statsmodels.genmod.generalized_estimating_equations.GEEResults.split_centered_resid static GEEResults.split_centered_resid() Returns the residuals centered within each group. The residuals are returned as a list of arrays containing the centered residuals for each cluster.

statsmodels.discrete.discrete_model.Logit.loglike Logit.loglike(params) [source] Log-likelihood of logit model. Parameters: params : array-like The parameters of the logit model. Returns: loglike : float The log-likelihood function of the model evaluated at params. See notes. Notes Where . This simplification comes from the fact that the logistic distribution is symmetric.

statsmodels.regression.mixed_linear_model.MixedLMResults.random_effects_cov static MixedLMResults.random_effects_cov() [source] Returns the conditional covariance matrix of the random effects for each group given the data. Returns: random_effects_cov : dict A dictionary mapping the distinct values of the group variable to the conditional covariance matrix of the random effects given the data.

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

statsmodels.sandbox.regression.gmm.IV2SLS class statsmodels.sandbox.regression.gmm.IV2SLS(endog, exog, instrument=None) [source] Class for instrumental variables estimation using Two-Stage Least-Squares Parameters: endog: array 1d : endogenous variable exog : array explanatory variables instruments : array instruments for explanatory variables, needs to contain those exog variables that are not instrumented out Notes All variables in exog are instrumented in the calculations. If vari

statsmodels.genmod.families.family.Binomial.resid_dev Binomial.resid_dev(endog, mu, scale=1.0) [source] Binomial deviance residuals Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional argument to divide the residuals by scale Returns: resid_dev : array Deviance residuals as defined below Notes If endog is binary: resid_dev = sign(endog-mu)*sqrt(-2*log(I_one*mu + I_zero*(1-mu))) where I_one i

statsmodels.regression.linear_model.OLSResults.summary2 OLSResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental summary function to summarize the regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default ti

statsmodels.duration.hazard_regression.PHRegResults.baseline_cumulative_hazard static PHRegResults.baseline_cumulative_hazard() [source] A list (corresponding to the strata) containing the baseline cumulative hazard function evaluated at the event points.

statsmodels.duration.hazard_regression.PHRegResults.weighted_covariate_averages static PHRegResults.weighted_covariate_averages() [source] The average covariate values within the at-risk set at each event time point, weighted by hazard.

statsmodels.regression.mixed_linear_model.MixedLM.steepest_ascent MixedLM.steepest_ascent(params, n_iter) [source] Take steepest ascent steps to increase the log-likelihood function. Parameters: params : array-like The starting point of the optimization. n_iter: non-negative integer : Return once this number of iterations have occured. Returns: A MixedLMParameters object containing the final value of the : optimization. :