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# scikits.statsmodels.rlm.RLM.fit

`RLM.``fit`(maxiter=50, tol=1e-008, scale_est='mad', init=None, cov='H1', update_scale=True, conv='dev')

  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), “resids” (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’, ‘stand_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 use ‘stand_mad’ for the median absolute deviation standardized around the median and ‘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 models.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 scikits.statsmodels.rlm.RLMresults

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