statsmodels.iolib.summary2.Summary.add_dict Summary.add_dict(d, ncols=2, align='l', float_format='%.4f') [source] Add the contents of a Dict to summary table Parameters: d : dict Keys and values are automatically coerced to strings with str(). Users are encouraged to format them before using add_dict. ncols: int : Number of columns of the output table align : string Data alignment (l/c/r)

statsmodels.emplike.descriptive.DescStatMV.mv_test_mean DescStatMV.mv_test_mean(mu_array, return_weights=False) [source] Returns -2 x log likelihood and the p-value for a multivariate hypothesis test of the mean Parameters: mu_array : 1d array Hypothesized values for the mean. Must have same number of elements as columns in endog return_weights : bool If True, returns the weights that maximize the likelihood of mu_array. Default is False. Returns: test_results : tuple The log-likelih

statsmodels.genmod.families.links.cauchy.inverse cauchy.inverse(z) The inverse of the CDF link Parameters: z : array-like The value of the inverse of the link function at p Returns: p : array Mean probabilities. The value of the inverse of CDF link of z Notes g^(-1)(z) = dbn.cdf(z)

statsmodels.genmod.families.links.CLogLog.inverse_deriv CLogLog.inverse_deriv(z) [source] Derivative of the inverse of the C-Log-Log transform link function Parameters: z : array-like The value of the inverse of the CLogLog link function at p Returns: The derivative of the inverse of the CLogLog link function :

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.tsa.vector_ar.var_model.VARResults.is_stable VARResults.is_stable(verbose=False) Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle

statsmodels.sandbox.regression.gmm.GMMResults.compare_j GMMResults.compare_j(other) [source] overidentification test for comparing two nested gmm estimates This assumes that some moment restrictions have been dropped in one of the GMM estimates relative to the other. Not tested yet We are comparing two separately estimated models, that use different weighting matrices. It is not guaranteed that the resulting difference is positive. TODO: Check in which cases Stata programs use the same weigt

statsmodels.miscmodels.count.PoissonOffsetGMLE.from_formula classmethod PoissonOffsetGMLE.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.Data

statsmodels.miscmodels.count.PoissonZiGMLE.expandparams PoissonZiGMLE.expandparams(params) expand to full parameter array when some parameters are fixed Parameters: params : array reduced parameter array Returns: paramsfull : array expanded parameter array where fixed parameters are included Notes Calling this requires that self.fixed_params and self.fixed_paramsmask are defined. developer notes: This can be used in the log-likelihood to ... this could also be replaced by a more gene

statsmodels.stats.gof.gof_chisquare_discrete statsmodels.stats.gof.gof_chisquare_discrete(distfn, arg, rvs, alpha, msg) [source] perform chisquare test for random sample of a discrete distribution Parameters: distname : string name of distribution function arg : sequence parameters of distribution alpha : float significance level, threshold for p-value Returns: result : bool 0 if test passes, 1 if test fails Notes originally written for scipy.stats test suite, still needs to be c