statsmodels.sandbox.distributions.extras.ACSkewT_gen class statsmodels.sandbox.distributions.extras.ACSkewT_gen [source] univariate Skew-T distribution of Azzalini class follows scipy.stats.distributions pattern but with __init__ Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_loc_scale is deprecated! expect([func, args, loc, scale, lb, ub, ...]) Calculate expected val

statsmodels.genmod.cov_struct.GlobalOddsRatio.get_eyy GlobalOddsRatio.get_eyy(endog_expval, index) [source] Returns a matrix V such that V[i,j] is the joint probability that endog[i] = 1 and endog[j] = 1, based on the marginal probabilities of endog and the global odds ratio current_or.

statsmodels.genmod.cov_struct.Nested class statsmodels.genmod.cov_struct.Nested(cov_nearest_method='clipped') [source] A nested working dependence structure. A working dependence structure that captures a nested hierarchy of groups, each level of which contributes to the random error term of the model. When using this working covariance structure, dep_data of the GEE instance should contain a n_obs x k matrix of 0/1 indicators, corresponding to the k subgroups nested under the top-level grou

statsmodels.discrete.discrete_model.NegativeBinomial.initialize NegativeBinomial.initialize() Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

statsmodels.discrete.discrete_model.DiscreteModel.cov_params_func_l1 DiscreteModel.cov_params_func_l1(likelihood_model, xopt, retvals) [source] 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.sandbox.distributions.transformed.Transf_gen.std Transf_gen.std(*args, **kwds) Standard deviation 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: std : float standard deviation of the distribution

statsmodels.discrete.discrete_model.Poisson.pdf Poisson.pdf(X) [source] Poisson model probability mass function Parameters: X : array-like X is the linear predictor of the model. See notes. Returns: pdf : ndarray The value of the Poisson probability mass function, PMF, for each point of X. Notes The PMF is defined as where assumes the loglinear model. I.e., The parameter X is in the above formula.

statsmodels.discrete.discrete_model.ProbitResults.bse static ProbitResults.bse()

statsmodels.discrete.discrete_model.BinaryModel.information BinaryModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.regression.try_catdata.convertlabels statsmodels.sandbox.regression.try_catdata.convertlabels(ys, indices=None) [source] convert labels based on multiple variables or string labels to unique index labels 0,1,2,...,nk-1 where nk is the number of distinct labels