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.genmod.families.links.identity.deriv identity.deriv(p) Derivative of the power transform Parameters: p : array-like Mean parameters Returns: g?(p) : array Derivative of power transform of p Notes g?(p) = power * p`**(`power - 1)

statsmodels.genmod.generalized_linear_model.GLMResults.summary GLMResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns: smr

statsmodels.nonparametric.kernel_density.KDEMultivariate.loo_likelihood KDEMultivariate.loo_likelihood(bw, func= at 0x2ac6543ac488>) [source] Returns the leave-one-out likelihood function. The leave-one-out likelihood function for the unconditional KDE. Parameters: bw: array_like : The value for the bandwidth parameter(s). func: callable, optional : Function to transform the likelihood values (before summing); for the log likelihood, use func=np.log. Default is f(x) = x. Notes The l

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.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

statsmodels.regression.mixed_linear_model.MixedLM.hessian_sqrt MixedLM.hessian_sqrt(params) [source] Returns the Hessian matrix of the log-likelihood evaluated at a given point, calculated with respect to the parameterization in which the random effects covariance matrix is represented through its Cholesky square root. Parameters: params : MixedLMParams or array-like The model parameters. If array-like, must contain packed parameters that are compatible with this model. Returns: The Hes

statsmodels.sandbox.regression.gmm.IVRegressionResults.pvalues static IVRegressionResults.pvalues()