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’, ‘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.
|