statsmodels.regression.mixed_linear_model.MixedLMResults.remove_data MixedLMResults.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

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.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.normalized_cov_params MixedLMResults.normalized_cov_params()

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.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.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.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.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. :