statsmodels.sandbox.distributions.extras.ACSkewT_gen.logpdf ACSkewT_gen.logpdf(x, *args, **kwds) Log of the probability density function at x of the given RV. This uses a more numerically accurate calculation if available. Parameters: x : array_like quantiles 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 sca

statsmodels.genmod.families.family.Gaussian.deviance Gaussian.deviance(endog, mu, scale=1.0) [source] Gaussian deviance function Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns: deviance : float The deviance function at (endog,mu) as defined below. Notes deviance = sum((endog-mu)**2)

statsmodels.sandbox.stats.multicomp.TukeyHSDResults.plot_simultaneous TukeyHSDResults.plot_simultaneous(comparison_name=None, ax=None, figsize=(10, 6), xlabel=None, ylabel=None) [source] Plot a universal confidence interval of each group mean Visiualize significant differences in a plot with one confidence interval per group instead of all pairwise confidence intervals. Parameters: comparison_name : string, optional if provided, plot_intervals will color code all groups that are significan

statsmodels.discrete.discrete_model.CountResults.f_test CountResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test ca

statsmodels.sandbox.regression.gmm.IVGMMResults.load classmethod IVGMMResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.genmod.generalized_linear_model.GLMResults.pearson_chi2 static GLMResults.pearson_chi2() [source]

statsmodels.genmod.generalized_linear_model.GLMResults.t_test GLMResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple

statsmodels.discrete.discrete_model.MultinomialResults.fittedvalues static MultinomialResults.fittedvalues()

statsmodels.genmod.generalized_estimating_equations.GEEResults.bse static GEEResults.bse() [source]

statsmodels.regression.mixed_linear_model.MixedLM.fit_regularized MixedLM.fit_regularized(start_params=None, method='l1', alpha=0, ceps=0.0001, ptol=1e-06, maxit=200, **fit_kwargs) [source] Fit a model in which the fixed effects parameters are penalized. The dependence parameters are held fixed at their estimated values in the unpenalized model. Parameters: method : string of Penalty object Method for regularization. If a string, must be ?l1?. alpha : array-like Scalar or vector of penal