<!doctype s-function-doc system "s-function-doc.dtd" [
<!entity % S-OLD "INCLUDE">
]
>
<s-function-doc>
<s-topics>
   <s-topic>aovRob</s-topic>
</s-topics>

<s-title>
Fit a Robust Analysis of Variance Model 
</s-title>

<s-description>
Returns an object of class <s-expression>aovRob</s-expression> that 
contains the robust analysis of variance for the specified model. 
</s-description>

<s-usage>
<s-old-style-usage>
aovRob(formula, data = sys.parent(), contrasts = NULL, ...) 
</s-old-style-usage>
</s-usage>

<s-args-required>
<s-arg name="formula">
formula or terms describing the model. 
</s-arg>
</s-args-required>

<s-args-optional>
<s-arg name="data">
if supplied, a data frame in which the objects named in the 
<s-expression>formula</s-expression> are to be found. If
<s-expression>data</s-expression> is omitted, the current search list is
used to find the objects in <s-expression>formula</s-expression>;
frequently, a data frame will have been attached.
</s-arg>

<s-arg name="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. 
</s-arg>

<s-arg name="...">
arguments to be passed to <s-expression>lmRob</s-expression>. In 
particular, the argument 
<s-expression>na.action</s-expression> can be a function that filters 
missing values from a data   
frame, and <s-expression>subset</s-expression> can be a vector for 
selecting observations (rows)  
from a data frame. 
</s-arg>
</s-args-optional>

<s-value>
an object of class <s-expression>"aovRob"</s-expression>. The following
components must be included in a legitimate
<s-expression>"aovRob"</s-expression> object:

<s-return-component name="coefficients">
the coefficients of the robust fit of the response(s) on the model  
matrix.  
</s-return-component>

<s-return-component name="residuals">
the residuals from the fit. 
</s-return-component>

<s-return-component name="fitted.values">
the fitted values for the model. 
</s-return-component>

<s-return-component name="rank">
the computed rank (number of estimable effects) for the model. 
</s-return-component>

<s-return-component name="terms">
an object of mode <s-expression>"expression"</s-expression> and 
class <s-expression>"terms"</s-expression> summarizing  
the formula. This is used by various methods, but not typically of  
direct relevance to users. 
</s-return-component>

<s-return-component name="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. 
</s-return-component>
</s-value>

<s-section name=" REFERENCES">

Gervini, D., and Yohai, V. J. (1999). A class of robust and fully 
efficient regression estimates, mimeo, Universidad de Buenos Aires. 
<br><br>
Maronna, R. A., and Yohai, V. J. (1999). Robust regression with both 
continuous and categorical predictors, mimeo, Universidad de Buenos Aires. 
</s-section>

<s-see>
<s-function name="lmRob.sgm">lmRob</s-function>,
<s-function name="lmRob.object.sgm">lmRob.object</s-function>. 
</s-see>

<s-examples>
<s-example type = text>
aovRob(Yield~Temp+Conc+Cat, data=catalyst) 
</s-example>
</s-examples>

<s-keywords>
<s-keyword>robust</s-keyword>
<s-keyword>models</s-keyword>
<s-keyword>design</s-keyword>
<s-keyword>librobust</s-keyword>
</s-keywords>

<s-docclass>
function
</s-docclass>

</s-function-doc>
