Fit a Robust Analysis of Variance Model

DESCRIPTION:

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.

SEE ALSO:

, .

EXAMPLES:

aovRob(Yield~Temp+Conc+Cat, data=catalyst)