statsmodels.regression.linear_model.OLS.loglike OLS.loglike(params) [source] The likelihood function for the clasical OLS model. Parameters: params : array-like The coefficients with which to estimate the log-likelihood. Returns: The concentrated likelihood function evaluated at params. :

statsmodels.discrete.discrete_model.MultinomialResults.llr static MultinomialResults.llr()

statsmodels.duration.hazard_regression.PHRegResults.remove_data PHRegResults.remove_data() 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 an attrib

statsmodels.discrete.discrete_model.MultinomialResults.predict MultinomialResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pa

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.loglike classmethod KalmanFilter.loglike(params, arma_model, set_sigma2=True) [source] The loglikelihood for an ARMA model using the Kalman Filter recursions. Parameters: params : array The coefficients of the ARMA model, assumed to be in the order of trend variables and k exogenous coefficients, the p AR coefficients, then the q MA coefficients. arma_model : statsmodels.tsa.arima.ARMA instance A reference to the ARMA model instance. set

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.std TransfTwo_gen.std(*args, **kwds) Standard deviation of the distribution. 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) Returns: std : float standard deviation of the distribution

statsmodels.stats.weightstats.DescrStatsW.corrcoef static DescrStatsW.corrcoef() [source] weighted correlation with default ddof assumes variables in columns and observations in rows

statsmodels.tsa.vector_ar.var_model.VARResults.test_causality VARResults.test_causality(equation, variables, kind='f', signif=0.05, verbose=True) [source] Compute test statistic for null hypothesis of Granger-noncausality, general function to test joint Granger-causality of multiple variables Parameters: equation : string or int Equation to test for causality variables : sequence (of strings or ints) List, tuple, etc. of variables to test for Granger-causality kind : {?f?, ?wald?} Perf

statsmodels.miscmodels.count.PoissonZiGMLE.nloglikeobs PoissonZiGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes

statsmodels.tsa.ar_model.ARResults.llf static ARResults.llf()