statsmodels.genmod.generalized_linear_model.GLMResults.save GLMResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If re

statsmodels.genmod.generalized_linear_model.GLMResults.summary GLMResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors 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 Returns: smr

statsmodels.genmod.generalized_linear_model.GLMResults.remove_data GLMResults.remove_data() [source] 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

statsmodels.genmod.generalized_linear_model.GLMResults.predict GLMResults.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 pass a dat

statsmodels.genmod.generalized_linear_model.GLMResults.normalized_cov_params GLMResults.normalized_cov_params()

statsmodels.genmod.generalized_linear_model.GLMResults.load classmethod GLMResults.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.genmod.generalized_linear_model.GLMResults.f_test GLMResults.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 hypotheses to tes

statsmodels.genmod.generalized_linear_model.GLMResults.initialize GLMResults.initialize(model, params, **kwd)

statsmodels.genmod.generalized_linear_model.GLMResults.cov_params GLMResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-li

statsmodels.genmod.generalized_linear_model.GLM.score_obs GLM.score_obs(params, scale=None) [source] score first derivative of the loglikelihood for each observation. Parameters: params : ndarray parameter at which score is evaluated scale : None or float If scale is None, then the default scale will be calculated. Default scale is defined by self.scaletype and set in fit. If scale is not None, then it is used as a fixed scale. Returns: score_obs : ndarray, 2d The first derivative of