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.genmod.generalized_estimating_equations.GEE class statsmodels.genmod.generalized_estimating_equations.GEE(endog, exog, groups, time=None, family=None, cov_struct=None, missing='none', offset=None, exposure=None, dep_data=None, constraint=None, update_dep=True, **kwargs) [source] Estimation of marginal regression models using Generalized Estimating Equations (GEE). GEE can be used to fit Generalized Linear Models (GLMs) when the data have a grouped structure, and the observations

statsmodels.discrete.discrete_model.Probit.score_obs Probit.score_obs(params) [source] Probit model Jacobian for each observation Parameters: params : array-like The parameters of the model Returns: jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at params. Notes for observations Where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.genmod.cov_struct.GlobalOddsRatio.initialize GlobalOddsRatio.initialize(model) [source]

statsmodels.genmod.families.family.Binomial.fitted Binomial.fitted(lin_pred) Fitted values based on linear predictors lin_pred. Parameters: lin_pred : array Values of the linear predictor of the model. dot(X,beta) in a classical linear model. Returns: mu : array The mean response variables given by the inverse of the link function.

statsmodels.genmod.generalized_linear_model.GLM.score_test GLM.score_test(params_constrained, k_constraints=None, exog_extra=None, observed=True) [source] score test for restrictions or for omitted variables The covariance matrix for the score is based on the Hessian, i.e. observed information matrix or optionally on the expected information matrix.. Parameters: params_constrained : array_like estimated parameter of the restricted model. This can be the parameter estimate for the current w

statsmodels.robust.norms.RobustNorm class statsmodels.robust.norms.RobustNorm [source] The parent class for the norms used for robust regression. Lays out the methods expected of the robust norms to be used by statsmodels.RLM. Parameters: None : : Some subclasses have optional tuning constants. See also statsmodels.rlm, and Notes Currently only M-estimators are available. References PJ Huber. ?Robust Statistics? John Wiley and Sons, Inc., New York, 1981. DC Montgomery, EA Peck. ?Introd

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.expect TransfTwo_gen.expect(func=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Calculate expected value of a function with respect to the distribution. The expected value of a function f(x) with respect to a distribution dist is defined as: ubound E[x] = Integral(f(x) * dist.pdf(x)) lbound Parameters: func : callable, optional Function for which integral is calculated. Takes only one

statsmodels.stats.power.FTestAnovaPower.solve_power FTestAnovaPower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, k_groups=2) [source] solve for any one parameter of the power of a F-test for the one sample F-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or floa

statsmodels.sandbox.distributions.extras.NormExpan_gen.fit NormExpan_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 parameters