statsmodels.genmod.generalized_linear_model.GLMResults.load classmethod GLMResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.discrete.discrete_model.MNLogit.from_formula classmethod MNLogit.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args :

statsmodels.miscmodels.tmodel.TLinearModel.nloglike TLinearModel.nloglike(params)

statsmodels.genmod.families.links.NegativeBinomial class statsmodels.genmod.families.links.NegativeBinomial(alpha=1.0) [source] The negative binomial link function Parameters: alpha : float, optional Alpha is the ancillary parameter of the Negative Binomial link function. It is assumed to be nonstochastic. The default value is 1. Permissible values are usually assumed to be in (.01, 2). Methods deriv(p) Derivative of the negative binomial transform inverse(z) Inverse of the negative bi

statsmodels.sandbox.distributions.transformed.Transf_gen.mean Transf_gen.mean(*args, **kwds) Mean of the distribution Parameters: 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 scale parameter (default=1) Returns: mean : float the mean of the distribution

statsmodels.sandbox.distributions.extras.ACSkewT_gen.freeze ACSkewT_gen.freeze(*args, **kwds) Freeze the distribution for the given arguments. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution. Should include all the non-optional arguments, may include loc and scale. Returns: rv_frozen : rv_frozen instance The frozen distribution.

statsmodels.regression.mixed_linear_model.MixedLMResults.summary MixedLMResults.summary(yname=None, xname_fe=None, xname_re=None, title=None, alpha=0.05) [source] Summarize the mixed model regression results. Parameters: yname : string, optional Default is y xname_fe : list of strings, optional Fixed effects covariate names xname_re : list of strings, optional Random effects covariate names title : string, optional Title for the top table. If not None, then this replaces the default

statsmodels.tsa.vector_ar.var_model.VARResults.detomega static VARResults.detomega() [source] Return determinant of white noise covariance with degrees of freedom correction:

statsmodels.genmod.families.links.Log.inverse Log.inverse(z) [source] Inverse of log transform link function Parameters: z : array The inverse of the link function at p Returns: p : array The mean probabilities given the value of the inverse z Notes g^{-1}(z) = exp(z)

statsmodels.genmod.families.links.NegativeBinomial.inverse_deriv NegativeBinomial.inverse_deriv(z) [source] Derivative of the inverse of the negative binomial transform Parameters: z : array-like Usually the linear predictor for a GLM or GEE model Returns: The value of the inverse of the derivative of the negative binomial : link :