statsmodels.genmod.generalized_linear_model.GLMResults.bse static GLMResults.bse()

statsmodels.miscmodels.count.PoissonOffsetGMLE.loglike PoissonOffsetGMLE.loglike(params)

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

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

statsmodels.sandbox.distributions.extras.NormExpan_gen.nnlf NormExpan_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.sandbox.distributions.transformed.LogTransf_gen.entropy LogTransf_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.miscmodels.tmodel.TLinearModel.fit TLinearModel.fit(start_params=None, method='nm', maxiter=500, full_output=1, disp=1, callback=None, retall=0, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.LikelihoodModel.fit

statsmodels.genmod.cov_struct.Nested.summary Nested.summary() [source] Returns a summary string describing the state of the dependence structure.