Monolix project for warfarin PK - 1

Description

RsmlxDemo2.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin using a PK model with parameters ka, V, Cl. There is no covariate in the model.

Usage

RsmlxDemo1.project

Format

A text file

Source

Monolix project

References

Rsmlx doumentation

Monolix project for warfarin PK - 2

Description

RsmlxDemo2.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin using a PK model with parameters ka, V, Cl. Here, V and Cl are function of weight.

Usage

RsmlxDemo2.project

Format

A text file

Source

Monolix project

References

Rsmlx doumentation

Bootstrapping - case resampling

Description

Generate replicates of the original data using random sampling with replacement. Population parameters are then estimated from each replicate.

Usage

bootmlx(
  project,
  nboot = 100,
  dataFolder = NULL,
  parametric = FALSE,
  tasks = c(populationParameterEstimation = TRUE),
  settings = NULL
)

Arguments

Details

Bootstrap functionality is now available directly in the lixoftConnectors package using the function runBootstrap. Please migrate, as this function will be deprecated in the future.

Value

a data frame with the bootstrap estimates

See Also

getBootstrapSettings settings for bootstrap with lixoftConnectors
runBootstrap run the bootstrap with lixoftConnectors
getBootstrapResults results for bootstrap with lixoftConnectors

Examples

## Not run: 
# RsmlxDemo1.mlxtran is a Monolix project for modelling the PK of warfarin using a PK model 
# with parameters ka, V, Cl.

# In this example, bootmlx will generate 100 random replicates of the original data and will
# use Monolix to estimate the population parameters from each of these 100 replicates:
r1 <- bootmlx(project="RsmlxDemo1.mlxtran")
  
# 5 replicates will now be generated, with 50 individuals in each replicate:
r2 <- bootmlx(project="RsmlxDemo1.mlxtran",  nboot = 5, settings = list(N = 50))

# Proportions of males and females in the original dataset will be preserved   
# in each replicate:
r3 <- bootmlx(project="RsmlxDemo1.mlxtran",  settings = list(covStrat = "sex"))

## End(Not run)

# See http://monolix.lixoft.com/rsmlx/bootmlx/ for detailed examples of use of bootmlx
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

Automatic complete statistical model building

Description

buildAll builds the complete statistical model by iteratively calling functions buildmlx and buildVar

Penalization criterion can be either a custom penalization of the form gamma*(number of parameters), AIC (gamma=2) or BIC (gamma=log(N)).

Usage

buildAll(
  project = NULL,
  final.project = NULL,
  model = "all",
  prior = NULL,
  weight = NULL,
  coef.w1 = 0.5,
  cv.min = 0.001,
  fError.min = 0.001,
  paramToUse = "all",
  covToTest = "all",
  covToTransform = "none",
  center.covariate = FALSE,
  criterion = "BICc",
  linearization = FALSE,
  ll = T,
  test = T,
  direction = NULL,
  steps = 1000,
  max.iter = 20,
  explor.iter = 2,
  seq.cov = FALSE,
  seq.corr = TRUE,
  seq.cov.iter = 0,
  p.max = 0.1,
  p.min = c(0.075, 0.05, 0.1),
  print = TRUE,
  nb.model = 1,
  fix.param1 = NULL,
  fix.param0 = NULL,
  remove = T,
  add = T,
  delta = c(30, 10, 5),
  omega.set = NULL,
  pop.set1 = NULL,
  pop.set2 = NULL
)

Arguments

Details

See https://monolix.lixoft.com/rsmlx/ for more details.

Value

a new Monolix project with a new statistical model.

Examples

## Not run: 
# Build the complete statistical model using the default settings
r1 <- buildAll(project="warfarinPK_project.mlxtran")

# Force parameter Tlag to be fixed (no variability) and parameter Cl to vary
r2 <- buildAll(project="warfarinPK_project.mlxtran", fix.param0="Tlag", fix.param1="Cl")

# Estimate the log-likelihood by linearization of the model (faster)
r3 <- buildAll(project="warfarinPK_project.mlxtran", linearization=T)


## End(Not run)

# See http://monolix.lixoft.com/rsmlx/buildmlx/ for detailed examples of use of buildmlx
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

Automatic model variance building

Description

buildVar is designed to build the best variance model for the random effects by selecting which individual parameters vary and which ones are fixed.

Usage

buildVar(
  project = NULL,
  final.project = NULL,
  prior = NULL,
  weight = NULL,
  cv.min = 0.001,
  fix.param1 = NULL,
  fix.param0 = NULL,
  criterion = "BICc",
  linearization = F,
  remove = T,
  add = T,
  delta = c(30, 10, 5),
  omega.set = NULL,
  pop.set1 = NULL,
  pop.set2 = NULL,
  print = TRUE
)

Arguments

Details

Penalization criterion can be either a custom penalization of the form gamma*(number of parameters), AIC (gamma=2) or BIC (gamma=log(N)).

See https://monolix.lixoft.com/rsmlx/ for more details.

Value

a new Monolix project with a new inter individual variability model.

Examples

## Not run: 
# Build the variability model using the default settings
r1 <- buildVar(project="warfarinPK_project.mlxtran")

# Force parameter Tlag to be fixed (no variability) and parameter Cl to vary
r2 <- buildVar(project="warfarinPK_project.mlxtran", fix.param0="Tlag", fix.param1="Cl")

# Estimate the log-likelihood by linearization of the model (faster)
r3 <- buildVar(project="warfarinPK_project.mlxtran", linearization=T)


## End(Not run)

# See http://monolix.lixoft.com/rsmlx/buildvar/ for detailed examples of use of buildvar
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

Automatic statistical model building

Description

Automatic statistical model building is available directly in the lixoftConnectors package using the function runModelBuilding.

Usage

buildmlx(
  project = NULL,
  final.project = NULL,
  model = "all",
  prior = NULL,
  weight = NULL,
  coef.w1 = 0.5,
  paramToUse = "all",
  covToTest = "all",
  covToTransform = "none",
  center.covariate = FALSE,
  criterion = "BICc",
  linearization = FALSE,
  ll = T,
  test = T,
  direction = NULL,
  steps = 1000,
  n.full = 10,
  max.iter = 20,
  explor.iter = 2,
  fError.min = 0.001,
  seq.cov = FALSE,
  seq.cov.iter = 0,
  seq.corr = TRUE,
  p.max = 0.1,
  p.min = c(0.075, 0.05, 0.1),
  print = TRUE,
  nb.model = 1
)

Arguments

Details

buildmlx uses SAMBA (Stochastic Approximation for Model Building Algorithm), an iterative procedure to accelerate and optimize the process of model building by identifying at each step how best to improve some of the model components. This method allows to find the optimal statistical model which minimizes some information criterion in very few steps.

Penalization criterion can be either a custom penalization of the form gamma*(number of parameters), AIC (gamma=2) or BIC (gamma=log(N)).

Several strategies can be used for building the covariate model at each iteration of the algorithm: direction="full" means that all the possible models are compared (default when the number of covariates is less than 10). Othrwise, direction is the mode of stepwise search of stepAIC {MASS}, can be one of "both", "backward", or "forward", with a default of "both" when there are at least 10 covariates. See https://monolix.lixoft.com/rsmlx/ for more details.

Value

a new Monolix project with a new statistical model.

See Also

getModelBuildingSettings settings for model building with lixoftConnectors
runModelBuilding run model building with lixoftConnectors
getModelBuildingResults results for model building with lixoftConnectors

Examples

## Not run: 
# RsmlxDemo1.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin 
# using a PK model with parameters ka, V, Cl.

# By default, buildmlx will compute the best statistical model in term of BIC, i.e , 
# the best covariate model, the best correlation model for the three random effects and the best 
# residual error model in terms of BIC. 
# In this example, three covariates (wt, age, sex) are available with the data and will be used 
# for building the covariate model for the three PK parameters:
r1 <- buildmlx(project="RsmlxDemo1.mlxtran")
  
# Here, the covariate model will be built for V and Cl only and log-transformation of all 
# continuous covariates will also be considered:
r2 <- buildmlx(project="RsmlxDemo1.mlxtran", paramToUse=c("V", "Cl"), covToTransform="all") 

# Only the covariate model will be  built, using AIC instead of BIC:
r3 <- buildmlx(project="RsmlxDemo1.mlxtran", model="covariate", criterion="AIC") 

## End(Not run)

# See http://monolix.lixoft.com/rsmlx/buildmlx/ for detailed examples of use of buildmlx
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

Confidence intervals for population parameters

Description

Compute confidence intervals for the population parameters estimated by Monolix.

Usage

confintmlx(
  project,
  parameters = "all",
  method = "fim",
  level = 0.9,
  linearization = TRUE,
  nboot = 100,
  parametric = FALSE,
  settings = NULL
)

Arguments

Details

Most functionality to compute confidence intervals (other than profile likelihood) is now available directly in the lixoftConnectors package. Please migrate the following uses of this function:

For method="proflike", continue using this function.

The method used for computing the confidence intervals can be either based on the standard errors derived from an estimation of the Fisher Information Matrix ("fim"), on the profile likelihood ("proflike") or on nonparametric bootstrap estimate ("bootstrap"). method="fim" is used by default.

When method="fim", the FIM can be either estimated using a linearization of the model or a stochastic approximation. When method="proflike", the observed likelihood can be either estimated using a linearization of the model or an importance sampling Monte Carlo procedure. When method="bootstrap", the bootstrap estimates are obtained using the bootmlx function

Value

a list with the computed confidence intervals, the method used and the level.

See Also

getEstimatedConfidenceIntervals replaces this function for method = "fim" in lixoftConnectors
runBootstrap replaces this function for method = "bootstrap" in lixoftConnectors

Examples

## Not run: 
# RsmlxDemo2.mlxtran is a Monolix project for modelling the PK of warfarin using a PK model 
# with parameters ka, V, Cl.

# confintmlx will compute a 90% confidence interval for all the population parameters 
# using the population estimates obtained by Monolix and the Fisher Information Matrix 
# estimated by linearization
r1 <- confintmlx(project="RsmlxDemo2.mlxtran") 

# 95% confidence intervals are now computed, using the FIM estimated by Monolix using a 
# stochastic approximation algorithm:
r2 <- confintmlx(project="RsmlxDemo2.mlxtran", linearization=FALSE, level=0.95) 

# Confidence intervals are computed for ka_pop and omega_ka only, 
# using the profile likelihood method:
r <- confintmlx(project    = "RsmlxDemo2.mlxtran", 
                method     = "proflike", 
                parameters = c("ka_pop","omega_ka")) 

# Confidence intervals are computed using 200 bootstrap samples:
r3 <- confintmlx(project="RsmlxDemo2.mlxtran", method="bootstrap", nboot=200)

## End(Not run)

# See http://monolix.lixoft.com/rsmlx/confintmlx/ for detailed examples of use of confintmlx
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

Covariate model building

Description

Automatic search of the best covariate model. Automatic covariate model building is available directly in the lixoftConnectors package using the function runModelBuilding. Please migrate, as this function will be deprecated in the future.

Two methods for covariate model building are proposed

• SCM: stepwise covariate modeling method In the forward selection, at each step, each of the remaining (i.e not yet included) parameter-covariate relationships are added to the model in an univariate model (one model per relationship), and run. Among all models, the model that improves some criteria (LRT, BIC or AIC) most is selected and taken forward to the next step. During backward elimination, parameter-covariate relationships are removed in an univariate manner.

• COSSAC: COnditional Sampling for Stepwise Approach based on Correlation tests method COSSAC makes use of the information contained in the base model run to choose which covariate to try first (instead of trying all covariates "blindly" as in SCM). Indeed, the correlation between the individual parameters (or random effects) and the covariates hints at possibly relevant parameter-covariate relationships. If the EBEs (empirical Bayes estimates) are used, shrinkage may bias the result. COSSAC instead uses samples from the a posteriori conditional distribution (available as "conditional distribution" task in MonolixSuite2018) to calculate the correlation between the random effects and covariates. A p-value can be derived using the Pearson's correlation test for continuous covariate and ANOVA for categorical covariate. The p-values are used to sort all the random effect-covariate relationships. Relationships with the lowest p-value are added first, run and confirmed using a likelihood ratio test, AIC or BIC criteria.

Usage

covariateSearch(
  project,
  final.project = NULL,
  method = NULL,
  covToTest = NULL,
  covToTransform = NULL,
  paramToUse = NULL,
  testRelations = NULL,
  settings = NULL
)

Arguments

See Also

getModelBuildingSettings settings for model building with lixoftConnectors
runModelBuilding run model building with lixoftConnectors
getModelBuildingResults results for model building with lixoftConnectors

Examples

## Not run: 
# RsmlxDemo1.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin 
# using a PK model with parameters ka, V, Cl.

# In this example, three covariates (wt, age, sex) are available with the data
# covariatesearch will compute the best covariate model, in term of BIC, 
# for the three PK parameters using the three covariates. 
r1 <- covariateSearch(project="RsmlxDemo1.mlxtran")
  
# Instead of using the COSSAC method, we can use the SCM method:
r2 <- covariateSearch(project="RsmlxDemo1.mlxtran", method = 'SCM')

# Here, the covariate model is built using age and wt only, for V and Cl only:
r3 <- covariateSearch(project    = "RsmlxDemo1.mlxtran", 
                      paramToUse = c("V","Cl"), 
                      covToTest  = c("age","wt"))

## End(Not run)

# See http://monolix.lixoft.com/rsmlx/covariatesearch/ for detailed examples of covariatesearch
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

Get estimated covariance and correlation matrices

Description

Get estimated covariance and correlation matrices

Usage

getEstimatedCovarianceMatrix()

Value

a list of two matrices.

Examples

## Not run: 
# Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded
r = getEstimatedCovarianceMatrix()  # r is a list with elements "cor.matrix" and "cov.matrix"

# See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

## End(Not run)

Get estimated individual and population parameters

Description

Get the individual individual parameters, the population parameters with the population covariates and the population parameters with the individual covariates.

Usage

getEstimatedIndividualParameters2()

Value

a list of data frames.

Examples

## Not run: 
# Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded
r = getEstimatedIndividualParameters2() 

# r is a list with elements "saem", "conditionalMean", "conditionalSD",   "conditionalMode",
# "popPopCov" and "popIndCov"

# See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

## End(Not run)

Get estimated predictions

Description

Get the individual predictions obtained with the estimated individual parameters :

Usage

getEstimatedPredictions()

Value

a list of data frames (one data frame per output).

Examples

## Not run: 
# Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded
r = getEstimatedPredictions() # r is a list with elements "y1" and "y2"

# See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

## End(Not run)

Get estimated residuals

Description

Get the residuals computed from the individual predictions obtained with the estimated individual parameters:

Usage

getEstimatedResiduals()

Value

a list of data frames (one data frame per output).

Examples

## Not run: 
# Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded
r = getEstimatedResiduals()  # r is a list with elements "y1" and "y2" 

# See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

## End(Not run)

Get simulated predictions

Description

Get the individual predictions obtained with the simulated individual parameters :

Usage

getSimulatedPredictions()

Value

a list of data frames (one data frame per output).

Examples

## Not run: 
# Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded
r = getSimulatedPredictions()  # r is a list with elements "Cc" and "E" 

# See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

## End(Not run)

Get simulated residuals

Description

Get the residuals computed from the individual predictions obtained with the simulated individual parameters:

Usage

getSimulatedResiduals()

Value

a list of data frames (one data frame per output).

Examples

## Not run: 
# Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded
r = getSimulatedResiduals()  # r is a list with elements "y1" and "y2" 

# See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

## End(Not run)

Initialize Rsmlx library

Description

Initialize Rsmlx library and lixoftConnectors. Prints information about the versions of Monolix and lixoftConnectors used.

Usage

initRsmlx(path = NULL)

Arguments

Value

A list:

• software: the software that is used (should be monolix with Rsmlx)

• path: the path to MonolixSuite

• version: the version of MonolixSuite that is used

• status: boolean equaling TRUE if the initialization has been successful.

Examples

## Not run: 
initRsmlx() 
initRsmlx(path="C:/ProgramData/Lixoft/MonolixSuite2024R1")  # specifiy a specific path

## End(Not run)

Automatic PK model building

Description

Fit several structural PK models and select the best one based on a Bayesian Information Criterion. Models to compare can be defined by rate constants and/or clearances and can include or not nonlinear elimination models. See https://monolix.lixoft.com/rsmlx/pkbuild/ for more details.

Usage

pkbuild(
  data = NULL,
  project = NULL,
  stat = FALSE,
  param = "clearance",
  new.dir = ".",
  MM = FALSE,
  linearization = F,
  criterion = "BICc",
  level = NULL,
  settings.stat = NULL
)

Arguments

Value

A list of results

Examples

## Not run: 
# Build a PK model for the warfarin PK data. 
# By default, only models using clearance (and inter compartmental clearances) are used
warf.pk1 <- pkbuild(data=warfarin)

# Models using elimination and transfer rate constants are used, 
# as well as nonlinear elimination models
warf.pk2 <- pkbuild(data=warfarin,  new.dir="warfarin", param="rate", MM=TRUE)

# Both models using clearances and rates are used. 
# Level is set to 7 in order to get accurate results.
warf.pk3 <- pkbuild(data=warfarin,  new.dir="warfarin", param="both", level=7)

## End(Not run)

Compute initial population PK parameters

Description

Use the pooled PK data to derive population PK parameters for a "standard" PK model (i.e. a model of the Monolix PK library). The structural model is automatically defined using the names of the PK parameters. Allowed names are: 'Tlag', 'Mtt', 'Ktr', 'ka', 'Tk0', 'V', 'V1', 'V2', 'V3', 'Q', 'Q2', 'Q3', 'Cl', 'k', 'k12', 'k21', 'k13', 'k31', 'Vm', 'Km'.

Usage

pkpopini(
  data = NULL,
  project = NULL,
  parameter = NULL,
  new.project = NULL,
  new.dir = NULL,
  par.ini = NULL
)

Arguments

Details

A Monolix project is then automatically created using these values as initial population parameters.

See https://monolix.lixoft.com/rsmlx/pkpopini/ for more details.

Value

A list of results

Examples

## Not run: 
# Create in the working directory a Monolix project for a 1 cpt model with 
# lag time, 0 order absorption and linear elimination
warf.ini1 <- pkpopini(data=warfarin, param=c("Tlag", "Tk0", "V", "Cl")) 

# Create in directory 'warfarin' a Monolix project called 'warfPK2.mlxtran' 
# for a 2 cpt model with 1st order absorption and nonlinear elimination
warf.ini3 <- pkpopini(data=warfarin, param=c("ka", "V", "k12", "k21", "Vm", "Km"), 
                      new.dir="warfarin", new.project="warfPK2.mlxtran") 

## End(Not run)

Read formatted data file

Description

Read data in a Monolix/NONMEM format

Usage

readDatamlx(
  data = NULL,
  out.data = FALSE,
  nbSSDoses = 10,
  obs.rows = FALSE,
  datafile = NULL,
  header = NULL
)

Arguments

Value

A list of data frames

Examples

## Not run: 
# using a data file:
warfarinPK <- list(dataFile = "data/warfarinPK.csv",
                   headerTypes = c("id", "time", "observation", "amount", 
                                   "contcov", "contcov", "catcov"),
                   administration = "oral")
d <- readDatamlx(data=warfarinPK)


## End(Not run)

Monolix results

Description

Monolix results used by the Rsmlx examples

Usage

resMonolix

Format

A R list

Source

Monolix demos

References

Rsmlx website: http://rsmlx.webpopix.org

Easy tuning of the settings of a Monolix project

Description

Use a single accuracy level, between 1 and 9, to automatically tune all the settings of a Monolix project. When the accuray level is equal to 1, the algorithms are very fast but the results may be not precise. When the accuray level is equal to 9, the algorithms are slow but the results are accurate. Default Monolix settings are obtained with level=5.

Usage

setSettings(project = NULL, new.project = NULL, level = 5)

Arguments

Examples

## Not run: 
# RsmlxDemo1.mlxtran is a Monolix project for modelling the PK of warfarin.
# All settings of the project are set so that algorithms used by Monolix converge as 
# quickly as possible possible:
setSettings(project="RsmlxDemo1.mlxtran", level=1)

# A new project will be created with settings set in order to obtain the most 
# precise results possible:
new.project= file.path(tempdir(),"RsmlxDemoNew.mlxtran")
setSettings(project="RsmlxDemo1.mlxtran", new.project=new.project, level=9)

# See http://monolix.lixoft.com/rsmlx/setSettings/ for detailed examples of use of setSettings
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

## End(Not run)

Statistical tests for model assessment

Description

Perform several statistical tests using the results of a Monolix run to assess the statistical components of the model in use.

Usage

testmlx(
  project = NULL,
  tests = c("covariate", "randomEffect", "correlation", "residual"),
  plot = FALSE,
  adjust = "edf",
  n.sample = NULL
)

Arguments

Details

The tests used are: 1) F-tests (or, equivalently, correlation tests) to evaluate the effect of each covariate on each parameter ("covariate"), 2) correlation tests to assess the correlation structure of the random effects ("correlation"), 3) Shapiro-Wilk and Miao-Gel-Gastwirth tests to assess, respectively the normality and the symmetry of the distribution of the random effects (""randomEffect"), 4) Shapiro-Wilk and Miao-Gel-Gastwirth tests to assess, respectively the normality and the symmetry of the distribution of residual errors ("residual").

By default, the four tests are performed.

When several samples of the conditional distributions are used, two methods are proposed in order to take into the dependance of the samples for the Shapiro-Wilk and Miao-Gel-Gastwirth tests: "edf" computes an effective degrees of freedom, "BH" performs one test per replicates and adjust the smallest p-value using the Benjamini-Hochberg correction.

Value

a list of data frames and ggplot objects if plot=TRUE

Examples

## Not run: 
# RsmlxDemo2.mlxtran is a Monolix project for modelling the PK of warfarin using a PK model 
# with parameters ka, V, Cl.

#testmlx will perform statistical tests for the different component of the statistical model:
r1 <- testmlx(project="RsmlxDemo2.mlxtran")

#testmlx will perform statistical tests for the covariate model and the correlation model only.
r2 <- testmlx(project="RsmlxDemo2.mlxtran", tests=c("covariate","correlation"))

## End(Not run)

# See http://monolix.lixoft.com/rsmlx/testmlx/ for detailed examples of use of testmlx
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation

warfarin PKPD data

Description

The warfarin PK and PD data for 32 patients

Usage

warfarin.data

Format

A csv file

Source

Monolix demos

References

O’Reilly (1968). Studies on coumarin anticoagulant drugs. Initiation of warfarin therapy without a loading dose. Circulation 1968, 38:169-177.

Find a Monolix PK model

Description

Return the path of the Monolix PK model defined by a list of parameter names See https://monolix.lixoft.com/rsmlx/whichPKmodel/ for more details.

Usage

whichPKmodel(parameter, mlxPath = NULL, pkPath = NULL, lib = FALSE)

Arguments

Examples

## Not run: 
whichPKmodel(parameter=c("Tlag", "Tk0", "V", "Cl"))

## End(Not run)