statsmodels.regression.mixed_linear_model.MixedLMResults.save MixedLMResults.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

statsmodels.regression.mixed_linear_model.MixedLMResults.predict MixedLMResults.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

statsmodels.regression.mixed_linear_model.MixedLMResults.profile_re MixedLMResults.profile_re(re_ix, num_low=5, dist_low=1.0, num_high=5, dist_high=1.0) [source] Calculate a series of values along a 1-dimensional profile likelihood. Parameters: re_ix : integer The index of the variance parameter for which to construct a profile likelihood. num_low : integer The number of points at which to calculate the likelihood below the MLE of the parameter of interest. dist_low : float The distanc

statsmodels.regression.mixed_linear_model.MixedLMResults.normalized_cov_params MixedLMResults.normalized_cov_params()

statsmodels.regression.mixed_linear_model.MixedLMResults.f_test MixedLMResults.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

statsmodels.regression.mixed_linear_model.MixedLMResults.load classmethod MixedLMResults.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.regression.mixed_linear_model.MixedLMResults.initialize MixedLMResults.initialize(model, params, **kwd)

statsmodels.regression.mixed_linear_model.MixedLMResults.conf_int MixedLMResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence

statsmodels.regression.mixed_linear_model.MixedLMResults.cov_params MixedLMResults.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 : ar

statsmodels.regression.mixed_linear_model.MixedLM.steepest_ascent MixedLM.steepest_ascent(params, n_iter) [source] Take steepest ascent steps to increase the log-likelihood function. Parameters: params : array-like The starting point of the optimization. n_iter: non-negative integer : Return once this number of iterations have occured. Returns: A MixedLMParameters object containing the final value of the : optimization. :