lmrob.fit {robustbase} | R Documentation |
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)
x |
design matrix (n x p) typically including a
column of |
y |
numeric response vector (of length n). |
control |
A list of control parameters as returned
by |
This function is the basic fitting function for MM-type estimation,
called by lmrob
and typically not to be used on its own.
It calls lmrob.S(..)
and uses it as initial estimator.
Note that by default the inference used (covariance matrix) depends
crucially on the S-estimator used, and hence it is currently no longer
possible to specify the S-estimator at this level.
A list with components
fitted.values |
X beta , i.e. |
residuals |
the raw residuals, |
weights |
robustness weights derived from the final M-estimator residuals (even when not converged). |
rank |
|
degree.freedom |
|
coefficients |
estimated regression coefficient vector |
scale |
the robustly estimated error standard deviation |
cov |
variance-covariance matrix of |
control |
iter |
converged |
logical indicating if the RWLS iterations have converged. |
init.S |
the whole initial S-estimator result, including its own
|
init |
A similar list that contains the results of intermediate estimates (not for MM-estimates). |
Matias Salibian-Barrera, Martin Maechler and Manuel Koller
lmrob
,
lmrob..M..fit
,
lmrob..D..fit
,
lmrob.S