<!doctype s-function-doc system "s-function-doc.dtd" [
<!entity % S-OLD "INCLUDE">
]
>
<s-function-doc>
<s-topics>
   <s-topic>lmRobBI</s-topic>
</s-topics>
<s-title>
Bounded Influence Robust Regression 
</s-title>
<s-description>
Performs bounded influence robust regression.
</s-description>
<s-usage>
<s-old-style-usage>
lmRobBI(formula, data=&lt;&lt;see below&gt;&gt;, weights=&lt;&lt;see below&gt;&gt;,
  subset=&lt;&lt;see below&gt;&gt;, na.action=na.fail, model=F, x=F, y=F,
  contrasts=NULL, control=lmRobBI.control(), ...) 
</s-old-style-usage>
</s-usage>
<s-args-required>
<s-arg name="formula">
a <s-expression>formula</s-expression> object,
with the response on the left of a <s-expression>~</s-expression> operator,
and the terms, separated by <s-expression>+</s-expression> operators,
on the right. 
</s-arg>
</s-args-required>
<s-args-optional>
<s-arg name="data">
a <s-expression>data.frame</s-expression> in which to interpret the variables named in the <s-expression>formula</s-expression>,  
or in the <s-expression>subset</s-expression> and the <s-expression>weights</s-expression> argument. If this is missing, then the  
variables in the <s-expression>formula</s-expression> should be on the search list. This may also be a  
single number to handle some special cases - see below for details. 
</s-arg>
<s-arg name="weights">
vector of observation weights; if supplied, the algorithm fits to minimize  
a function of the sum of the weights multiplied into the squared residuals.  
The length of <s-expression>weights</s-expression> must be the same as the number of observations. The  
weights must be nonnegative and it is strongly recommended that they be  
strictly positive, since zero weights are ambiguous, compared to use of the  
<s-expression>subset</s-expression> argument. 
</s-arg>
<s-arg name="subset">
expression saying which subset of the rows of the data should be used in  
the fit. This can be a logical vector (which is replicated to have length  
equal to the number of observations), or a numeric vector indicating which  
observation numbers are to be included, or a character vector of the row  
names to be included. All observations are included by default. 
</s-arg>
<s-arg name="na.action">
a function to filter missing data. This is applied to the <s-expression>model.frame</s-expression>  
after any <s-expression>subset</s-expression> argument has been used. The default (with <s-expression>na.fail</s-expression>) is  
to create an error if any missing values are found. A possible alternative  
is <s-expression>na.exclude</s-expression>, which deletes observations that contain one or more missing  
values. 
</s-arg>
<s-arg name="model">
logical flag: if <s-expression>TRUE</s-expression>, the model frame is returned in component model. 
</s-arg>
<s-arg name="x">
logical flag: if <s-expression>TRUE</s-expression>, the model matrix is returned in component <s-expression>x</s-expression>. 
</s-arg>
<s-arg name="y">
logical flag: if <s-expression>TRUE</s-expression>, the response is returned in component <s-expression>y</s-expression>. 
</s-arg>
<s-arg name="control">
a list of control parameters to be used in the numerical algorithms. See  
<s-expression>lmRobBI.control()</s-expression> for the possible control parameters and their  
default settings. 
</s-arg>
</s-args-optional>
<s-value>
a list describing the regression.
See <s-expression>lmRobBI.object</s-expression> for a complete  
description of the object returned. 
</s-value>
<s-section name=" REFERENCES">

Marazzi, A. (1993).  
<it>Algorithms, Routines, and S Functions for Robust Statistics.</it>
Wadsworth &#38; Brooks/Cole, Pacific Grove, CA. 
</s-section>
<s-see>
<s-function name="lmRobBI.object.sgm">lmRobBI.object</s-function>,
<s-function name="lmRobBI.control.sgm">lmRobBI.control</s-function>.
</s-see>
<s-keywords>
<s-keyword>librobust</s-keyword>
<s-keyword>regression</s-keyword>
</s-keywords>
<s-docclass>
function
</s-docclass>
</s-function-doc>
