glmRob
which is a
Robust Generalized Linear Model fit.
glmRob(formula = formula(data), family = binomial, data = sys.parent(), subset, na.action = na.fail, start = eta, fit.method = "cubif", model = F, x = F, y = T, contrasts = NULL, cubif.control = glmRob.cubif.control(...), mallows.control = glmRob.mallows.control(...), misclass.control = glmRob.misclass.control(...), method = "glmRob.fit", estim = "mcd", robust.cov.control = covRob.control(estim = estim, quan = 0.75, ...), ...)
lm
and
formula
for
details.
binomial
and
poisson
are implemented. See the
documentation of
glm
for details.
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.
"cubif"
, Mallow's leverage
downweighting estimates are fit with
fit.method =
"mallows"
and
"misclass"
fits a consistent estimate based on the misclassification model.
The last two estimators are only defined for logistic regression
models with a Bernoulli response variable.
TRUE
, the
model.frame
is returned. If this argument
is itself a
model.frame
, then the
formula
and
data
arguments are
ignored, and
model
is used to define the model.
TRUE
, the
model.matrix
is returned.
TRUE
, the response variable
is returned (default is
TRUE
).
glmRob.cubif.control
for their
names and default values. These can also be set as arguments of
glmRob
itself.
glmRob.mallows.control
for their names and default
values. These can also be set as arguments of
glmRob
itself.
glmRob.misclass.control
for their names
and default values. These can also be set as arguments of
glmRob
itself.
glmRob.cubif.control
,
glmRob.mallows.control
or
glmRob.misclass.control
for details.
glmRob
that contains the
Robust Generalized Linear Model fit.
Copas, J. B. (1988). Binary Regression Models for
Contaminated Data. JRSS \fB50\fR, 225-265.
Kunsch, L., Stefanski L. and Carroll, R. (1989).
Conditionally Unbiased Bounded-Influence Estimation in General
Regression Models, with Applications to Generalized Linear
Models.
JASA \fB84\fR, 460-466.
Carroll, R. J. and Pederson, S. (1993). On Robustness in
the Logistic Regression Model. JRSS \fB55\fR, 693-706.
Marazzi, A. (1993).
Algorithms, routines and S functions for robust statistics.
Wadsworth & Brooks/Cole, Pacific Grove, CA.
glmRob(sumY~Age10+Base4*Trt, family=poisson, data=breslow.dat, fit.method = "cubif")