statsmodels.discrete.discrete_model.Logit.loglikeobs Logit.loglikeobs(params) [source] Log-likelihood of logit model for each observation. Parameters: params : array-like The parameters of the logit model. Returns: loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at params. See Notes Notes for observations where . This simplification comes from the fact that the logistic distribution is symmetric.

statsmodels.sandbox.distributions.transformed.LogTransf_gen.mean LogTransf_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.regression.gmm.NonlinearIVGMM.calc_weightmatrix NonlinearIVGMM.calc_weightmatrix(moms, weights_method='cov', wargs=(), params=None) calculate omega or the weighting matrix Parameters: moms : array, (nobs, nmoms) moment conditions for all observations evaluated at a parameter value weights_method : string ?cov? If method=?cov? is cov then the matrix is calculated as simple covariance of the moment conditions. see fit method for available aoptions for the weight and cov

statsmodels.discrete.discrete_model.MNLogit.cov_params_func_l1 MNLogit.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.regression.linear_model.RegressionResults.predict RegressionResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass

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

statsmodels.emplike.descriptive.DescStatUV.test_kurt DescStatUV.test_kurt(kurt0, return_weights=False) [source] Returns -2 x log-likelihood and the p-value for the hypothesized kurtosis. Parameters: kurt0 : float Kurtosis value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns: test_results : tuple The log-likelihood ratio and p-value of kurt0

statsmodels.sandbox.distributions.extras.NormExpan_gen.stats NormExpan_gen.stats(*args, **kwds) Some statistics of the given RV 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 (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which moments