ModelfitResults#

class pharmpy.workflows.ModelfitResults(__version__='0.109.0', name=None, description=None, ofv=None, ofv_iterations=None, parameter_estimates=None, parameter_estimates_sdcorr=None, parameter_estimates_iterations=None, covariance_matrix=None, correlation_matrix=None, precision_matrix=None, standard_errors=None, standard_errors_sdcorr=None, relative_standard_errors=None, minimization_successful=None, minimization_successful_iterations=None, estimation_runtime=None, estimation_runtime_iterations=None, individual_ofv=None, individual_estimates=None, individual_estimates_covariance=None, residuals=None, predictions=None, runtime_total=None, termination_cause=None, termination_cause_iterations=None, function_evaluations=None, function_evaluations_iterations=None, significant_digits=None, significant_digits_iterations=None, log_likelihood=None, log=None, evaluation=None, covstep_successful=None, gradients=None, gradients_iterations=(None,), warnings=None)[source]#

Bases: Results

Base class for results from a modelfit operation

Variables:

name (str) – Name of model
description (str) – Description of model
correlation_matrix (pd.DataFrame) – Correlation matrix of the population parameter estimates
covariance_matrix (pd.DataFrame) – Covariance matrix of the population parameter estimates
precision_matrix (pd.DataFrame) – Precision matrix of the population parameter estimates
evaluation_ofv (float) – The objective function value as if the model was evaluated. Currently works for classical estimation methods by taking the OFV of the first iteration.
individual_ofv (pd.Series) – OFV for each individual
individual_estimates (pd.DataFrame) – Estimates for etas
individual_estimates_covariance (pd.Series) – Estimated covariance between etas
parameter_estimates (pd.Series) – Population parameter estimates
parameter_estimates_iterations (pd.DataFrame) – All recorded iterations for parameter estimates
parameter_estimates_sdcorr (pd.Series) – Population parameter estimates with variability parameters as standard deviations and correlations
residuals (pd.DataFrame) – Table of various residuals
predictions (pd.DataFrame) – Table of various predictions
estimation_runtime (float) – Runtime for one estimation step
runtime_total (float) – Total runtime of estimation
standard_errors (pd.Series) – Standard errors of the population parameter estimates
standard_errors_sdcorr (pd.Series) – Standard errors of the population parameter estimates on standard deviation and correlation scale
relative_standard_errors (pd.Series) – Relative standard errors of the population parameter estimates
termination_cause (str) – The cause of premature termination. One of ‘maxevals_exceeded’ and ‘rounding_errors’
function_evaluations (int) – Number of function evaluations
evaluation (pd.Series) – A bool for each estimation step. True if this was a model evaluation and False otherwise
covstep_successful (bool or None) – Covariance status.
gradients (pd.Series) – Final parameter gradients
gradients_iterations (pd.DataFrame) – All recorded parameter gradients
warnings (List) – List of warnings

Attributes Summary

`correlation_matrix`
`covariance_matrix`
`covstep_successful`
`description`
`estimation_runtime`
`estimation_runtime_iterations`
`evaluation`
`function_evaluations`
`function_evaluations_iterations`
`gradients`
`gradients_iterations`
`individual_estimates`
`individual_estimates_covariance`
`individual_ofv`
`log`
`log_likelihood`
`minimization_successful`
`minimization_successful_iterations`
`name`
`ofv`
`ofv_iterations`
`parameter_estimates`
`parameter_estimates_iterations`
`parameter_estimates_sdcorr`
`precision_matrix`
`predictions`
`relative_standard_errors`
`residuals`
`runtime_total`
`significant_digits`
`significant_digits_iterations`
`standard_errors`
`standard_errors_sdcorr`
`termination_cause`
`termination_cause_iterations`
`warnings`

Attributes Documentation

correlation_matrix = None#

covariance_matrix = None#

covstep_successful = None#

description = None#

estimation_runtime = None#

estimation_runtime_iterations = None#

evaluation = None#

function_evaluations = None#

function_evaluations_iterations = None#

gradients = None#

gradients_iterations = (None,)#

individual_estimates = None#

individual_estimates_covariance = None#

individual_ofv = None#

log = None#

log_likelihood = None#

minimization_successful = None#

minimization_successful_iterations = None#

name = None#

ofv = None#

ofv_iterations = None#

parameter_estimates = None#

parameter_estimates_iterations = None#

parameter_estimates_sdcorr = None#

precision_matrix = None#

predictions = None#

relative_standard_errors = None#

residuals = None#

runtime_total = None#

significant_digits = None#

significant_digits_iterations = None#

standard_errors = None#

standard_errors_sdcorr = None#

termination_cause = None#

termination_cause_iterations = None#

warnings = None#