.


Get the optimal design.

Description

Get the optimal design.

Usage

getOptimalDesign(object)

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

Arguments

object

An object from the class OptimizationAlgorithm.

Value

The optimal design.


Get the optimal frequencies

Description

Get the optimal frequencies

Usage

getOptimalFrequencies(object)

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

Arguments

object

An object from the class FedorovWynnAlgorithm.

Value

A vector giving the optimal frequencies


Get the optimal weights.

Description

Get the optimal weights.

Usage

getOptimalWeights(object)

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

Arguments

object

An object from the class MultiplicativeAlgorithm.

Value

A vector giving the optimal weights.


Get the optimization results.

Description

Get the optimization results.

Usage

getOptimizationResults(object)

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

Arguments

object

An object from the class Optimization.

Value

An object from the class OptimizationAlgorithm giving the optimization results.


Get the optimization algorithm.

Description

Get the optimization algorithm.

Usage

getOptimizer(object)

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

Arguments

object

An object from the class PFIMProject.

Value

A string giving the name of the optimization algorithm.


Get the optimization parameters.

Description

Get the optimization parameters.

Usage

getOptimizerParameters(object)

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

Arguments

object

An object from the class PFIMProject.

Value

A list giving the optimization parameters.


getOutcome

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

object

An object defined from a class of PFIM.

Value

A string giving the outcome of the object.


Get the outcomes of a model.

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

object

An object from the class Model.

Value

A list giving the outcomes of the model.


getOutcomesEvaluation

Description

Get the results of the evaluation of the outcomes.

Usage

getOutcomesEvaluation(object)

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

Arguments

object

An object Design from the class Design.

Value

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


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

object

An object from the class Model.

Value

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


getOutcomesGradient

Description

Get the results of the evaluation of the outcome gradients.

Usage

getOutcomesGradient(object)

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

Arguments

object

An object Design from the class Design.

Value

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


Get the parameters of an object.

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

object

An object defined form a class of PFIM.

Value

Return the list of the parameters of the object.


Get a PD model.

Description

Get a PD model.

Usage

getPDModel(object, PDModelName)

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

Arguments

object

An object from the class LibraryOfPKPDModels.

PDModelName

A string giving the name of the PD model.

Value

Return a PD model.


Get a PK model.

Description

Get a PK model.

Usage

getPKModel(object, PKModelName)

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

Arguments

object

An object from the class LibraryOfPKPDModels.

PKModelName

A string giving the name of the PK model.

Value

Return a PK model.


Get a PKPD model.

Description

Get a PKPD model.

Usage

getPKPDModel(object, namesModel)

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

Arguments

object

An object from the class LibraryOfPKPDModels.

namesModel

A vector of strings giving the names of the PK and PD models.

Value

Return a PKPD model.


Get the plot options for graphs responses and SI

Description

Get the plot options for graphs responses and SI

Usage

getPlotOptions(plotOptions, outcomesNames)

Arguments

plotOptions

A list giving the plots options.

outcomesNames

A list giving the output names.

Value

The list containing the plot options.


Get the proportion of subjects.

Description

Get the proportion of subjects.

Usage

getProportionsOfSubjects(object)

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

Arguments

object

An object from the class Optimization.

Value

A vector giving the proportion of subjects.


Get the RSE

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

object

An object from the class Fim.

model

An object from the class Model.

Value

A vector giving the RSE.


Get the sampling of an object.

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

object

An object defined form a class of PFIM.

Value

A list of the samplings of the object.


Get the windows for the sampling times.

Description

Get the windows for the sampling times.

Usage

getSamplingsWindows(object)

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

Arguments

object

An object from the class SamplingTimeConstraints.

Value

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


getSamplingTime

Description

Get the sampling times by outcome.

Usage

getSamplingTime(object, outcome)

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

Arguments

object

An object Arm from the class Arm.

outcome

A string giving the name of the outcome.

Value

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


getSamplingTimeConstraint

Description

Get the sampling times constraints by outcome.

Usage

getSamplingTimeConstraint(object, outcome)

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

Arguments

object

An object Arm from the class Arm.

outcome

A string giving the name of the outcome.

Value

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


getSamplingTimes

Description

Get the vectors of sampling times for an arm.

Usage

getSamplingTimes(object)

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

Arguments

object

An object Arm from the class Arm.

Value

The list samplingTimes for the object Arm.


getSamplingTimesConstraints

Description

Get the sampling times constraints.

Usage

getSamplingTimesConstraints(object)

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

Arguments

object

An object Arm from the class Arm.

Value

The list getSamplingTimesConstraints.


Get the SE.

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

object

An object from the class Fim.

Value

A vector giving the SE.


Get the shrinkage.

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

object

An object from the class Fim.

Value

A vector giving the shrinkage of the Bayesian fim.


Get the parameter sigma inter.

Description

Get the parameter sigma inter.

Usage

getSigmaInter(object)

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

Arguments

object

An object from the class ModelError.

Value

A numeric giving the parameter sigma inter.


Get the parameter sigma slope.

Description

Get the parameter sigma slope.

Usage

getSigmaSlope(object)

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

Arguments

object

An object from the class ModelError.

Value

A numeric giving the parameter sigma slope.


getSize

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

object

An object defined form a class of PFIM.

Value

A numeric giving the size of the object.


getTau

Description

Get the frequency tau.

Usage

getTau(object)

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

Arguments

object

An object Administration from the class Administration.

Value

The numeric tau giving the frequency tau.


getTimeDose

Description

Get the times vector when doses are given.

Usage

getTimeDose(object)

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

Arguments

object

An object Administration from the class Administration.

Value

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


Get the infusion duration.

Description

Get the infusion duration.

Usage

getTinf(object)

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

Arguments

object

An object Administration from the class Administration.

Value

The numeric Tinf giving the infusion duration Tinf.


Return the variable of an ode model

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

object

An object from the class Model.

Value

Return the variable of an ode model


Get the matrix of the variance effects.

Description

Get the matrix of the variance effects.

Usage

getVarianceEffects(object)

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

Arguments

object

An object from the class Fim.

Value

The matrix of the variance effects.


Get the parameter weightThreshold

Description

Get the parameter weightThreshold

Usage

getWeightThreshold(object)

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

Arguments

object

An object from the class MultiplicativeAlgorithm.

Value

A numeric giving the WeightThreshold.


Class "Fim"

Description

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


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

outcome

outcome

timeDose

timeDose

dose

dose

Tinf

Tinf

tau

tau

Value

Administration


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

outcome

outcome

doses

doses


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

size

size

administrations

administrations

initialConditions

initialConditions

samplingTimes

samplingTimes

administrationsConstraints

administrationsConstraints

samplingTimesConstraints

samplingTimesConstraints

dataForArmEvaluation

dataForArmEvaluation

Value

Arm


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

outcome

outcome

equation

equation

derivatives

derivatives

sigmaInter

sigmaInter

sigmaSlope

sigmaSlope

cError

cError

Value

Combined1


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

outcome

outcome

equation

equation

derivatives

derivatives

sigmaInter

sigmaInter

sigmaSlope

sigmaSlope

cError

cError

Value

Constant


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

size

size

arms

arms

outcomesEvaluation

outcomesEvaluation

outcomesGradient

outcomesGradient

numberOfArms

numberOfArms

fim

fim

Value

Design


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

parameters

parameters

Value

Distribution


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

model

model

modelEquations

modelEquations

modelParameters

modelParameters

modelError

modelError

outcomes

outcomes

designs

designs

fim

fim

odeSolverParameters

odeSolverParameters

Value

Evaluation


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

elementaryProtocols

elementaryProtocols

numberOfSubjects

numberOfSubjects

proportionsOfSubjects

proportionsOfSubjects

showProcess

showProcess

Value

FedorovWynnAlgorithm


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

fisherMatrix

fisherMatrix

fixedEffects

fixedEffects

varianceEffects

varianceEffects

shrinkage

shrinkage

Value

Fim


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

fisherMatrix

content

fixedEffects

Value

LibraryOfModels


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

Value

LibraryOfPKPDModels


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

...

args

Value

LogNormal


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

description

description

equations

equations

outcomes

outcomes

outcomesForEvaluation

outcomesForEvaluation

parameters

parameters

modelError

modelError

initialConditions

initialConditions

odeSolverParameters

odeSolverParameters

modelFromLibrary

modelFromLibrary

Value

Model


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

description

description

equations

equations

outcomes

outcomes

parameters

parameters

modelError

modelError

Value

ModelAnalytic


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

description

description

equations

equations

outcomes

outcomes

parameters

parameters

modelError

modelError

Value

ModelAnalyticBolus


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

description

description

equations

equations

outcomes

outcomes

parameters

parameters

modelError

modelError

Value

ModelAnalyticBolusSteadyState


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

description

description

equations

equations

outcomes

outcomes

parameters

parameters

modelError

modelError

Value

ModelAnalyticInfusion


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

description

description

equations

equations

outcomes

outcomes

parameters

parameters

modelError

modelError

Value

ModelAnalyticInfusionSteadyState


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

description

description

equations

equations

outcomes

outcomes

parameters

parameters

modelError

modelError

Value

ModelAnalyticSteadyState


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

description

description

equations

equations

outcomes

outcomes

parameters

parameters

modelError

modelError

initialConditions

initialConditions

odeSolverParameters

odeSolverParameters

Value

ModelBolus


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

outcome

outcome

equation

equation

derivatives

derivatives

sigmaInter

sigmaInter

sigmaSlope

sigmaSlope

cError

cError

Value

ModelError


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

description

description

equations

equations

outcomes

outcomes

parameters

parameters

modelError

modelError

initialConditions

initialConditions

odeSolverParameters

odeSolverParameters

Value

ModelInfusion


initialize

Description

initialize

Usage

## S4 method for signature 'ModelParameter'
initialize(.Object, name, distribution, fixedMu, fixedOmega)

Arguments

.Object

.Object

name

name

distribution

distribution

fixedMu

fixedMu

fixedOmega

fixedOmega

Value

ModelParameter


initialize

Description

initialize

Usage

## S4 method for signature 'MultiplicativeAlgorithm'
initialize(
  .Object,
  arms,
  lambda,
  delta,
  numberOfIterations,
  weightThreshold,
  optimalWeights,
  optimalDesign,
  showProcess
)

Arguments

.Object

.Object

arms

arms

lambda

lambda

delta

delta

numberOfIterations

numberOfIterations

weightThreshold

weightThreshold

optimalWeights

optimalWeights

optimalDesign

optimalDesign

showProcess

showProcess

Value

MultiplicativeAlgorithm


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

...

args

Value

Normal


initialize

Description

initialize

Usage

## S4 method for signature 'Optimization'
initialize(
  .Object,
  name,
  model,
  modelEquations,
  modelParameters,
  modelError,
  optimizer,
  optimizerParameters,
  outcomes,
  designs,
  fim,
  odeSolverParameters,
  optimizationResults,
  evaluationFIMResults,
  evaluationInitialDesignResults
)

Arguments

.Object

.Object

name

name

model

model

modelEquations

modelEquations

modelParameters

modelParameters

modelError

modelError

optimizer

optimizer

optimizerParameters

optimizerParameters

outcomes

outcomes

designs

designs

fim

fim

odeSolverParameters

odeSolverParameters

optimizationResults

optimizationResults

evaluationFIMResults

evaluationFIMResults

evaluationInitialDesignResults

evaluationInitialDesignResults

Value

Optimization


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

name

name

parameters

parameters

Value

OptimizationAlgorithm


initialize

Description

initialize

Usage

## S4 method for signature 'PFIMProject'
initialize(.Object, name, description)

Arguments

.Object

.Object

name

name

description

description

Value

PFIMProject


initialize

Description

initialize

Usage

## S4 method for signature 'PGBOAlgorithm'
initialize(
  .Object,
  N,
  muteEffect,
  maxIteration,
  purgeIteration,
  seed,
  showProcess,
  optimalDesign,
  iterationAndCriteria
)

Arguments

.Object

.Object

N

N

muteEffect

muteEffect

maxIteration

maxIteration

purgeIteration

purgeIteration

seed

seed

showProcess

showProcess

optimalDesign

optimalDesign

iterationAndCriteria

iterationAndCriteria

Value

PGBOAlgorithm


initialize

Description

initialize

Usage

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

Arguments

.Object

.Object

outcome

outcome

equation

equation

derivatives

derivatives

sigmaInter

sigmaInter

sigmaSlope

sigmaSlope

cError

cError

Value

Proportional


initialize

Description

initialize

Usage

## S4 method for signature 'PSOAlgorithm'
initialize(
  .Object,
  maxIteration,
  populationSize,
  personalLearningCoefficient,
  globalLearningCoefficient,
  seed,
  showProcess,
  optimalDesign,
  iterationAndCriteria
)

Arguments

.Object

.Object

maxIteration

maxIteration

populationSize

populationSize

personalLearningCoefficient

personalLearningCoefficient

globalLearningCoefficient

globalLearningCoefficient

seed

seed

showProcess

showProcess

optimalDesign

optimalDesign

iterationAndCriteria

iterationAndCriteria

Value

PSOAlgorithm


initialize

Description

initialize

Usage

## S4 method for signature 'SamplingTimeConstraints'
initialize(
  .Object,
  outcome,
  initialSamplings,
  fixedTimes,
  numberOfsamplingsOptimisable,
  samplingsWindows,
  numberOfTimesByWindows,
  minSampling
)

Arguments

.Object

.Object

outcome

outcome

initialSamplings

initialSamplings

fixedTimes

fixedTimes

numberOfsamplingsOptimisable

numberOfsamplingsOptimisable

samplingsWindows

samplingsWindows

numberOfTimesByWindows

numberOfTimesByWindows

minSampling

minSampling

Value

SamplingTimeConstraints


initialize

Description

initialize

Usage

## S4 method for signature 'SamplingTimes'
initialize(.Object, outcome, samplings)

Arguments

.Object

.Object

outcome

outcome

samplings

samplings

Value

SamplingTimes


initialize

Description

initialize

Usage

## S4 method for signature 'SimplexAlgorithm'
initialize(
  .Object,
  pctInitialSimplexBuilding,
  maxIteration,
  tolerance,
  optimalDesigns,
  iterationAndCriteria,
  showProcess
)

Arguments

.Object

.Object

pctInitialSimplexBuilding

pctInitialSimplexBuilding

maxIteration

maxIteration

tolerance

tolerance

optimalDesigns

optimalDesigns

iterationAndCriteria

iterationAndCriteria

showProcess

showProcess

Value

SimplexAlgorithm


Test if the dose is in the equations of the model.

Description

Test if the dose is in the equations of the model.

Usage

isDoseInEquations(object)

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

Arguments

object

An object from the class Model.

Value

Return a Boolean giving if the dose is in the equations of the model.


Test if a mode is analytic.

Description

Test if a mode is analytic.

Usage

isModelAnalytic(object)

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

Arguments

object

An object from the class Model.

Value

Return a Boolean giving if the mode is analytic or not.


Test if a mode is bolus.

Description

Test if a mode is bolus.

Usage

isModelBolus(object, designs)

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

Arguments

object

An object from the class Model.

designs

A list of objects from the class Design.

Value

Return a Boolean giving if the mode is bolus or not.


Test if a mode is infusion

Description

Test if a mode is infusion

Usage

isModelInfusion(object)

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

Arguments

object

An object from the class Model.

Value

Return a Boolean giving if the mode is infusion or not.


Test if a mode is ode.

Description

Test if a mode is ode.

Usage

isModelODE(object)

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

Arguments

object

An object from the class Model.

Value

Return a Boolean giving if the mode is ode or not.


Test if a mode is steady state.

Description

Test if a mode is steady state.

Usage

isModelSteadyState(object)

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

Arguments

object

An object from the class Model.

Value

Return a Boolean giving if the mode is steady state or not.


Class "LibraryOfModels"

Description

The class LibraryOfModels represents the library of models.

Objects from the class

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

Slots for LibraryOfModels objects

name:

A string giving the name of the library of models.

content:

A list giving the content of the library of model.


Library of the PK models

Description

Library of the PK models

Usage

LibraryOfPDModels()

Library of the PK models

Description

Library of the PK models

Usage

LibraryOfPKModels()

Class "LibraryOfPKPDModels"

Description

The class LibraryOfPKPDModels represents the library of PKPD models. The class LibraryOfPKPDModels inherits from the class LibraryOfModels.


Class "LogNormal"

Description

The class defines all the required methods for a LogNormal distribution object. The class LogNormal inherits from the class Distribution.


Class "Model"

Description

The class Model defines information concerning the construction of a model.

Objects from the class

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

Slots for Administration objects

name:

A string giving the name of the model.

description:

A list of string giving the description of the model.

equations:

A list giving the equations of the model.

outcomes:

A list giving the outcomes of the model.

outcomesForEvaluation:

A list giving the outcomes used for the evaluation of the model.

parameters:

A list giving the parameters of the model.

modelError:

A list giving the model error of the model.

initialConditions:

A list giving the initial conditions of the model.

odeSolverParameters:

A list giving the parameters for the solver of the model.

modelFromLibrary:

A list giving the model equations when the model is constructed from the library of model.


Class "ModelAnalytic"

Description

The class Model defines information concerning the construction of an analytical model. The class ModelAnalytic inherits from the class Model.


Class "ModelAnalyticBolus"

Description

The class Model defines information concerning the construction of an analytical bolus model. The class ModelAnalyticBolus inherits from the class ModelAnalytic.


Class "ModelAnalyticBolusSteadyState"

Description

The class Model defines information concerning the construction of an analytical model in steady state. The class ModelAnalyticBolusSteadyState inherits from the class ModelAnalyticSteadyState.


Class "ModelAnalyticInfusion"

Description

The class Model defines information concerning the construction of an analytical model in infusion. The class ModelAnalyticInfusion inherits from the class ModelInfusion.


Class "ModelAnalyticInfusionSteadyState"

Description

The class Model defines information concerning the construction of an analytical model in infusion in steady state. The class ModelAnalyticInfusionSteadyState inherits from the class ModelAnalyticInfusion.


Class "ModelAnalyticSteadyState"

Description

The class ModelAnalyticSteadyState defines information concerning the construction of an analytical model steady state. The class ModelAnalyticSteadyState inherits from the class ModelAnalytic.


Class "ModelBolus"

Description

...


Class "ModelInfusion"

Description

...


Class "ModelODE"

Description

The class ModelODE defines information concerning the construction of an ode model. The class ModelODE inherits from the class Model.


Class "ModelODEDoseInEquations"

Description

The class ModelODEDoseInEquations defines information concerning the construction of an ode model where the dose is in the model equations. The class ModelODEDoseInEquations inherits from the class ModelODE.


Class "ModelODEInfusion"

Description

The class ModelODEInfusion defines information concerning the construction of an ode model in infusion. The class ModelODEInfusion inherits from the class ModelInfusion.


Class "ModelODEInfusionDoseInEquations"

Description

The class ModelODEInfusionDoseInEquations defines information concerning the construction of an ode model in infusion where the dose is in the model equations. The class ModelODEInfusionDoseInEquations inherits from the class ModelODEInfusion.


Class "ModelParameter"

Description

The class ModelParameter defines information concerning the model parameters.

Objects from the class

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

Slots for ModelParameter objects

name:

A string giving the name of the parameter.

distribution:

An object from the class Distribution giving the distribution of the parameter.

fixedMu:

A boolean giving if mu is fixed or not.

fixedOmega:

A boolean giving if omega is fixed or not.


Function MultiplicativeAlgorithm_Rcpp

Description

Run the MultiplicativeAlgorithm_Rcpp in Rcpp

Usage

MultiplicativeAlgorithm_Rcpp(
  fisherMatrices_input,
  numberOfFisherMatrices_input,
  weights_input,
  numberOfParameters_input,
  dim_input,
  lambda_input,
  delta_input,
  iterationInit_input
)

Arguments

fisherMatrices_input

fisherMatrices_input

numberOfFisherMatrices_input

numberOfFisherMatrices_input

weights_input

weights_input

numberOfParameters_input

numberOfParameters_input

dim_input

dim_input

lambda_input

lambda_input

delta_input

delta_input

iterationInit_input

iterationInit_input


Class "MultiplicativeAlgorithm"

Description

The class MultiplicativeAlgorithm implements the multiplicative algorithm.

Objects from the class

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

Slots for MultiplicativeAlgorithm objects

arms:

A list giving the arms.

lambda:

A numeric giving the lambda parameter of the multiplicative algorithm.

delta:

A numeric giving the delta parameter of the multiplicative algorithm.

numberOfIterations:

A numeric giving the maximal number iteration of the optimization process.

weightThreshold:

A numeric giving the threshold of the weights.

optimalWeights:

A vector giving the optimal weights.

optimalDesign:

An object of the class Design giving the optimal design.

showProcess:

A boolean for showing or not the process of optimization.


Class "Normal"

Description

The class defines all the required methods for a Normal distribution object. The class Normal inherits from the class Distribution.


Class "Optimization"

Description

A class storing information concerning the design optimization.

Objects from the class

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

Slots for Administration objects

name:

A character string giving the name of the optimization process.

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.

optimizer:

A object of class OptimizationAlgorithm giving the optimization algorithm.

optimizerParameters:

A list giving the parameters of the optimization algorithm.

outcomes:

A list giving the outcomes of the model.

designs:

A list giving the designs to be optimized.

fim:

A object of class FIM giving the Fisher information matrix.

odeSolverParameters:

A list giving the parameters for the ode solver.

optimizationResults:

A object of class OptimizationAlgorithm giving the results of the optimization.

evaluationFIMResults:

A object of class Evaluation giving the results of the evaluation of the optimal design.

evaluationInitialDesignResults:

A object of class Evaluation giving the results of the evaluation of the initial design.


Class "OptimizationAlgorithm"

Description

A class storing information concerning the optimization algorithm.

Objects from the class

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

Slots for Administration objects

name:

A character string giving the name of the optimization algorithm.

parameters:

A list giving the parameters of the optimization algorithm.


Optimize a design.

Description

Optimize a design.

Usage

optimize(object, optimizerParameters, optimizationObject)

## S4 method for signature 'FedorovWynnAlgorithm'
optimize(object, optimizerParameters, optimizationObject)

## S4 method for signature 'MultiplicativeAlgorithm'
optimize(object, optimizerParameters, optimizationObject)

## S4 method for signature 'PGBOAlgorithm'
optimize(object, optimizationObject)

## S4 method for signature 'PSOAlgorithm'
optimize(object, optimizationObject)

## S4 method for signature 'SimplexAlgorithm'
optimize(object, optimizerParameters, optimizationObject)

Arguments

object

An object from the class OptimizationAlgorithm.

optimizerParameters

A list giving the optimization parameters.

optimizationObject

An object giving the optimization algorithm.

Value

A list giving the results if the optimization.


Define the parameters for computing the gradients of a model.

Description

Define the parameters for computing the gradients of a model.

Usage

parametersForComputingGradient(object, valuePars)

## S4 method for signature 'Model'
parametersForComputingGradient(object, valuePars)

Arguments

object

An object from the class Model.

valuePars

Vector of parameter values

Value

A list giving the parameters for computing the gradients of a model.


Class "PFIMProject"

Description

A class storing information concerning a PFIM project.

Objects from the class

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

Slots for PFIMProject objects

name:

A character string giving the name of the PFIM project.

description:

A list giving the description of the PFIM project.


Class "PGBOAlgorithm"

Description

The class "PGBOAlgorithm" implements the PGBO algorithm: Population Genetics Based Optimizer, developed by Hervé Le Nagard [1].

Objects from the Class PGBOAlgorithm

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

Slots for PGBOAlgorithm objects

N:

A numeric giving the population size.

muteEffect:

A numeric giving the mutation effect.

maxIteration:

A numeric giving the maximum number of iterations.

seed:

A numeric giving the seed.

showProcess:

A boolean to show or not the process.

optimalDesign:

A Design object giving the optimal design.

iterationAndCriteria:

A list giving the optimal criteria at each iteration.

References

[1] Rebecca Bauer, France Mentré, Halima Kaddouri, Jacques Le Bras, Hervé Le Nagard, Benefits of a new Metropolis-Hasting based algorithm, in non-linear regression for estimation of ex vivo antimalarial sensitivity in patients infected with two strains, Computers in Biology and Medicine, Volume 55, 2014, Pages 16-25, ISSN 0010-4825


Graphs of the results of the evaluation.

Description

Graphs of the results of the evaluation.

Usage

plotEvaluation(object, plotOptions)

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

Arguments

object

An object from the class Evaluation.

plotOptions

A list giving the plot options.

Value

A list giving the graphs for the evaluation of the responses and sensitivity indices.


Class "PlotEvaluation"

Description

A class storing information concerning the design evaluation. The class PlotEvaluation inherits from the class Evaluation.


Graph of the frequencies for the FW algorithm.

Description

Graph of the frequencies for the FW algorithm.

Usage

plotFrequencies(object)

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

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

Arguments

object

An object from the class OptimizationAlgorithm.

Value

The graphs of the frequencies for the FW algorithm.


plotOutcomesEvaluation

Description

Plot the evaluation of the outcomes.

Usage

plotOutcomesEvaluation(
  object,
  outcomesEvaluationInitialDesign,
  model,
  plotOptions
)

## S4 method for signature 'Design'
plotOutcomesEvaluation(
  object,
  outcomesEvaluationInitialDesign,
  model,
  plotOptions
)

Arguments

object

An object Design from the class Design.

outcomesEvaluationInitialDesign

A list containing the evaluation of the initial design.

model

An object model from the class Model.

plotOptions

A list containing the plot options.

Value

A list containing the plots the evaluation of the outcomes.


plotOutcomesGradient

Description

Plot the evaluation of the outcome gradients.

Usage

plotOutcomesGradient(object, outcomesGradientInitialDesign, model, plotOptions)

## S4 method for signature 'Design'
plotOutcomesGradient(object, outcomesGradientInitialDesign, model, plotOptions)

Arguments

object

An object design from the class Design.

outcomesGradientInitialDesign

A list with the evaluation of the gradient for the initial design.

model

An object model from the class Model.

plotOptions

A list containing the plot options.

Value

A list containing the plots the evaluation of the outcome gradients..


Graph of the RSE.

Description

Graph of the RSE.

Usage

plotRSE(object, plotOptions)

## S4 method for signature 'PFIMProject'
plotRSE(object, plotOptions)

Arguments

object

An object from the class Evaluation.

plotOptions

A list giving the plot options.

Value

A graph of the RSE.


Graph the SE.

Description

Graph the SE.

Usage

plotSE(object, plotOptions)

## S4 method for signature 'PFIMProject'
plotSE(object, plotOptions)

Arguments

object

An object from the class Evaluation.

plotOptions

A list giving the plot options.

Value

A graph of the SE.


Graphs of the results of the evaluation.

Description

Graphs of the results of the evaluation.

Usage

plotSensitivityIndice(object, plotOptions)

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

Arguments

object

An object from the class Evaluation.

plotOptions

A list giving the plot options.

Value

A list giving the graphs for the evaluation of the responses and sensitivity indices.


Graph of the shrinkage.

Description

Graph of the shrinkage.

Usage

plotShrinkage(object, plotOptions)

## S4 method for signature 'PFIMProject'
plotShrinkage(object, plotOptions)

Arguments

object

An object from the class Evaluation.

plotOptions

A list giving the plot options.

Value

A graph of the shrinkage.


Graph of the weights for the multiplicative algorithm.

Description

Graph of the weights for the multiplicative algorithm.

Usage

plotWeights(object)

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

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

Arguments

object

An object from the class OptimizationAlgorithm.

Value

The graphs of the weights for the multiplicative algorithm.


Class "PopulationFim"

Description

A class storing information regarding the population Fisher matrix. The class PopulationFim inherits from the class Fim.


Class "Proportional"

Description

The Class "Proportional" defines the the residual error variance according to the formula g(sigma_inter, sigma_slope, c_error, f(x, theta)) = sigma_slope*f(x,theta).

Objects from the Class Proportional

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

Slots for the Proportional objects

.Object:

An object of the Class Proportional

sigma_inter:

A numeric value giving the sigma inter of the error model

sigma_slope:

A numeric value giving the sigma slope of the error model


Class "PSOAlgorithm"

Description

The class "PSOAlgorithm" implements the PSO algorithm.

Objects from the class PSOAlgorithm

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

Slots for PSOAlgorithm objects

maxIteration:

A numeric giving the maximum of iterations.

populationSize:

A numeric giving the population size.

seed:

A numeric giving the seed.

personalLearningCoefficient:

A numeric giving the personal learning coefficient.

globalLearningCoefficient:

A numeric giving the global learning coefficient.

showProcess:

A boolean to show or not the process.

optimalDesign:

A Design object giving the optimal design.

iterationAndCriteria:

A list giving the optimal criteria at each iteration.


Report

Description

Report

Usage

Report(object, outputPath, outputFile, plotOptions)

## S4 method for signature 'Evaluation'
Report(object, outputPath, outputFile, plotOptions)

## S4 method for signature 'Optimization'
Report(object, outputPath, outputFile, plotOptions)

Arguments

object

An object from the class PFIMProject.

outputPath

A string giving the output path.

outputFile

A string giving the name of the output file.

plotOptions

A list giving the plot options.

Value

The report in html.


reportTablesAdministration

Description

Generate table for the report.

Usage

reportTablesAdministration(object)

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

Arguments

object

An object design from the class Design.

Value

A table of the administration parameters for the report.


reportTablesDesign

Description

Generate table for the report.

Usage

reportTablesDesign(object)

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

Arguments

object

An object design from the class Design.

Value

A table of the design parameters for the report.


Generate the tables for the report.

Description

Generate the tables for the report.

Usage

reportTablesFIM(object, evaluationObject)

## S4 method for signature 'BayesianFim'
reportTablesFIM(object, evaluationObject)

## S4 method for signature 'IndividualFim'
reportTablesFIM(object, evaluationObject)

## S4 method for signature 'PopulationFim'
reportTablesFIM(object, evaluationObject)

Arguments

object

An object from the class Fim.

evaluationObject

A list giving the results of the evaluation of the model.

Value

A list giving the table in kable format for the report.


Generate the tables for model errors for the evaluation report.

Description

Generate the tables for model errors for the evaluation report.

Usage

reportTablesModelError(object)

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

Arguments

object

An object from the class Model.

Value

A kable table for the evaluation report.


Generate the tables for model parameters for the evaluation report.

Description

Generate the tables for model parameters for the evaluation report.

Usage

reportTablesModelParameters(object)

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

Arguments

object

An object from the class Model.

Value

A kable table for the evaluation report.


reportTablesPlot

Description

Generate all the table for the evaluation report

Usage

reportTablesPlot(object, plotOptions)

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

Arguments

object

An object evaluation from the class Evaluation.

plotOptions

A list containing the options for the plots.

Value

The list tables containing the tables for the evaluation report.


reportTablesSamplingConstraints

Description

Generate table for the report.

Usage

reportTablesSamplingConstraints(object)

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

Arguments

object

An object design from the class Design.

Value

A table of the sampling constraints parameters for the report.


Resize the fisher Matrix from a vector to a matrix.

Description

Resize the fisher Matrix from a vector to a matrix.

Usage

resizeFisherMatrix(nbOfDimensions, fisherMatrix)

## S4 method for signature 'ANY'
resizeFisherMatrix(nbOfDimensions, fisherMatrix)

Arguments

nbOfDimensions

: a numeric for the dimensions of the fisher matrix.

fisherMatrix

: a vector that contain the low triangular Fisher matrix + its main diagonal.

Value

The Fisher matrix of size nbOfDimensions*nbOfDimensions


run

Description

run

Usage

run(object)

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

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

Arguments

object

An object from the class PFIMProject.

Value

A list giving the results of evaluation or optimization.


Class "SamplingTimeConstraints"

Description

The class "SamplingTimeConstraints" implements the constraints for the sampling times.

Objects from the class SamplingTimeConstraints

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

Slots for SamplingTimeConstraints objects

outcome:

A string giving the outcome.

initialSamplings:

A vector giving the sampling times.

fixedTimes:

A vector giving the fixed sampling times.

numberOfsamplingsOptimisable:

A vector giving the sampling times to be optimized.

samplingsWindows:

A list giving the windows for the sampling times.

numberOfTimesByWindows:

A vector giving the number of sampling times by windows.

minSampling:

A numeric giving the minimal sampling times.


Class "SamplingTimes"

Description

The class "SamplingTimes" implements the sampling times.

Objects from the class SamplingTimes

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

Slots for SamplingTimes objects

outcome:

A string giving the outcome.

samplings:

A vector giving the sampling times.


setAdministrations

Description

Set all the administration for an arm.

Usage

setAdministrations(object, administrations)

## S4 method for signature 'Arm'
setAdministrations(object, administrations)

Arguments

object

An object Arm from the class Arm.

administrations

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

Value

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


setArm

Description

Set the arms in a design.

Usage

setArm(object, arm)

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

Arguments

object

An object Design from the class Design.

arm

A list of object Arm giving the arms of the design.

Value

An object Design with the list Arm updated.


Set the arms of an object.

Description

Set the arms of an object.

Usage

setArms(object, arms)

## S4 method for signature 'Design'
setArms(object, arms)

## S4 method for signature 'OptimizationAlgorithm'
setArms(object, arms)

Arguments

object

An object defined form a class of PFIM.

arms

A list of arms.

Value

The object with the updated arms.


Set the parameter c.

Description

Set the parameter c.

Usage

setcError(object, cError)

## S4 method for signature 'ModelError'
setcError(object, cError)

Arguments

object

An object from the class ModelError.

cError

A numeric giving the parameter c.

Value

The model error with the parameter c.


Set content of a library of models.

Description

Set content of a library of models.

Usage

setContent(object, content)

## S4 method for signature 'LibraryOfModels'
setContent(object, content)

Arguments

object

An object from the class LibraryOfModels.

content

A list giving the content of the library of models.

Value

The library of models with the updated content.


setDataForArmEvaluation

Description

setDataForArmEvaluation

Usage

setDataForArmEvaluation(object, data)

## S4 method for signature 'Arm'
setDataForArmEvaluation(object, data)

Arguments

object

An object Arm from the class Arm.

data

A list containing the data for arm evaluation

Value

Set the list containing the data for arm evaluation.


Generate the table of dose, time dose etc. for model evaluation

Description

Generate the table of dose, time dose etc. for model evaluation

Usage

setDataForModelEvaluation(object, arm)

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

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

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

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

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

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

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

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

Arguments

object

An object from the class Model.

arm

An object from the class Arm.

Value

Return a dataframe with all the data for model evaluation


Set the derivatives of the model error equation.

Description

Set the derivatives of the model error equation.

Usage

setDerivatives(object, derivatives)

## S4 method for signature 'ModelError'
setDerivatives(object, derivatives)

Arguments

object

An object from the class ModelError.

derivatives

The derivatives of the model error equation.

Value

The model error with the updated model error equation.


Set the description of a model.

Description

Set the description of a model.

Usage

setDescription(object, description)

## S4 method for signature 'Model'
setDescription(object, description)

Arguments

object

An object from the class Model.

description

A list giving the description of a model.

Value

The model with the updated description.


Set the designs.

Description

Set the designs.

Usage

setDesigns(object, designs)

## S4 method for signature 'Optimization'
setDesigns(object, designs)

Arguments

object

An object from the class Optimization.

designs

A list of objects from the class Design.

Value

The object with the new designs.


Set the distribution.

Description

Set the distribution.

Usage

setDistribution(object, distribution)

## S4 method for signature 'ModelParameter'
setDistribution(object, distribution)

Arguments

object

An object from the class ModelParameter.

distribution

An object from the class Distribution.

Value

The model parameter with the updated distribution.


Set the amount of dose

Description

Set the amount of dose

Usage

setDose(object, dose)

## S4 method for signature 'Administration'
setDose(object, dose)

Arguments

object

An object Administration from the class Administration.

dose

A numeric value of the amount of dose.

Value

The numeric amount_dose giving the new value of the amount of dose.


Set the equation of a model error.

Description

Set the equation of a model error.

Usage

setEquation(object, equation)

## S4 method for signature 'ModelError'
setEquation(object, equation)

Arguments

object

An object from the class ModelError.

equation

An expression giving the equation of a model error.

Value

The model error with the updated equation.


Set the equations of a model.

Description

Set the equations of a model.

Usage

setEquations(object, equations)

## S4 method for signature 'Model'
setEquations(object, equations)

Arguments

object

An object from the class Model.

equations

A list giving the equations of the model.

Value

The model with the updated equations.


Set the equations after infusion.

Description

Set the equations after infusion.

Usage

setEquationsAfterInfusion(object, equations)

## S4 method for signature 'Model'
setEquationsAfterInfusion(object, equations)

Arguments

object

An object from the class Model.

equations

A list giving the equations after the infusion.

Value

The model with the updated equations after the infusion.


Set the equations during infusion.

Description

Set the equations during infusion.

Usage

setEquationsDuringInfusion(object, equations)

## S4 method for signature 'Model'
setEquationsDuringInfusion(object, equations)

Arguments

object

An object from the class Model.

equations

A list giving the equations during the infusion.

Value

The model with the updated equations during the infusion.


Set the evaluation results.

Description

Set the evaluation results.

Usage

setEvaluationFIMResults(object, value)

## S4 method for signature 'Optimization'
setEvaluationFIMResults(object, value)

Arguments

object

An object from the class Optimization.

value

An object from the class Evaluation giving the evaluation results.

Value

The object with the updated object from the class Evaluation.


Set the evaluation results of the initial design.

Description

Set the evaluation results of the initial design.

Usage

setEvaluationInitialDesignResults(object, value)

## S4 method for signature 'Optimization'
setEvaluationInitialDesignResults(object, value)

Arguments

object

An object from the class Optimization.

value

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

Value

The object with the updated object from the class Evaluation.


setFim

Description

Set the fim of the design.

Usage

setFim(object, fim)

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

Arguments

object

An object Design from the class Design.

fim

An object fim from the class Fim.

Value

An object Design with the fim updated.


Convert the type of the object fim to a string.

Description

Convert the type of the object fim to a string.

Usage

setFimTypeToString(object)

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

Arguments

object

An object from the class Fim.

Value

The type of the object fim convert as a string.


Set the FIM.

Description

Set the FIM.

Usage

setFisherMatrix(object, value)

## S4 method for signature 'Fim'
setFisherMatrix(object, value)

Arguments

object

An object from the class Fim.

value

A matrix giving the FIM.

Value

The object from the class Fim with the FIM updated.


Set the fixed effects.

Description

Set the fixed effects.

Usage

setFixedEffects(object)

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

Arguments

object

An object from the class Fim.

Value

Update the matrix of the fixed effects.


Set the mu as fixed or not.

Description

Set the mu as fixed or not.

Usage

setFixedMu(object, value)

## S4 method for signature 'ModelParameter'
setFixedMu(object, value)

Arguments

object

An object from the class ModelParameter.

value

A Boolean if fixed or not.

Value

The mode parameter with the the mu updated as fixed or not.


Set the omega as fixed of not.

Description

Set the omega as fixed of not.

Usage

setFixedOmega(object, value)

## S4 method for signature 'ModelParameter'
setFixedOmega(object, value)

Arguments

object

An object from the class ModelParameter.

value

A Boolean fixed or not.

Value

The model parameter with the omega updated as fixed or not.


setInitialConditions

Description

Set the initial conditions of a ode model.

Usage

setInitialConditions(object, initialConditions)

## S4 method for signature 'Arm'
setInitialConditions(object, initialConditions)

## S4 method for signature 'Model'
setInitialConditions(object, initialConditions)

Arguments

object

An object from the class Model.

initialConditions

A list giving the initial conditions.

Value

The model with the updated initial conditions.


Set the iteration with the convergence criteria.

Description

Set the iteration with the convergence criteria.

Usage

setIterationAndCriteria(object, value)

## S4 method for signature 'OptimizationAlgorithm'
setIterationAndCriteria(object, value)

Arguments

object

An object from the class OptimizationAlgorithm.

value

A dataframe giving the iteration with the convergence criteria.

Value

A dataframe giving the iteration with the convergence criteria.


Set the model.

Description

Set the model.

Usage

setModel(object, model)

## S4 method for signature 'PFIMProject'
setModel(object, model)

Arguments

object

An object from the class PFIMProject.

model

An object from the class Model.

Value

The object with the updated model.


Set the model error.

Description

Set the model error.

Usage

setModelError(object, modelError)

## S4 method for signature 'Model'
setModelError(object, modelError)

Arguments

object

An object from the class Model.

modelError

An object from the class ModelError.

Value

The model with the updated model error.


Set a model from the library of model

Description

Set a model from the library of model

Usage

setModelFromLibrary(object, modelFromLibrary)

## S4 method for signature 'Model'
setModelFromLibrary(object, modelFromLibrary)

Arguments

object

An object from the class Model.

modelFromLibrary

An object from the class Model.

Value

The model with the updated model from library of models.


Set the value of the fixed effect mu of an object.

Description

Set the value of the fixed effect mu of an object.

Usage

setMu(object, value)

## S4 method for signature 'Distribution'
setMu(object, value)

## S4 method for signature 'ModelParameter'
setMu(object, value)

Arguments

object

An object defined form a class of PFIM.

value

The value of the fixed effect mu.

Value

The object with the updated fixed effect mu.


Set the name of an object.

Description

Set the name of an object.

Usage

setName(object, name)

## S4 method for signature 'Arm'
setName(object, name)

## S4 method for signature 'Design'
setName(object, name)

## S4 method for signature 'Model'
setName(object, name)

Arguments

object

An object defined form a class of PFIM.

name

A string giving the name of the object.

Value

The object with the updated name.


setNumberOfArms

Description

Set the number of arms in a design.

Usage

setNumberOfArms(object, numberOfArms)

## S4 method for signature 'Design'
setNumberOfArms(object, numberOfArms)

Arguments

object

An object Design from the class Design.

numberOfArms

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

Value

An object Design with the numberOfArms updated.


Set the parameters of the ode solver.

Description

Set the parameters of the ode solver.

Usage

setOdeSolverParameters(object, odeSolverParameters)

## S4 method for signature 'Model'
setOdeSolverParameters(object, odeSolverParameters)

Arguments

object

An object from the class Model.

odeSolverParameters

A list giving the parameters of the ode solver.

Value

The model with the updated parameters of the ode solver.


Set the matrix omega of an object.

Description

Set the matrix omega of an object.

Usage

setOmega(object, value)

## S4 method for signature 'Distribution'
setOmega(object, value)

## S4 method for signature 'ModelParameter'
setOmega(object, value)

Arguments

object

An object defined form a class of PFIM.

value

The matrix omega.

Value

The object with the updated matrix omega.


Set the optimal design.

Description

Set the optimal design.

Usage

setOptimalDesign(object, optimalDesign)

## S4 method for signature 'OptimizationAlgorithm'
setOptimalDesign(object, optimalDesign)

Arguments

object

An object from the class OptimizationAlgorithm.

optimalDesign

An object from the class Design.

Value

The object with the updated optimal design.


Set the optimal weights.

Description

Set the optimal weights.

Usage

setOptimalWeights(object, optimalWeights)

## S4 method for signature 'MultiplicativeAlgorithm'
setOptimalWeights(object, optimalWeights)

Arguments

object

An object from the class MultiplicativeAlgorithm.

optimalWeights

A vector giving the optimal weights.

Value

The object with the updated optimal weights.


Set the optimization results.

Description

Set the optimization results.

Usage

setOptimizationResults(object, value)

## S4 method for signature 'Optimization'
setOptimizationResults(object, value)

Arguments

object

An object from the class Optimization.

value

An object from the class OptimizationAlgorithm giving the optimization results.

Value

The object with the updated object from the class OptimizationAlgorithm.


setOutcome

Description

Set the outcome of an object.

Usage

setOutcome(object, outcome)

## S4 method for signature 'Administration'
setOutcome(object, outcome)

## S4 method for signature 'SamplingTimes'
setOutcome(object, outcome)

Arguments

object

An object defined form a class of PFIM.

outcome

A string defined the outcome.

Value

A string giving the updated outcome of the object.


Set the outcomes of a model.

Description

Set the outcomes of a model.

Usage

setOutcomes(object, outcomes)

## S4 method for signature 'Model'
setOutcomes(object, outcomes)

Arguments

object

An object from the class Model.

outcomes

A list giving the outcomes of the model.

Value

The model with the updated outcomes.


setOutcomesEvaluation

Description

Set the results of the evaluation of the outcomes.

Usage

setOutcomesEvaluation(object, outcomesEvaluation)

## S4 method for signature 'Design'
setOutcomesEvaluation(object, outcomesEvaluation)

Arguments

object

An object Design from the class Design.

outcomesEvaluation

A list containing the evaluation of the outcomes.

Value

An object Design with the list outcomesEvaluation updated.


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

Description

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

Usage

setOutcomesForEvaluation(object, outcomes)

## S4 method for signature 'Model'
setOutcomesForEvaluation(object, outcomes)

Arguments

object

An object from the class Model.

outcomes

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

Value

The model with the updated outcomes for the evaluation.


setOutcomesGradient

Description

Set the results of the evaluation of the outcomes.

Usage

setOutcomesGradient(object, outcomesGradient)

## S4 method for signature 'Design'
setOutcomesGradient(object, outcomesGradient)

Arguments

object

An object Design from the class Design.

outcomesGradient

A list containing the evaluation of the outcome gradients.

Value

An object Design with the list outcomesGradient updated.


Set the parameters of an object.

Description

Set the parameters of an object.

Usage

setParameters(object, parameters)

## S4 method for signature 'Distribution'
setParameters(object, parameters)

## S4 method for signature 'Model'
setParameters(object, parameters)

## S4 method for signature 'FedorovWynnAlgorithm'
setParameters(object, parameters)

## S4 method for signature 'MultiplicativeAlgorithm'
setParameters(object, parameters)

## S4 method for signature 'PGBOAlgorithm'
setParameters(object, parameters)

## S4 method for signature 'PSOAlgorithm'
setParameters(object, parameters)

## S4 method for signature 'SimplexAlgorithm'
setParameters(object, parameters)

Arguments

object

An object defined form a class of PFIM.

parameters

A list of parameters.

Value

The object with the updated list of parameters.


setSamplingConstraintForOptimization

Description

Set the sampling times constraint for optimization with PSO, PGBO and Simplex

Usage

setSamplingConstraintForOptimization(object)

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

Arguments

object

An object from the class Design.

Value

The arms with the sampling times constraints.


Set the sampling times.

Description

Set the sampling times.

Usage

setSamplings(object, samplings)

## S4 method for signature 'SamplingTimes'
setSamplings(object, samplings)

Arguments

object

An object from the class SamplingTimes.

samplings

A vector giving the sampling times.

Value

The updated sampling times.


setSamplingTime

Description

Set the sampling time of an arm.

Usage

setSamplingTime(object, samplingTime)

## S4 method for signature 'Arm'
setSamplingTime(object, samplingTime)

Arguments

object

An object Arm from the class Arm.

samplingTime

An object samplingTime from the class SamplingTimes.

Value

An object Arm from the class Arm with the new sampling time samplingTime.


setSamplingTimes

Description

Set the vectors of sampling times for an arm.

Usage

setSamplingTimes(object, samplingTimes)

## S4 method for signature 'Arm'
setSamplingTimes(object, samplingTimes)

Arguments

object

An object Arm from the class Arm.

samplingTimes

The list containing the new sampling times.

Value

An object Arm from the class Arm with the new sampling times samplingTimes.


setSamplingTimesConstraints

Description

Set the sampling times constraints.

Usage

setSamplingTimesConstraints(object, samplingTimesConstraints)

## S4 method for signature 'Arm'
setSamplingTimesConstraints(object, samplingTimesConstraints)

Arguments

object

An object Arm from the class Arm.

samplingTimesConstraints

An object SamplingTimeConstraints from the class SamplingTimeConstraints.

Value

The arm with the new sampling time constraints.


Set the shrinkage.

Description

Set the shrinkage.

Usage

setShrinkage(object, value)

## S4 method for signature 'BayesianFim'
setShrinkage(object, value)

## S4 method for signature 'IndividualFim'
setShrinkage(object, value)

## S4 method for signature 'PopulationFim'
setShrinkage(object, value)

Arguments

object

An object from the class Fim.

value

A vector giving the shrinkage of the Bayesian fim.

Value

The object with the updated shrinkage.


Set the parameter sigma inter.

Description

Set the parameter sigma inter.

Usage

setSigmaInter(object, sigmaInter)

## S4 method for signature 'ModelError'
setSigmaInter(object, sigmaInter)

Arguments

object

An object from the class ModelError.

sigmaInter

A numeric giving the parameter sigma inter.

Value

The model error with the updated sigma inter.


Set the parameter sigma slope.

Description

Set the parameter sigma slope.

Usage

setSigmaSlope(object, sigmaSlope)

## S4 method for signature 'ModelError'
setSigmaSlope(object, sigmaSlope)

Arguments

object

An object from the class ModelError.

sigmaSlope

A numeric giving the parameter sigma slope.

Value

The model error with the updated sigma slope.


setSize

Description

Set the size of an object.

Set the size of an arm.

Usage

setSize(object, size)

setSize(object, size)

## S4 method for signature 'Arm'
setSize(object, size)

## S4 method for signature 'Design'
setSize(object, size)

Arguments

object

An object Arm from the class Arm.

size

A numeric giving the new size of the object Arm.

Value

The object with its size updated.

The object Arm object with its new size.


setTau

Description

Set the frequency tau.

Usage

setTau(object, tau)

## S4 method for signature 'Administration'
setTau(object, tau)

Arguments

object

An object Administration from the class Administration.

tau

A numeric value for the infusion lag tau.

Value

The object Administration object with its new value of the infusion lag tau.


setTimeDose

Description

Set the times vector when doses are given.

Usage

setTimeDose(object, timeDose)

## S4 method for signature 'Administration'
setTimeDose(object, timeDose)

Arguments

object

An object Administration from the class Administration.

timeDose

A numeric value of the time dose.

Value

The object Administration with its new times vector for doses.


Set the infusion duration.

Description

Set the infusion duration.

Usage

setTinf(object, Tinf)

## S4 method for signature 'Administration'
setTinf(object, Tinf)

Arguments

object

An object Administration from the class Administration.

Tinf

A numeric value for the infusion duration Tinf.

Value

The object Administration with its new value of the infusion duration Tinf.


Set the matrix of the variance effects.

Description

Set the matrix of the variance effects.

Usage

setVarianceEffects(object)

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

Arguments

object

An object from the class Fim.

Value

Update the matrix of the variance effects.


show

Description

show

show

show

show

show

show

show

show

Usage

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

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

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

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

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

## S4 method for signature 'PGBOAlgorithm'
show(object)

## S4 method for signature 'PSOAlgorithm'
show(object)

## S4 method for signature 'SimplexAlgorithm'
show(object)

Arguments

object

object


Class "SimplexAlgorithm"

Description

Class "SimplexAlgorithm" implements the Multiplicative algorithm.

Objects from the class SimplexAlgorithm

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

Slots for SamplingTimes objects

pctInitialSimplexBuilding:

A numeric giving the percentage of the initial simplex.

maxIteration:

A numeric giving the number of maximum iteration.

tolerance:

A numeric giving the tolerance threshold.

showProcess:

A boolean to show or not the process.

optimalDesign:

A Design object giving the optimal design.

iterationAndCriteria:

A list giving the optimal criteria at each iteration.