<!doctype s-function-doc system "s-function-doc.dtd" [
<!entity % S-OLD "INCLUDE">
]
>
<s-function-doc>
<s-topics>
   <s-topic>lmRobBI.eff</s-topic>
</s-topics>
<s-title>
Tuning Constant for Bounded Influence Estimator 
</s-title>
<s-description>
Returns one tuning constant for bounded influence estimator, given the  
asymptotic efficiency.  
</s-description>
<s-usage>
<s-old-style-usage>
lmRobBI.eff(eff, method="sch", tuning.p=NULL, nx=1, intercept=T, sigmax=1) 
</s-old-style-usage>
</s-usage>
<s-args-required>
<s-arg name="eff">
the asymptotic efficiency required. 
</s-arg>
</s-args-required>
<s-args-optional>
<s-arg name="method">
a character vector, which specifies the type of bounded influence 
estimator. If <s-expression>method="sch"</s-expression>, the Schweppe-type or Krasker-Welsch type 
bounded influence estimator is used; if <s-expression>method="mal"</s-expression>, the Mallows-type 
bounded influence estimator is used. 
</s-arg>
<s-arg name="tuning.p">
a scalar which gives the value of the tuning constant for the  
psi-function of the corresponding generalized M-estimator. It is 
only used if <s-expression>method="mal"</s-expression>. 
</s-arg>
<s-arg name="nx">
the number of explanatory variables used in the regression, not including 
the intercept. 
</s-arg>
<s-arg name="intercept">
logical flag: if <s-expression>TRUE</s-expression>, an intercept will be fitted by the regression. 
</s-arg>
<s-arg name="sigmax">
a scalar which gives the standard deviation of the model matrix <s-expression>x</s-expression>. 
</s-arg>
</s-args-optional>
<s-value>
the tuning constant of the optimal weight function which will have  
the required asymptotic efficiency. 
</s-value>
<s-see>
<s-function name="chb.sgm">chb</s-function>,
<s-function name="lmRobMM.effvy.sgm">lmRobMM.effvy</s-function>.
</s-see>
<s-keywords>
<s-keyword>librobust</s-keyword>
</s-keywords>
<s-docclass>
function
</s-docclass>
</s-function-doc>
