statsmodels.robust.robust_linear_model.RLMResults.sresid static RLMResults.sresid() [source]

statsmodels.duration.hazard_regression.PHRegResults.conf_int PHRegResults.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_interv

statsmodels.stats.outliers_influence.OLSInfluence.sigma2_not_obsi static OLSInfluence.sigma2_not_obsi() [source] (cached attribute) error variance for all LOOO regressions This is ?mse_resid? from each auxiliary regression. uses results from leave-one-observation-out loop

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.discrete.discrete_model.ProbitResults.resid_generalized static ProbitResults.resid_generalized() [source] Generalized residuals Notes The generalized residuals for the Probit model are defined

statsmodels.stats.proportion.proportion_effectsize statsmodels.stats.proportion.proportion_effectsize(prop1, prop2, method='normal') [source] effect size for a test comparing two proportions for use in power function Parameters: prop1, prop2: float or array_like : Returns: es : float or ndarray effect size for (transformed) prop1 - prop2 Notes only method=?normal? is implemented to match pwr.p2.test see http://www.statmethods.net/stats/power.html Effect size for normal is defined as 2

statsmodels.robust.robust_linear_model.RLMResults.save RLMResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove_

statsmodels.duration.hazard_regression.PHRegResults.summary PHRegResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the proportional hazards regression results. Parameters: yname : string, optional Default is y xname : list of strings, optional Default is x# 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 interval

statsmodels.genmod.generalized_estimating_equations.GEEResults.tvalues static GEEResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.sandbox.distributions.transformed.LogTransf_gen.rvs LogTransf_gen.rvs(*args, **kwds) Random variates of given type. 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). size : int or tuple of ints, optional Defining number of random variates (default=1). Retu