statsmodels.duration.hazard_regression.PHRegResults.initialize PHRegResults.initialize(model, params, **kwd)

statsmodels.sandbox.stats.multicomp.catstack statsmodels.sandbox.stats.multicomp.catstack(args) [source]

statsmodels.discrete.discrete_model.LogitResults class statsmodels.discrete.discrete_model.LogitResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for Logit Model Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criterion.

statsmodels.tsa.vector_ar.var_model.VARResults.acorr VARResults.acorr(nlags=None) Compute theoretical autocorrelation function Returns: acorr : ndarray (p x k x k)

Linear Mixed Effects Models Linear Mixed Effects models are used for regression analyses involving dependent data. Such data arise when working with longitudinal and other study designs in which multiple observations are made on each subject. Two specific mixed effects models are ?random intercepts models?, where all responses in a single group are additively shifted by a value that is specific to the group, and ?random slopes models?, where the values follow a mean trajectory that is linear in

statsmodels.tsa.arima_model.ARMA.geterrors ARMA.geterrors(params) [source] Get the errors of the ARMA process. Parameters: params : array-like The fitted ARMA parameters order : array-like 3 item iterable, with the number of AR, MA, and exogenous parameters, including the trend

statsmodels.sandbox.regression.gmm.LinearIVGMM.momcond LinearIVGMM.momcond(params)

statsmodels.regression.linear_model.WLS.whiten WLS.whiten(X) [source] Whitener for WLS model, multiplies each column by sqrt(self.weights) Parameters: X : array-like Data to be whitened Returns: sqrt(weights)*X :

statsmodels.sandbox.regression.gmm.IVRegressionResults.summary2 IVRegressionResults.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 replac

statsmodels.discrete.discrete_model.MultinomialResults.f_test MultinomialResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypothese