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.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.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.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

statsmodels.graphics.gofplots.qqplot_2samples statsmodels.graphics.gofplots.qqplot_2samples(data1, data2, xlabel=None, ylabel=None, line=None, ax=None) [source] Q-Q Plot of two samples? quantiles. Can take either two ProbPlot instances or two array-like objects. In the case of the latter, both inputs will be converted to ProbPlot instances using only the default values - so use ProbPlot instances if finer-grained control of the quantile computations is required. Parameters: data1, data2 : a

statsmodels.tsa.arima_model.ARIMAResults.aic static ARIMAResults.aic()

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

statsmodels.miscmodels.count.PoissonZiGMLE.loglikeobs PoissonZiGMLE.loglikeobs(params)