statsmodels.graphics.plot_grids.scatter_ellipse statsmodels.graphics.plot_grids.scatter_ellipse(data, level=0.9, varnames=None, ell_kwds=None, plot_kwds=None, add_titles=False, keep_ticks=False, fig=None) [source] Create a grid of scatter plots with confidence ellipses. ell_kwds, plot_kdes not used yet looks ok with 5 or 6 variables, too crowded with 8, too empty with 1 Parameters: data : array_like Input data. level : scalar, optional Default is 0.9. varnames : list of str, optional V

statsmodels.genmod.families.links.Link.inverse_deriv Link.inverse_deriv(z) [source] Derivative of the inverse link function g^(-1)(z). Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the link function : Notes This reference implementation gives the correct result but it inefficient, so it can be overriden in subclasses.

statsmodels.regression.linear_model.OLS.initialize OLS.initialize()

statsmodels.sandbox.regression.gmm.IVGMMResults.conf_int IVGMMResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval.

statsmodels.miscmodels.count.PoissonOffsetGMLE.fit PoissonOffsetGMLE.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.discrete.discrete_model.Logit.hessian Logit.hessian(params) [source] Logit model Hessian matrix of the log-likelihood Parameters: params : array-like The parameters of the model Returns: hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at params Notes

statsmodels.genmod.generalized_linear_model.GLMResults.remove_data GLMResults.remove_data() [source] remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time

Weight Functions Andrew?s Wave Hampel 17A Huber?s t Least Squares Ramsay?s Ea Trimmed Mean Tukey?s Biweight

statsmodels.sandbox.regression.gmm.GMM.momcond_mean GMM.momcond_mean(params) [source] mean of moment conditions,

statsmodels.regression.linear_model.OLS.hessian OLS.hessian(params) The Hessian matrix of the model