Model#

class pharmpy.Model[source]#

Bases: object

The Pharmpy model class

Attributes Summary

`datainfo`	Definitions of model statements
`dataset`	Dataset connected to model
`dependent_variable`	The model dependent variable, i.e. y.
`description`	A free text discription of the model
`estimation_steps`	Definitions of estimation steps
`filename_extension`	Filename extension of model file
`initial_individual_estimates`	Initial estimates for individual parameters
`model_code`	Model type specific code
`modelfit_results`	Modelfit results for this model
`name`	Name of the model
`observation_transformation`	Transformation to be applied to the observation data
`parameters`	Definitions of population parameters
`parent_model`	Name of parent model
`random_variables`	Definitions of random variables
`statements`	Definitions of model statements
`value_type`	The type of the model value (dependent variable)

Methods Summary

`copy`()	Create a deepcopy of the model object
`create_model`([obj])	Factory for creating a `pharmpy.model` object from an object representing the model
`has_same_dataset_as`(other)	Check if this model has the same dataset as another model
`read_modelfit_results`(path)	Read in modelfit results
`update_datainfo`()	Update model.datainfo for a new dataset

Attributes Documentation

datainfo#

Definitions of model statements

See pharmpy.Statements

dataset#: Dataset connected to model

dependent_variable#: The model dependent variable, i.e. y

description#: A free text discription of the model

estimation_steps#

Definitions of estimation steps

See pharmpy.EstimationSteps

filename_extension#: Filename extension of model file

initial_individual_estimates#: Initial estimates for individual parameters

model_code#: Model type specific code

modelfit_results#: Modelfit results for this model

name#: Name of the model

observation_transformation#: Transformation to be applied to the observation data

parameters#

Definitions of population parameters

See pharmpy.Parameters

parent_model#: Name of parent model

random_variables#

Definitions of random variables

See pharmpy.RandomVariables

statements#

Definitions of model statements

See pharmpy.Statements

value_type#

The type of the model value (dependent variable)

By default this is set to ‘PREDICTION’ to mean that the model outputs a prediction. It could optionally be set to ‘LIKELIHOOD’ or ‘-2LL’ to let the model output the likelihood or -2*log(likelihood) of the prediction. If set to a symbol this variable can be used to change the type for different records. The model would then set this symbol to 0 for a prediction value, 1 for likelihood and 2 for -2ll.

Methods Documentation

copy()[source]#: Create a deepcopy of the model object

static create_model(obj=None, **kwargs)[source]#

Factory for creating a pharmpy.model object from an object representing the model

Parameters:: obj – path-like object pointing to the model file or an IO object.
Returns:: Model – Generic Model if obj is None, otherwise appropriate implementation is invoked (e.g. NONMEM7 Model).

has_same_dataset_as(other)[source]#

Check if this model has the same dataset as another model

Parameters:: other (Model) – Another model
Returns:: bool – True if both models have the same dataset

read_modelfit_results(path: Path)[source]#: Read in modelfit results

update_datainfo()[source]#: Update model.datainfo for a new dataset