
def _parse_args(self,  loc=0, scale=1):
    return (), loc, scale

def _parse_args_rvs(self,  loc=0, scale=1, size=None):
    return self._argcheck_rvs( loc, scale, size=size)

def _parse_args_stats(self,  loc=0, scale=1, moments='mv'):
    return (), loc, scale, moments


A logistic (or Sech-squared) continuous random variable.

    As an instance of the `rv_continuous` class, `logistic` object inherits from it
    a collection of generic methods (see below for the full list),
    and completes them with details specific for this particular distribution.
    
    Methods
    -------
    rvs(loc=0, scale=1, size=1, random_state=None)
        Random variates.
    pdf(x, loc=0, scale=1)
        Probability density function.
    logpdf(x, loc=0, scale=1)
        Log of the probability density function.
    cdf(x, loc=0, scale=1)
        Cumulative distribution function.
    logcdf(x, loc=0, scale=1)
        Log of the cumulative distribution function.
    sf(x, loc=0, scale=1)
        Survival function  (also defined as ``1 - cdf``, but `sf` is sometimes more accurate).
    logsf(x, loc=0, scale=1)
        Log of the survival function.
    ppf(q, loc=0, scale=1)
        Percent point function (inverse of ``cdf`` --- percentiles).
    isf(q, loc=0, scale=1)
        Inverse survival function (inverse of ``sf``).
    moment(order, loc=0, scale=1)
        Non-central moment of the specified order.
    stats(loc=0, scale=1, moments='mv')
        Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
    entropy(loc=0, scale=1)
        (Differential) entropy of the RV.
    fit(data)
        Parameter estimates for generic data.
        See `scipy.stats.rv_continuous.fit <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.fit.html#scipy.stats.rv_continuous.fit>`__ for detailed documentation of the
        keyword arguments.
    expect(func, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds)
        Expected value of a function (of one argument) with respect to the distribution.
    median(loc=0, scale=1)
        Median of the distribution.
    mean(loc=0, scale=1)
        Mean of the distribution.
    var(loc=0, scale=1)
        Variance of the distribution.
    std(loc=0, scale=1)
        Standard deviation of the distribution.
    interval(confidence, loc=0, scale=1)
        Confidence interval with equal areas around the median.

    Notes
    -----
    The probability density function for `logistic` is:

    .. math::

        f(x) = \frac{\exp(-x)}
                    {(1+\exp(-x))^2}

    `logistic` is a special case of `genlogistic` with ``c=1``.

    Remark that the survival function (``logistic.sf``) is equal to the
    Fermi-Dirac distribution describing fermionic statistics.

    The probability density above is defined in the "standardized" form. To shift
    and/or scale the distribution use the ``loc`` and ``scale`` parameters.
    Specifically, ``logistic.pdf(x, loc, scale)`` is identically
    equivalent to ``logistic.pdf(y) / scale`` with
    ``y = (x - loc) / scale``. Note that shifting the location of a distribution
    does not make it a "noncentral" distribution; noncentral generalizations of
    some distributions are available in separate classes.

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.stats import logistic
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)
    
    Calculate the first four moments:
    
    
    >>> mean, var, skew, kurt = logistic.stats(moments='mvsk')
    
    Display the probability density function (``pdf``):
    
    >>> x = np.linspace(logistic.ppf(0.01),
    ...                 logistic.ppf(0.99), 100)
    >>> ax.plot(x, logistic.pdf(x),
    ...        'r-', lw=5, alpha=0.6, label='logistic pdf')
    
    Alternatively, the distribution object can be called (as a function)
    to fix the shape, location and scale parameters. This returns a "frozen"
    RV object holding the given parameters fixed.
    
    Freeze the distribution and display the frozen ``pdf``:
    
    >>> rv = logistic()
    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
    
    Check accuracy of ``cdf`` and ``ppf``:
    
    >>> vals = logistic.ppf([0.001, 0.5, 0.999])
    >>> np.allclose([0.001, 0.5, 0.999], logistic.cdf(vals))
    True
    
    Generate random numbers:
    
    >>> r = logistic.rvs(size=1000)
    
    And compare the histogram:
    
    >>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
    >>> ax.set_xlim([x[0], x[-1]])
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()
    

    

Standard Errors assume that the covariance matrix of the errors is correctly specified.

Results class for OrderedModel

    This class inherits from GenericLikelihoodModelResults and not all
    inherited methods might be appropriate in this case.
    