lmrob.fit.RdCompute 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, bare.only = FALSE)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.
defunct.
logical indicating if the result should be
return()ed after the bare computation steps are done.
Useful, e.g., when you only need the coefficients.
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.
A list with components (some missing in case bare.only is true)
\(X \beta\), 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).