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

<s-topics>
   <s-topic>glmRob.cubif.control</s-topic>
</s-topics>

<s-title>
Control for Bounded Influence Robust GLM Estimator
</s-title>

<s-description>
Allows users to set parameters for <s-expression>glmRob</s-expression>.
</s-description>

<s-usage>
<s-old-style-usage>
glmRob.cubif.control(epsilon = 0.001, maxit = 50, bpar = 2, cpar = 1.5, trc
= F, ...)
</s-old-style-usage>
</s-usage>

<s-args-optional>
<s-arg name="epsilon">
convergence threshold for the parameters.
</s-arg>

<s-arg name="maxit">
maximum number of iterations.
</s-arg>

<s-arg name="ufact">
tuning constant for the final estimate. The truncation  
value for the likelihood equation is equal to `ufact *  
sqrt(p) where <s-expression>p</s-expression> is the rank of the design matrix. The larger 
this value the more similar to the maximum likelihood estimate 
the final estimate is. The smallest acceptable value  
for <s-expression>ufact</s-expression> is 1.1 . 
</s-arg>

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

<s-arg name="cpar">
tuning constant for the initial estimate. This is the 
truncation value for the likelihood equation for the  
initial estimate. It determines the starting point of the 
iterative algorithm to calculate the final estimate.  
</s-arg>

<s-arg name="trc">
logical value. If <s-expression>TRUE</s-expression> the number of the
current iteration is printed on the screen. 
</s-arg>
</s-args-optional>

<s-value>
a list is returned, consisting of these parameters packaged to be 
used by <s-expression>glmRob()</s-expression>. The values for
<s-expression>glmRob.cubif.control()</s-expression> can be supplied 
directly in a call to <s-expression>glmRob()</s-expression>. These values
are filtered through <s-expression>glmRob.cubif.control()</s-expression>
inside <s-expression>glmRob()</s-expression>. 
</s-value>

<s-see>
<s-function name="glmRob.sgm">glmRob</s-function>. </s-see>
<s-examples>

<s-example type = text>
# The following are equivalent
<br>
glmRob(formula, family, fit.method = 'cubif',
cubif.control=glmRob.cubif.control(epsilon=0.0001, maxit = 100))
glmRob(formula, family, fit.method = 'cubif', epsilon=0.0001, maxit = 100)
</s-example>
</s-examples>

<s-keywords>
<s-keyword>robust</s-keyword>
<s-keyword>regression</s-keyword>
<s-keyword>librobust</s-keyword>
</s-keywords>

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

</s-function-doc>
