statsmodels.regression.linear_model.GLS.initialize GLS.initialize()

statsmodels.genmod.families.links.CLogLog.deriv CLogLog.deriv(p) [source] Derivative of C-Log-Log transform link function Parameters: p : array-like Mean parameters Returns: g?(p) : array The derivative of the CLogLog transform link function Notes g?(p) = - 1 / (log(p) * p)

statsmodels.regression.linear_model.OLSResults.pvalues static OLSResults.pvalues()

statsmodels.regression.linear_model.RegressionResults.HC2_se static RegressionResults.HC2_se() [source] See statsmodels.RegressionResults

statsmodels.regression.linear_model.OLSResults.load classmethod OLSResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.tsa.arima_model.ARMAResults.summary2 ARMAResults.summary2(title=None, alpha=0.05, float_format='%.4f') [source] Experimental summary function for ARIMA Results Parameters: 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 float_format: string : print format for floats in parameters summary Returns: smry : Summary instance This holds the summary table and text,

statsmodels.tsa.arima_model.ARIMA.score ARIMA.score(params) Compute the score function at params. Notes This is a numerical approximation.

statsmodels.sandbox.regression.gmm.GMM.gmmobjective_cu GMM.gmmobjective_cu(params, weights_method='cov', wargs=()) [source] objective function for continuously updating GMM minimization Parameters: params : array parameter values at which objective is evaluated Returns: jval : float value of objective function

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.gmmobjective_cu NonlinearIVGMM.gmmobjective_cu(params, weights_method='cov', wargs=()) objective function for continuously updating GMM minimization Parameters: params : array parameter values at which objective is evaluated Returns: jval : float value of objective function

statsmodels.discrete.discrete_model.Probit.from_formula classmethod Probit.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : e