<!doctype s-function-doc system "s-function-doc.dtd" [
<!entity % S-OLD "INCLUDE">
]
>
<s-function-doc>
<s-topics>
   <s-topic>lmRob.fit.compute</s-topic>
</s-topics>
<s-title>
Fit a Robust Linear Model 
</s-title>
<s-description>
Fits a robust linear model with high breakdown point and high efficiency  
estimates. This is used by <s-expression>lmRob</s-expression>, but not supposed to be called by the 
users directly.  
</s-description>
<s-usage>
<s-old-style-usage>
lmRob.fit.compute(x2, y, x1=NULL, x1.idx=NULL, nrep=NULL,  
    robust.control=lmRob.robust.control(...), genetic.control=NULL, ...) 
</s-old-style-usage>
</s-usage>
<s-args-required>
<s-arg name="x2">
numeric vector or matrix for the continuous predictors. 
</s-arg>
<s-arg name="y">
numeric vector for the response in a linear model. 
</s-arg>
</s-args-required>
<s-args-optional>
<s-arg name="x1 ">
numeric vector or matrix for the discrete predictors.  
</s-arg>
<s-arg name="x1.idx">
numeric vector giving the index of <s-expression>x1</s-expression> as column numbers of the whole 
predictor matrix. 
</s-arg>
<s-arg name="nrep">
the number of random subsamples to be drawn. If <s-expression>"Exhaustive"</s-expression> resampling  
is being used, the value of <s-expression>nrep</s-expression> is ignored. 
</s-arg>
<s-arg name="robust.control">
a list of control parameters to be used in the numerical algorithms. See  
<s-expression>lmRob.robust.control()</s-expression> for the possible control parameters and their  
default settings. 
</s-arg>
<s-arg name="genetic.control">
a list of control parameters to be used in the genetic algorithm, if  
chosen. See <s-expression>lmRob.genetic.control()</s-expression> for the possible control parameters.  
</s-arg>
</s-args-optional>
<s-value>
an object of class <s-expression>"lmRob"</s-expression>. See <s-expression>lmRob.object</s-expression> for a complete  
description of the object returned. 
</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>
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. 
<br><br>
Yohai, V. (1988). High breakdown-point and high efficiency estimates for  
regression,  
<it>Annals of Statistics, </it>
15, 642-665. 
<br><br>
Yohai, V., Stahel, W. A., and Zamar, R. H. (1991). A procedure for robust  
estimation and inference in linear regression, in Stahel, W. A. and  
Weisberg, S. W., Eds.,  
<it>Directions in robust statistics and diagnostics, Part II. </it>
Springer-Verlag. 
</s-section>
<s-see>
<s-function name="lmRob.sgm">lmRob</s-function>,
<s-function name="lmRob.genetic.control.sgm">lmRob.genetic.control</s-function>,
<s-function name="lmRob.genetic.control.sgm">lmRob.genetic.control</s-function>,
<s-function name="lmRob.robust.control.sgm">lmRob.robust.control</s-function>. </s-see>
<s-keywords>
<s-keyword>robust</s-keyword>
<s-keyword>regression</s-keyword>
<s-keyword>models</s-keyword>
<s-keyword>librobust</s-keyword>
</s-keywords>
<s-docclass>
function
</s-docclass>
</s-function-doc>
