statsmodels.nonparametric.kde.KDEUnivariate.entropy static KDEUnivariate.entropy() [source] Returns the differential entropy evaluated at the support Notes Will not work if fit has not been called. 1e-12 is added to each probability to ensure that log(0) is not called.

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

statsmodels.tsa.arima_model.ARIMA.loglike_kalman ARIMA.loglike_kalman(params, set_sigma2=True) Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.

statsmodels.discrete.discrete_model.MNLogit.fit MNLogit.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str, optional The metho

statsmodels.regression.quantile_regression.QuantRegResults.cov_HC2 static QuantRegResults.cov_HC2() See statsmodels.RegressionResults

statsmodels.discrete.discrete_model.LogitResults.bic static LogitResults.bic()

statsmodels.discrete.discrete_model.MNLogit class statsmodels.discrete.discrete_model.MNLogit(endog, exog, **kwargs) [source] Multinomial logit model Parameters: endog : array-like endog is an 1-d vector of the endogenous response. endog can contain strings, ints, or floats. Note that if it contains strings, every distinct string will be a category. No stripping of whitespace is done. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regre

statsmodels.discrete.discrete_model.LogitResults.fittedvalues static LogitResults.fittedvalues()

statsmodels.genmod.cov_struct.Exchangeable.covariance_matrix_solve Exchangeable.covariance_matrix_solve(expval, index, stdev, rhs) [source] Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each obse

statsmodels.stats.inter_rater.cohens_kappa statsmodels.stats.inter_rater.cohens_kappa(table, weights=None, return_results=True, wt=None) [source] Compute Cohen?s kappa with variance and equal-zero test Parameters: table : array_like, 2-Dim square array with results of two raters, one rater in rows, second rater in columns weights : array_like The interpretation of weights depends on the wt argument. If both are None, then the simple kappa is computed. see wt for the case when wt is not N