identifiability - Parameter identifiability analysis in Python

This module performs parameter identifiability analysis to calculate and plot confidence intervals based on a profile-likelihood. The code is adapted from LMFIT, with custom functions to select the range for parameter scanning and for plotting the profile likelihood. The significance is assessed with the chi-squared distribution. Optimization runs can be performed in parallel (using the multiprocessing module).

Installation

identifiability is a pure-Python module. The latest development version can be installed with

$ pip install https://github.com/jmrohwer/identifiability/archive/refs/heads/main.zip

The latest stable release is available on PyPI:

$ pip install identifiability

The module can be used in combination with PySCeS for simulation and parameter estimation of kinetic models using the CVODE solver. When performing identifiability analysis in parallel using multiprocessing, additional dependences are required; these can be installed with:

$ pip install "identifiability[pyscesmp]"

Basic usage

For background, the reader is referred to the section on Calculation of confidence intervals in the LMFIT documentation.

To start the identifiability analysis, the user first needs to have performed a parameter estimation with LMFIT. The method for estimating confidence intervals takes an instantiated LMFIT Minimizer object and a MinimizerResult object as input.

A typical workflow would entail:

>>> from identifiability import conf_interval
>>> c = conf_interval(
        mini, result, prob=0.95, limits=0.5, log=False, points=11, return_CIclass=True
    )
>>> print(c[0])  # OrderedDict of parameter names and corresponding confidence intervals
>>> c[1].plot_ci('a')   # plot confidence interval for parameter 'a'
>>> c[1].plot_all_ci()  # plot confidence intervals for all parameters

When using the Model wrapper of LMFIT to perform the parameter estimation and model fit, the instantiated ModelResult object should be passed twice to the conf_interval() method, instead of the Minimizer and MinimizerResult (see above). In this case the function call would be:

>>> c = conf_interval(
        modelresult, modelresult, prob=0.95, limits=0.5, 
        log=False, points=11, return_CIclass=True
    )

Once a profile likelihood has been calculated, the same data can be used to calculate the confidence interval for a different probability, thus avoiding the computationally intensive re-calculation of the profile likelihood:

>>> c[1].calc_all_ci(prob=0.8)

Docstring of the conf_interval method

def conf_interval(
    minimizer,
    result,
    p_names=None,
    prob=0.95,
    limits=0.5,
    log=False,
    points=11,
    method='leastsq',
    return_CIclass=False,
    mp=True,
):
    """
    Calculate the confidence interval (CI) for parameters.

    The parameter for which the CI is calculated will be varied, while the
    remaining parameters are re-optimized to minimize the chi-square. The
    resulting chi-square is used to calculate the probability with a given
    statistic, i.e. chi-squared test.

    Parameters
    ----------
    minimizer : Minimizer or ModelResult
        The minimizer to use, holding objective function.
    result : MinimizerResult or ModelResult
        The result of running Minimizer.minimize() or Model.fit().
    p_names : list, optional
        Names of the parameters for which the CI is calculated. If None
        (default), the CI is calculated for every parameter.
    prob : float, optional
        The probability for the confidence interval (<1). If None,
        the default is 0.95 (95 % confidence interval).
    limits : float, optional
        The limits (as a fraction of the original parameter value) within which
        to vary the parameters for identifiability analysis (default is 0.5).
        If ``log=False``, the parameter is varied from p*limits to p*(2 - limits), 
        where p is the original value.
        If ``log=True``, the parameter is varied from p*limits to p/limits.
    log : bool, optional
        Whether to vary the parameter in a log (True) or a linear (False,
        default) scale.
    points : int, optional
        The number of points for which to calculate the profile likelihood over
        the given parameter range.
    method : str, optional
        The lmfit mimimize() method to use (default='leastsq')
    return_CIclass : bool, optional
        When true, return the instantiated ``ConfidenceInterval`` class to
        access its methods directly (default=False).
    mp : bool, optional
        Run the optimization in parallel using ``multiprocessing`` (default=True)

    Returns
    -------
    output : dict
        A dictionary containing a list of ``(lower, upper)``-tuples containing
        the confidence bounds for each parameter.
    ci : ``ConfidenceInterval`` instance, optional
        Instantiated ``ConfidenceInterval`` class to access the attached methods.
    """

© Johann M. Rohwer, 2023