EstimationStep#

class pharmpy.EstimationStep(method, interaction=False, cov=False, evaluation=False, maximum_evaluations=None, laplace=False, isample=None, niter=None, auto=None, keep_every_nth_iter=None, residuals=None, predictions=None, solver=None, solver_rtol=None, solver_atol=None, tool_options=None, eta_derivatives=None, epsilon_derivatives=None)[source]#

Bases: object

Definition of one estimation operation

Attributes Summary

auto

Let estimation tool automatically add settings

cov

Should the parameter uncertainty be estimated?

epsilon_derivatives

List of names of epsilons for which to calculate derivatives

eta_derivatives

List of names of etas for which to calculate derivatives

evaluation

Only perform model evaluation

interaction

Preserve eta-epsilon interaction in the computation of the objective function

isample

Number of samples per subject (or similar) for EM methods

keep_every_nth_iter

Keep results for every nth iteration

laplace

Use the laplacian method

maximum_evaluations

Maximum allowable number of evaluations of the objective function

method

Name of the estimation method

niter

Number of iterations for EM methods

predictions

List of predictions to estimate

residuals

List of residuals to calculate

solver

Numerical solver to use when numerically solving the ODE system Supported solvers and their corresponding NONMEM ADVAN

solver_atol

Absolute tolerance for numerical ODE system solver

solver_rtol

Relative tolerance for numerical ODE system solver

supported_methods

tool_options

Dictionary of tool specific options

Methods Summary

derive(**kwargs)

Derive a new EstimationStep with new properties

Attributes Documentation

auto#

Let estimation tool automatically add settings

cov#

Should the parameter uncertainty be estimated?

epsilon_derivatives#

List of names of epsilons for which to calculate derivatives

eta_derivatives#

List of names of etas for which to calculate derivatives

evaluation#

Only perform model evaluation

interaction#

Preserve eta-epsilon interaction in the computation of the objective function

isample#

Number of samples per subject (or similar) for EM methods

keep_every_nth_iter#

Keep results for every nth iteration

laplace#

Use the laplacian method

maximum_evaluations#

Maximum allowable number of evaluations of the objective function

method#

Name of the estimation method

niter#

Number of iterations for EM methods

predictions#

List of predictions to estimate

residuals#

List of residuals to calculate

solver#

Numerical solver to use when numerically solving the ODE system Supported solvers and their corresponding NONMEM ADVAN

Solver

NONMEM ADVAN

CVODES

ADVAN14

DGEAR

ADVAN8

DVERK

ADVAN6

IDA

ADVAN15

LSODA

ADVAN13

LSODI

ADVAN9

solver_atol#

Absolute tolerance for numerical ODE system solver

solver_rtol#

Relative tolerance for numerical ODE system solver

supported_methods = ['FO', 'FOCE', 'ITS', 'IMPMAP', 'IMP', 'SAEM', 'BAYES']#
tool_options#

Dictionary of tool specific options

Methods Documentation

derive(**kwargs)[source]#

Derive a new EstimationStep with new properties