lmRobBI(formula, data=<<see below>>, weights=<<see below>>, subset=<<see below>>, na.action=na.fail, model=F, x=F, y=F, contrasts=NULL, control=lmRobBI.control(), ...)
formula object,
with the response on the left of a
~ operator,
and the terms, separated by
+ operators,
on the right.
data.frame in which to interpret the variables named in the
formula,
or in the
subset and the
weights argument. If this is missing, then the
variables in the
formula should be on the search list. This may also be a
single number to handle some special cases - see below for details.
weights must be the same as the number of observations. The
weights must be nonnegative and it is strongly recommended that they be
strictly positive, since zero weights are ambiguous, compared to use of the
subset argument.
model.frame
after any
subset argument has been used. The default (with
na.fail) is
to create an error if any missing values are found. A possible alternative
is
na.exclude, which deletes observations that contain one or more missing
values.
TRUE, the model frame is returned in component model.
TRUE, the model matrix is returned in component
x.
TRUE, the response is returned in component
y.
lmRobBI.control() for the possible control parameters and their
default settings.
lmRobBI.object for a complete
description of the object returned.
Marazzi, A. (1993). Algorithms, Routines, and S Functions for Robust Statistics. Wadsworth & Brooks/Cole, Pacific Grove, CA.