statsmodels.robust.robust_linear_model.RLMResults.load classmethod RLMResults.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.robust.robust_linear_model.RLMResults.initialize RLMResults.initialize(model, params, **kwd)

statsmodels.robust.robust_linear_model.RLMResults.f_test RLMResults.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 test can

statsmodels.robust.robust_linear_model.RLMResults.cov_params RLMResults.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-like, o

statsmodels.robust.robust_linear_model.RLMResults.conf_int RLMResults.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_interval.

statsmodels.robust.robust_linear_model.RLM.score RLM.score(params) [source]

statsmodels.robust.robust_linear_model.RLM.predict RLM.predict(params, exog=None) [source] Return linear predicted values from a design matrix. Parameters: params : array-like, optional after fit has been called Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns: An array of fitted values : Notes If the model as not yet been fit, params is not optional.

statsmodels.robust.robust_linear_model.RLM.loglike RLM.loglike(params) [source]

statsmodels.robust.robust_linear_model.RLM.initialize RLM.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

statsmodels.robust.robust_linear_model.RLM.information RLM.information(params) [source]