statsmodels.sandbox.distributions.extras.NormExpan_gen.freeze NormExpan_gen.freeze(*args, **kwds) Freeze the distribution for the given arguments. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution. Should include all the non-optional arguments, may include loc and scale. Returns: rv_frozen : rv_frozen instance The frozen distribution.

statsmodels.regression.linear_model.RegressionResults.compare_lm_test RegressionResults.compare_lm_test(restricted, demean=True, use_lr=False) [source] Use Lagrange Multiplier test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. demean : bo

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.entropy TransfTwo_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.genmod.families.links.nbinom class statsmodels.genmod.families.links.nbinom(alpha=1.0) [source] The negative binomial link function. Notes g(p) = log(p/(p + 1/alpha)) nbinom is an alias of NegativeBinomial. nbinom = NegativeBinomial(alpha=1.) Methods deriv(p) Derivative of the negative binomial transform inverse(z) Inverse of the negative binomial transform inverse_deriv(z) Derivative of the inverse of the negative binomial transform

statsmodels.discrete.discrete_model.ProbitResults class statsmodels.discrete.discrete_model.ProbitResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for Probit Model Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criteri

statsmodels.stats.weightstats.ttest_ind statsmodels.stats.weightstats.ttest_ind(x1, x2, alternative='two-sided', usevar='pooled', weights=(None, None), value=0) [source] ttest independent sample convenience function that uses the classes and throws away the intermediate results, compared to scipy stats: drops axis option, adds alternative, usevar, and weights option Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The altern

statsmodels.nonparametric.kde.KDEUnivariate.evaluate KDEUnivariate.evaluate(point) [source] Evaluate density at a single point. Parameters: point : float Point at which to evaluate the density.

statsmodels.stats.power.GofChisquarePower.solve_power GofChisquarePower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, n_bins=2) [source] solve for any one parameter of the power of a one sample chisquare-test for the one sample chisquare-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. n_bins needs to be defined, a default=2 is used. Parameters: effect_size : float standardized effect size, according to

statsmodels.genmod.families.links.Logit.deriv2 Logit.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.discrete.discrete_model.LogitResults.initialize LogitResults.initialize(model, params, **kwd)