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

statsmodels.iolib.table.SimpleTable.as_html SimpleTable.as_html(**fmt_dict) [source] Return string. This is the default formatter for HTML tables. An HTML table formatter must accept as arguments a table and a format dictionary.

statsmodels.miscmodels.count.PoissonGMLE.jac PoissonGMLE.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.sandbox.distributions.transformed.LogTransf_gen.nnlf LogTransf_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.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]