statsmodels.genmod.cov_struct.GlobalOddsRatio.update GlobalOddsRatio.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.genmod.cov_struct.GlobalOddsRatio.pooled_odds_ratio GlobalOddsRatio.pooled_odds_ratio(tables) [source] Returns the pooled odds ratio for a list of 2x2 tables. The pooled odds ratio is the inverse variance weighted average of the sample odds ratios of the tables.

statsmodels.genmod.cov_struct.GlobalOddsRatio.observed_crude_oddsratio GlobalOddsRatio.observed_crude_oddsratio() [source] To obtain the crude (global) odds ratio, first pool all binary indicators corresponding to a given pair of cut points (c,c?), then calculate the odds ratio for this 2x2 table. The crude odds ratio is the inverse variance weighted average of these odds ratios. Since the covariate effects are ignored, this OR will generally be greater than the stratified OR.

statsmodels.genmod.cov_struct.GlobalOddsRatio.initialize GlobalOddsRatio.initialize(model) [source]

statsmodels.genmod.cov_struct.GlobalOddsRatio.get_eyy GlobalOddsRatio.get_eyy(endog_expval, index) [source] Returns a matrix V such that V[i,j] is the joint probability that endog[i] = 1 and endog[j] = 1, based on the marginal probabilities of endog and the global odds ratio current_or.

statsmodels.genmod.cov_struct.GlobalOddsRatio.covariance_matrix GlobalOddsRatio.covariance_matrix(expected_value, index) [source] Returns the working covariance or correlation matrix for a given cluster of data. Parameters: endog_expval: array-like : The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer : The index of the cluster for which the covariane or correlation matrix will be returned Returns: M: matrix : Th

statsmodels.genmod.cov_struct.GlobalOddsRatio.covariance_matrix_solve GlobalOddsRatio.covariance_matrix_solve(expval, index, stdev, rhs) 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 observa

statsmodels.genmod.generalized_linear_model.GLMResults.summary2 GLMResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') [source] Experimental summary for regression Results Parameters: yname : string Name of the dependent variable (optional) xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) title : string, optional Title for the top table. If not None, then this replaces the default title

statsmodels.genmod.generalized_linear_model.GLMResults.t_test GLMResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple

statsmodels.genmod.generalized_linear_model.GLMResults.wald_test GLMResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. 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 hypotheses to test can be given as a string. See the examples. tuple :