statsmodels.discrete.discrete_model.BinaryResults.save BinaryResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remo

statsmodels.sandbox.distributions.transformed.negsquarenormalg statsmodels.sandbox.distributions.transformed.negsquarenormalg = Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus

statsmodels.sandbox.distributions.extras.NormExpan_gen.mean NormExpan_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.duration.hazard_regression.PHRegResults.wald_test PHRegResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A

statsmodels.sandbox.distributions.extras.NormExpan_gen.fit_loc_scale NormExpan_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.discrete.discrete_model.CountModel.loglike CountModel.loglike(params) Log-likelihood of model.

statsmodels.stats.weightstats.DescrStatsW.std_mean static DescrStatsW.std_mean() [source] standard deviation of weighted mean

statsmodels.regression.mixed_linear_model.MixedLM class statsmodels.regression.mixed_linear_model.MixedLM(endog, exog, groups, exog_re=None, use_sqrt=True, missing='none', **kwargs) [source] An object specifying a linear mixed effects model. Use the fit method to fit the model and obtain a results object. Parameters: endog : 1d array-like The dependent variable exog : 2d array-like A matrix of covariates used to determine the mean structure (the ?fixed effects? covariates). groups : 1d

statsmodels.regression.linear_model.OLSResults.cov_HC3 static OLSResults.cov_HC3() See statsmodels.RegressionResults

statsmodels.discrete.discrete_model.MultinomialResults.bic static MultinomialResults.bic() [source]