getCorrelationOfEstimates | Get the inverse of the last estimated Fisher matrix computed either by all the Fisher methods used during the last scenario run or by the specific one passed in argument. |

getEstimatedIndividualParameters | Get the last estimated values for each subject of some of the individual parameters present within the current project. |

getEstimatedLogLikelihood | Get the values computed by using a log-likelihood algorithm during the last scenario run, with or without a method-based filter. |

getEstimatedPopulationParameters | Get the last estimated value of some of the population parameters present within the current project (fixed effects + individual variances + correlations + latent probabilities + error model parameters). |

getEstimatedRandomEffects | Get the random effects for each subject of some of the individual parameters present within the current project. |

getEstimatedStandardErrors | Get the last estimated standard errors of population parameters computed either by all the Fisher methods used during the last scenario run or by the specific one passed in argument. |

getLaunchedTasks | Get a list of the tasks which have results to provide. |

getSAEMiterations | Retrieve the successive values of some of the population parameters present within the current project (fixed effects + individual variances + correlations + latent probabilities + error model parameters) during the previous run of the SAEM algorithm. |

getSimulatedIndividualParameters | Get the simulated values for each replicate of each subject of some of the individual parameters present within the current project. |

getSimulatedRandomEffects | Get the simulated values for each replicate of each subject of some of the individual random effects present within the current project. |

## Get the inverse of the Fisher Matrix

### Description

Get the inverse of the last estimated Fisher matrix computed either by all the Fisher methods used during the last scenario run or by the specific one passed in argument.

WARNING: The Fisher matrix cannot be accessible until the Fisher algorithm has been launched once.

The user can choose to display only the Fisher matrix estimated with a specific method.

Existing Fisher methods :

Fisher by Linearization | “linearization” |

Fisher by Stochastic Approximation | “stochasticApproximation” |

WARNING: Only the methods which have been used during the last scenario run can provide results.

### Usage

getCorrelationOfEstimates(method = "")

### Arguments

method

<a href="*string*“>optional Fisher method whose results should be displayed.

If this field is not specified, the results provided by all the methods used during the last scenario run are displayed.

### Value

A list whose each field contains the Fisher matrix computed by one of the available Fisher methods used during the ast scenario run.

A matrix is defined as a structure containing the following fields :

rownames | list of row names |

columnnames | list of column names |

rownumber | number of rows |

data | vector<…> containing matrix raw values (column major) |

### Click here to see examples

## Not run:

getCorrelationOfEstimates(“linearization”)

-> list( linearization = list( data = c(1,0,0,0,1,-0.06,0,-0.06,1), rownumber = 3, rownames = c(“Cl_pop”,”omega_Cl”,”a”), columnnames = c(“Cl_pop”,”omega_Cl”,”a”) ) )

getCorrelationOfEstimates() -> list( linearization = list(…), stochasticApproximation = list(…) )

## End(Not run)

Top of the page, Monolix-R functions.

## Get last estimated individual parameter values

### Description

Get the last estimated values for each subject of some of the individual parameters present within the current project.

WARNING: Estimated individual parameters values cannot be accessible until the individual estimation algorithm has been launched once.

NOTE: The user can choose to display only the individual parameter values estimated with a specific method.

Existing individual estimation methods :

Conditional Mean SAEM | “saem” |

Conditional Mean | “conditionalMean” |

Conditional Mode | “conditionalMode” |

WARNING: Only the methods which have been used during the last scenario run can provide estimation results.

### Usage

getEstimatedIndividualParameters(..., method = "")

### Arguments

…

(*string*) Name of the individual parameters whose values must be displayed. Call `getIndividualParameterModel`

to get a list of the individual parameters present within the current project.

method

<a href="*string*“>optional Individual parameter estimation method whose results should be displayed.

If there are latent covariate used in the model, the estimated modality is displayed too

If this field is not specified, the results provided by all the methods used during the last scenario run are displayed.

### Value

A data frame giving, for each wanted method, the last estimated values of the individual parameters of interest for each subject with the corresponding standard deviation values.

### See Also

### Click here to see examples

## Not run:

indivParams = getEstimatedIndividualParameters() # retrieve the values of all the available individual parameters for all methods

-> $saem

id Cl V ka

1 0.28 7.71 0.29

. … … …

N 0.1047.62 1.51

indivParams = getEstimatedIndividualParameters(“Cl”, “V”, method = “conditionalMean”) # retrieve the values of the individual parameters “Cl” and “V” estimated by the conditional mode method

## End(Not run)

Top of the page, Monolix-R functions.

## Get Log-Likelihood values

### Description

Get the values computed by using a log-likelihood algorithm during the last scenario run, with or without a method-based filter.

WARNING: The log-likelihood values cannot be accessible until the log-likelihood algorithm has been launched once.

The user can choose to display only the log-likelihood values computed with a specific method.

Existing log-likelihood methods :

Log-likelihood by Linearization | “linearization” |

Log-likelihood by Important Sampling | “importanceSampling” |

WARNING: Only the methods which have been used during the last scenario run can provide results.

### Usage

getEstimatedLogLikelihood(method = "")

### Arguments

method

<a href="*string*“>optional Log-likelihood method whose results should be displayed.

If this field is not specified, the results provided by all the methods used during the last scenario run are retrieved.

### Value

A list associating the name of each method passed in argument to the corresponding log-likelihood values computed by during the last scenario run.

### Click here to see examples

## Not run:

getEstimatedLogLikelihood()

-> list( linearization = [LL = -170.505, AIC = 350.280, BIC = 365.335] ,

importanceSampling = […] )

getEstimatedLogLikelihood(“linearization”)

-> list( linearization = [LL = -170.505, AIC = 350.280, BIC = 365.335] )

## End(Not run)

Top of the page, Monolix-R functions.

## Get last estimated population parameter value

### Description

Get the last estimated value of some of the population parameters present within the current project (fixed effects + individual variances + correlations + latent probabilities + error model parameters).

WARNING: Estimated population parameters values cannot be accessible until the SAEM algorithm has been launched once.

### Usage

getEstimatedPopulationParameters(...)

### Arguments

…

[optional] (*array<string>*) Names of the population parameters whose value must be displayed. Call `getPopulationParameterInformation`

to get a list of the population parameters present within the current project.

If this field is not specified, the function will retrieve the values of all the available population parameters.

### Value

A named vector containing the last estimated value of each one of the population parameters passed in argument.

### Click here to see examples

## Not run:

getEstimatedPopulationParameters(“V_pop”) -> [V_pop = 0.5]

getEstimatedPopulationParameters(“V_pop”,”Cl_pop”) -> [V_pop = 0.5, Cl_pop = 0.25]

getEstimatedPopulationParameters() -> [V_pop = 0.5, Cl_pop = 0.25, ka_pop = 0.05]

## End(Not run)

Top of the page, Monolix-R functions.

## Get estimated the random effects

### Description

Get the random effects for each subject of some of the individual parameters present within the current project.

WARNING: Estimated random effects cannot be accessible until the individual estimation algorithm has been launched once.

The user can choose to display only the random effects estimated with a specific method.

NOTE: The random effects are defined in the gaussian referential, e.g. if ka is lognormally distributed around ka_pop, eta_i = log(ka_i)-log(ka_pop)

Existing individual estimation methods :

Conditional Mean SAEM | “saem” |

Conditional Mean | “conditionalMean” |

Conditional Mode | “conditionalMode” |

WARNING: Only the methods which have been used during the last scenario run can provide estimation results. Please call ` getLaunchedTasks`

to get a list of the methods whose results are available.

### Usage

getEstimatedRandomEffects(..., method = "")

### Arguments

…

(*string*) Name of the individual parameters whose random effects must be displayed. Call `getIndividualParameterModel`

to get a list of the individual parameters present within the current project.

method

<a href="*string*“>optional Individual parameter estimation method whose results should be displayed.

If this field is not specified, the results provided by all the methods used during the last scenario run are displayed.

### Value

A data frame giving, for each wanted method, the last estimated eta values of the individual parameters of interest for each subject with the corresponding standard deviation values.

### See Also

` getEstimatedIndividualParameters`

### Click here to see examples

## Not run:

etaParams = getEstimatedRandomEffects() # retrieve the values of all the available random effects for all methods, without the associated standard deviations

-> $saem

id Cl V ka

1 0.28 7.71 0.29

. … … …

N 0.1047.62 1.51

etaParams = getEstimatedRandomEffects(“Cl”, “V”, method = “conditionalMode”) # retrieve the values of the individual parameters “Cl” and “V” estimated by the conditional mean from SAEM algorithm

## End(Not run)

Top of the page, Monolix-R functions.

## Get standard errors of population parameters

### Description

Get the last estimated standard errors of population parameters computed either by all the Fisher methods used during the last scenario run or by the specific one passed in argument.

WARNING: The standard errors cannot be accessible until the Fisher algorithm has been launched once.

Existing Fisher methods :

Fisher by Linearization | “linearization” |

Fisher by Stochastic Approximation | “stochasticApproximation” |

WARNING: Only the methods which have been used during the last scenario run can provide results.

### Usage

getEstimatedStandardErrors(method = "")

### Arguments

method

<a href="*string*“>optional Fisher method whose results should be displayed.

If this field is not specified, the results provided by all the methods used during the last scenario run are retrieved

### Value

A list associating each retrieved Fisher algorithm method to the standard errors of population parameters computed during its last run.

### Click here to see examples

## Not run:

getEstimatedStandardErrors() -> list( linearization = […], stochasticApproximation = […] )

getEstimatedStandardErrors(“linearization”) -> list( linearization = […] )

## End(Not run)

Top of the page, Monolix-R functions.

## Get tasks with results

### Description

Get a list of the tasks which have results to provide. A task is the association of:

- an algorithm (string)
- a vector of methods (string) relative to this algorithm for the standardErrorEstimation and the loglikelihoodEstimation, TRUE or FALSE for the other one.

### Usage

getLaunchedTasks()

### Value

The list of tasks with results, indexed by algorithm names.

### Click here to see examples

## Not run:

tasks = getLaunchedTasks()

tasks

-> $populationParameterEstimation = TRUE

$conditionalModeEstimation = TRUE

$standardErrorEstimation = “linearization”

## End(Not run)

Top of the page, Monolix-R functions.

## Get SAEM algorithm iterations

### Description

Retrieve the successive values of some of the population parameters present within the current project (fixed effects + individual variances + correlations + latent probabilities + error model parameters) during the previous run of the SAEM algorithm.

WARNING: Convergence history of population parameters values cannot be accessible until the SAEM algorithm has been launched once.

### Usage

getSAEMiterations(...)

### Arguments

…

[optional] (*array<string>*) Names of the population parameters whose convergence history must be displayed. Call `getPopulationParameterInformation`

to get a list of the population parameters present within the current project.

If this field is not specified, the function will retrieve the values of all the available population parameters.

### Value

A list containing a pair composed by the number of exploratory and smoothing iterations and a data frame which associates each wanted population parameter to its successive values over SAEM algorithm iterations.

### Click here to see examples

## Not run:

report = getSAEMiterations()

report

-> $iterationNumbers

c(50,25)

$estimates

V Cl

0.25 0

0.3 0.5

. .

0.35 0.25

## End(Not run)

Top of the page, Monolix-R functions.

## Get simulated individual parameters

### Description

Get the simulated values for each replicate of each subject of some of the individual parameters present within the current project.

WARNING: Simulated individual parameters values cannot be accessible until the individual estimation with conditional mean algorithm has been launched once.

### Usage

getSimulatedIndividualParameters(...)

### Arguments

…

(*string*) Name of the individual parameters whose values must be displayed. Call `getIndividualParameterModel`

to get a list of the individual parameters present within the current project.

### Value

A list giving the last simulated values of the individual parameters of interest for each replicate of each subject.

### See Also

### Click here to see examples

## Not run:

simParams = getSimulatedIndividualParameters() # retrieve the values of all the available individual parameters

simParams

rep id Cl V ka

1 1 0.022 0.37 1.79

1 2 0.033 0.42 -0.92

. . … … …

2 1 0.021 0.33 1.47

. . … … …

## End(Not run)

Top of the page, Monolix-R functions.

## Get simulated random effects

### Description

Get the simulated values for each replicate of each subject of some of the individual random effects present within the current project.

WARNING: Simulated individual random effects values cannot be accessible until the individual estimation algorithm with conditional mean has been launched once.

### Usage

getSimulatedRandomEffects(...)

### Arguments

…

(*string*) Name of the individual parameters whose values must be displayed. Call `getIndividualParameterModel`

to get a list of the individual parameters present within the current project.

### Value

A list giving the last simulated values of the individual random effects of interest for each replicate of each subject.

### See Also

`getIndividualParameterModel`

### Click here to see examples

## Not run:

simEtas = getSimulatedRandomEffects() # retrieve the values of all the available individual random effects

simEtas

rep id Cl V ka

1 1 0.022 0.37 1.79

1 2 0.033 0.42 -0.92

. . … … …

2 1 0.021 0.33 1.47

. . … … …

## End(Not run)

Top of the page, Monolix-R functions.