| lmrob.fit.MM {robustbase} | R Documentation |
Compute MM-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.
lmrob.fit.MM(x, y, control)
x |
design matrix (n x p) typically including a
column of 1s for the intercept. |
y |
numeric response vector (of length n). |
control |
A list of control parameters as returned
by lmrob.control, used for both the initial S-estimate
and the subsequent M-estimate. |
This function is the basic fitting function for MM-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 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. X %*% coefficients. |
residuals |
the raw residuals, y - fitted.values |
weights |
robustness weights derived from the final M-estimator residuals (even when not converged). |
rank |
|
degree.freedom |
n - rank |
coefficients |
estimated regression coefficient vector |
initial.coefficients |
|
scale |
the robustly estimated error standard deviation |
cov |
variance-covariance matrix of coefficients, if the
RWLS iterations have converged, otherwise the vcov-matrix of the
initial estimator. |
control |
|
iter |
|
converged |
logical indicating if the RWLS iterations have converged. |
init.S |
the whole initial S-estimator result, including its own
converged flag, see lmrob.S. |