Standard/"basic" NONMEM individual problem

Description

This class holds the results from an individual NONMEM problem (not an entire control file) that has no $SIM step.

Objects from the Class

Individual objects of this class are not meant to be instantiated on their own, but are created when loading a NONMEM run via importNm

Slots

parameterIterations:

A data.frame of the iteration of each parameter estimate, if available

objectiveFinal:

The numeric value of the objective function minimum

thetaInitial:

Initial value of the "thetas"

sigmaInitial:

Initial value of the "sigmas"

omegaInitial:

Initial value of the "omegas"

thetaFinal:

Final estimates of the "thetas", together with the standard errors, if available (as a 2-row matrix)

sigmaFinal:

Final estimates of the "sigmas", together with the standard errors, if available (as an array with 1 or 2 matrices)

omegaFinal:

Final estimates of the "omegas", together with the standard errors, if available (as an array with 1 or 2 matrices)

parameterCovMatrix:

The variance-covariance of the parameter estimators, if available

parameterCorMatrix:

The correlation matrix of the parameter estimators, if available

minInfo:

A string describing the status of the objective function-minimization

Extends

The virtual class "NMProblem", directly.

Methods

getOmegas

signature(obj = "NMBasicModel"): Retrieves the omega estimates from the problem

getSigmas

signature(obj = "NMBasicModel"): Retrieves the sigma estimates from the problem

getThetas

signature(obj = "NMBasicModel"): Retrieves the theta estimates from the problem

addDerivedCategorical

signature(obj = "NMBasicModel"): Create user-defined categorical variables

imposeCategoryFormat

signature(obj = "NMBasicModel"): Coerces variables to categories

nmData

Extracts the input and output data from the problem

show

Display overview of problem

getObjective

signature(obj = "NMBasicModel")

getEstimateCov

signature(obj = "NMBasicModel"): Extracts the covariance matrix and optionally the correlation matrix of the estimators for this problem.

Author(s)

Mango Solutions <support@mango-solutions.com>

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