statsmodels.discrete.discrete_model.ProbitResults.cov_params ProbitResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like

statsmodels.tools.tools.recipr0 statsmodels.tools.tools.recipr0(X) [source] Return the reciprocal of an array, setting all entries equal to 0 as 0. It does not assume that X should be positive in general.

statsmodels.sandbox.regression.gmm.IVRegressionResults.resid static IVRegressionResults.resid()

statsmodels.regression.mixed_linear_model.MixedLM.get_scale MixedLM.get_scale(fe_params, cov_re) [source] Returns the estimated error variance based on given estimates of the slopes and random effects covariance matrix. Parameters: fe_params : array-like The regression slope estimates cov_re : 2d array Estimate of the random effects covariance matrix (Psi). Returns: scale : float The estimated error variance.

statsmodels.duration.hazard_regression.PHRegResults.get_distribution PHRegResults.get_distribution() [source] Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case. Returns: A list of objects of type scipy.stats.distributions.rv_discrete : Notes The distributions are obtained from a simple discrete estimate of the survivor function that puts all mass on the observed failure times wihtin a stratum.

statsmodels.discrete.discrete_model.LogitResults.resid_dev static LogitResults.resid_dev() Deviance residuals Notes Deviance residuals are defined where and is the total number of observations sharing the covariate pattern . For now is always set to 1.

statsmodels.genmod.generalized_linear_model.GLMResults.null_deviance static GLMResults.null_deviance() [source]

statsmodels.sandbox.distributions.extras.ACSkewT_gen.fit_loc_scale ACSkewT_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.

statsmodels.genmod.families.family.Poisson.weights Poisson.weights(mu) Weights for IRLS steps Parameters: mu : array-like The transformed mean response variable in the exponential family Returns: w : array The weights for the IRLS steps Notes w = 1 / (link?(mu)**2 * variance(mu))

statsmodels.sandbox.distributions.extras.ACSkewT_gen.logsf ACSkewT_gen.logsf(x, *args, **kwds) Log of the survival function of the given RV. Returns the log of the ?survival function,? defined as (1 - cdf), evaluated at x. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional sca