statsmodels.discrete.discrete_model.DiscreteModel.fit_regularized DiscreteModel.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=True, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, qc_verbose=False, **kwargs) [source] Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, o

statsmodels.genmod.generalized_linear_model.GLMResults.initialize GLMResults.initialize(model, params, **kwd)

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.stats ExpTransf_gen.stats(*args, **kwds) Some statistics of the given 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 (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which mo

statsmodels.sandbox.distributions.extras.pdf_moments statsmodels.sandbox.distributions.extras.pdf_moments(cnt) [source] Return the Gaussian expanded pdf function given the list of central moments (first one is mean). Changed so it works only if four arguments are given. Uses explicit formula, not loop. Notes This implements a Gram-Charlier expansion of the normal distribution where the first 2 moments coincide with those of the normal distribution but skew and kurtosis can deviate from it. I

statsmodels.regression.quantile_regression.QuantRegResults.prsquared static QuantRegResults.prsquared() [source]

statsmodels.discrete.discrete_model.CountResults.pvalues static CountResults.pvalues()

statsmodels.graphics.correlation.plot_corr_grid statsmodels.graphics.correlation.plot_corr_grid(dcorrs, titles=None, ncols=None, normcolor=False, xnames=None, ynames=None, fig=None, cmap='RdYlBu_r') [source] Create a grid of correlation plots. The individual correlation plots are assumed to all have the same variables, axis labels can be specified only once. Parameters: dcorrs : list or iterable of ndarrays List of correlation matrices. titles : list of str, optional List of titles for t

statsmodels.sandbox.regression.gmm.GMMResults.cov_params GMMResults.cov_params(**kwds) [source]

statsmodels.sandbox.regression.gmm.IVRegressionResults.compare_lr_test IVRegressionResults.compare_lr_test(restricted, large_sample=False) Likelihood ratio 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. large_sample : bool Flag indic

statsmodels.nonparametric.bandwidths.select_bandwidth statsmodels.nonparametric.bandwidths.select_bandwidth(x, bw, kernel) [source] Selects bandwidth for a selection rule bw this is a wrapper around existing bandwidth selection rules Parameters: x : array-like Array for which to get the bandwidth bw : string name of bandwidth selection rule, currently supported are: normal_reference, scott, silverman kernel : not used yet Returns: bw : float The estimate of the bandwidth