Returns an object of class
aovRob that
contains the robust analysis of variance for the specified model.
USAGE:
aovRob(formula, data = sys.parent(), contrasts = NULL, ...)
REQUIRED ARGUMENTS:
formula
formula or terms describing the model.
OPTIONAL ARGUMENTS:
data
if supplied, a data frame in which the objects named in the
formula are to be found. If
data is omitted, the current search list is
used to find the objects in
formula;
frequently, a data frame will have been attached.
contrasts
a list of contrasts to be used for some or all of the factors appearing
as variables in the model formula. The names of the list should be the
names of the corresponding variables, and the elements should either be
contrast-type matrices (matrices with as many rows as levels of the
factor and with columns linearly independent of each other and of a
column of one's), or else they should be functions that compute such
contrast matrices.
...
arguments to be passed to
lmRob. In
particular, the argument
na.action can be a function that filters
missing values from a data
frame, and
subset can be a vector for
selecting observations (rows)
from a data frame.
VALUE:
an object of class
"aovRob". The following
components must be included in a legitimate
"aovRob"
object:
coefficients
the coefficients of the robust fit of the response(s) on the model
matrix.
residuals
the residuals from the fit.
fitted.values
the fitted values for the model.
rank
the computed rank (number of estimable effects) for the model.
terms
an object of mode
"expression" and
class
"terms" summarizing
the formula. This is used by various methods, but not typically of
direct relevance to users.
call
an image of the call that produced the object, but with the arguments
all named and with the actual formula included as the formula argument.
REFERENCES:
Gervini, D., and Yohai, V. J. (1999). A class of robust and fully
efficient regression estimates, mimeo, Universidad de Buenos Aires.
Maronna, R. A., and Yohai, V. J. (1999). Robust regression with both
continuous and categorical predictors, mimeo, Universidad de Buenos Aires.