ModelfitResults#

class pharmpy.workflows.ModelfitResults(__version__='1.3.0', 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, derivatives=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, individual_eta_samples=None)[source]#

Bases: Results

Base class for results from a modelfit operation

Variables:

  • 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

  • derivaitves (pd.DataFrame) – Table of various derivatives

  • 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

  • individual_eta_samples (pd.DataFrame) – Individual eta samples

Attributes Summary

correlation_matrix

covariance_matrix

covstep_successful

derivatives

estimation_runtime

estimation_runtime_iterations

evaluation

function_evaluations

function_evaluations_iterations

gradients

gradients_iterations

individual_estimates

individual_estimates_covariance

individual_eta_samples

individual_ofv

log

log_likelihood

minimization_successful

minimization_successful_iterations

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#
derivatives = 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_eta_samples = None#
individual_ofv = None#
log = None#
log_likelihood = None#
minimization_successful = None#
minimization_successful_iterations = 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#