statsmodels.discrete.discrete_model.Poisson.cdf Poisson.cdf(X) [source] Poisson model cumulative distribution function Parameters: X : array-like X is the linear predictor of the model. See notes. Returns: The value of the Poisson CDF at each point. : Notes The CDF is defined as where assumes the loglinear model. I.e., The parameter X is in the above formula.

statsmodels.discrete.discrete_model.LogitResults.resid_response static LogitResults.resid_response() The response residuals Notes Response residuals are defined to be where .

statsmodels.stats.proportion.proportions_chisquare_allpairs statsmodels.stats.proportion.proportions_chisquare_allpairs(count, nobs, multitest_method='hs') [source] chisquare test of proportions for all pairs of k samples Performs a chisquare test for proportions for all pairwise comparisons. The alternative is two-sided Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. prop : float, optional The proba

statsmodels.discrete.discrete_model.LogitResults.remove_data LogitResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribute

statsmodels.discrete.discrete_model.LogitResults.get_margeff LogitResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each regressor. ?all?

statsmodels.discrete.discrete_model.Poisson.fit_constrained Poisson.fit_constrained(constraints, start_params=None, **fit_kwds) [source] fit the model subject to linear equality constraints The constraints are of the form R params = q where R is the constraint_matrix and q is the vector of constraint_values. The estimation creates a new model with transformed design matrix, exog, and converts the results back to the original parameterization. Parameters: constraints : formula expression or

statsmodels.robust.scale.hubers_scale statsmodels.robust.scale.hubers_scale = Huber?s scaling for fitting robust linear models. Huber?s scale is intended to be used as the scale estimate in the IRLS algorithm and is slightly different than the Huber class. Parameters: d : float, optional d is the tuning constant for Huber?s scale. Default is 2.5 tol : float, optional The convergence tolerance maxiter : int, optiona The maximum number of iterations. The default is 30. Notes Huber?s s

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.entropy SkewNorm2_gen.entropy(*args, **kwds) Differential entropy of the 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 Scale parameter (default=1).

statsmodels.discrete.discrete_model.BinaryResults.llf static BinaryResults.llf()

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.fit TransfTwo_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape param