statsmodels.sandbox.regression.gmm.IVGMMResults.load classmethod IVGMMResults.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.genmod.generalized_linear_model.GLMResults.pearson_chi2 static GLMResults.pearson_chi2() [source]

statsmodels.genmod.generalized_linear_model.GLMResults.t_test GLMResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. 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 tuple

statsmodels.discrete.discrete_model.MultinomialResults.fittedvalues static MultinomialResults.fittedvalues()

statsmodels.genmod.generalized_estimating_equations.GEEResults.bse static GEEResults.bse() [source]

statsmodels.regression.mixed_linear_model.MixedLM.fit_regularized MixedLM.fit_regularized(start_params=None, method='l1', alpha=0, ceps=0.0001, ptol=1e-06, maxit=200, **fit_kwargs) [source] Fit a model in which the fixed effects parameters are penalized. The dependence parameters are held fixed at their estimated values in the unpenalized model. Parameters: method : string of Penalty object Method for regularization. If a string, must be ?l1?. alpha : array-like Scalar or vector of penal

statsmodels.regression.linear_model.OLS.information OLS.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.regression.quantile_regression.QuantRegResults.ess static QuantRegResults.ess()

statsmodels.sandbox.stats.multicomp.GroupsStats.groupsswithin GroupsStats.groupsswithin() [source]

statsmodels.tsa.arima_model.ARIMA.hessian ARIMA.hessian(params) Compute the Hessian at params, Notes This is a numerical approximation.