RLM.fit()

statsmodels.robust.robust_linear_model.RLM.fit

RLM.fit(maxiter=50, tol=1e-08, scale_est='mad', init=None, cov='H1', update_scale=True, conv='dev') [source]

Fits the model using iteratively reweighted least squares.

The IRLS routine runs until the specified objective converges to tol or maxiter has been reached.

Parameters:

conv : string Indicates the convergence criteria. Available options are ?coefs? (the coefficients), ?weights? (the weights in the iteration), ?sresid? (the standardized residuals), and ?dev? (the un-normalized log-likelihood for the M estimator). The default is ?dev?. cov : string, optional ?H1?, ?H2?, or ?H3? Indicates how the covariance matrix is estimated. Default is ?H1?. See rlm.RLMResults for more information. init : string Specifies method for the initial estimates of the parameters. Default is None, which means that the least squares estimate is used. Currently it is the only available choice. maxiter : int The maximum number of iterations to try. Default is 50. scale_est : string or HuberScale() ?mad? or HuberScale() Indicates the estimate to use for scaling the weights in the IRLS. The default is ?mad? (median absolute deviation. Other options are ?HuberScale? for Huber?s proposal 2. Huber?s proposal 2 has optional keyword arguments d, tol, and maxiter for specifying the tuning constant, the convergence tolerance, and the maximum number of iterations. See statsmodels.robust.scale for more information. tol : float The convergence tolerance of the estimate. Default is 1e-8. update_scale : Bool If update_scale is False then the scale estimate for the weights is held constant over the iteration. Otherwise, it is updated for each fit in the iteration. Default is True.

Returns:

results : object statsmodels.rlm.RLMresults