<!doctype s-function-doc system "s-function-doc.dtd" [
<!entity % S-OLD "INCLUDE">
]
>
<s-function-doc>
<s-topics>
   <s-topic>cov.lmRob</s-topic>
</s-topics>
<s-title>
Robust Covariance Matrix 
</s-title>
<s-description>
Returns a robust covariance matrix of the coefficient estimates in a linear  
model, as estimated by <s-expression>lmRob()</s-expression> function. 
</s-description>
<s-usage>
<s-old-style-usage>
cov.lmRob(x) 
</s-old-style-usage>
</s-usage>
<s-args-required>
<s-arg name="x">
an object of class <s-expression>"lmRob"</s-expression>. 
</s-arg>
</s-args-required>
<s-value>
a matrix of robust covariances of the coefficient estimates. 
</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>
Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., and Stahel, W. A. (1986).  
<it>Robust statistics: the approach based on influence functions. </it>
John &#38; Wiley.  
<br><br>
Huber, P. J. (1981).  
<it>Robust statistics. </it>
John &#38; Wiley. 
<br><br>
Marazzi, A. (1993).  
<it>Algorithms, routines, and S functions for robust statistics. </it>
Wadsworth &#38; Brooks/Cole, Pacific Grove, CA. 
<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="cor.lmRob.sgm">cor.lmRob</s-function>,  <s-function name="lmRob.object.sgm">lmRob.object</s-function>.   </s-see>
<s-examples>
<s-example type = text>
stack.df &lt;- data.frame(Loss=stack.loss,stack.x) 
stack.rob &lt;- lmRob(Loss~Air.Flow+Water.Temp+Acid.Conc.,data=stack.df) 
stack.cov &lt;- cov.lmRob(stack.robust) 
</s-example>
</s-examples>
<s-keywords>
<s-keyword>robust</s-keyword>
<s-keyword>librobust</s-keyword>
</s-keywords>
<s-docclass>
function
</s-docclass>
</s-function-doc>
