Compute MM-type estimators of regression: An S-estimator is used as starting value, and an M-estimator with fixed scale and redescending psi-function is used from there. Optionally a D-step (Design Adaptive Scale estimate) as well as a second M-step is calculated.
lmrob.fit(x, y, control, init = NULL, mf = NULL)design matrix (\(n \times p\)) typically including a
column of 1s for the intercept.
numeric response vector (of length \(n\)).
a list of control parameters as returned
by lmrob.control, used for both the initial S-estimate
and the subsequent M- and D-estimates.
optional list of initial estimates. See
Details.
unused and deprecated.
A list with components
\(X \beta\), i.e., X %*% coefficients.
the raw residuals, y - fitted.values
robustness weights derived from the final M-estimator residuals (even when not converged).
n - rank
estimated regression coefficient vector
the robustly estimated error standard deviation
variance-covariance matrix of coefficients, if the
RWLS iterations have converged (and control$cov is not "none").
logical indicating if the RWLS iterations have converged.
the whole initial S-estimator result, including its own
converged flag, see lmrob.S (only for MM-estimates).
A similar list that contains the results of intermediate estimates (not for MM-estimates).
This function is the basic fitting function for MM-type estimation,
called by lmrob and typically not to be used on its own.
If given, init must be a list of initial estimates containing
at least the initial coefficients and scale as coefficients and
scale. Otherwise it calls lmrob.S(..) and uses it
as initial estimator.