<!doctype s-function-doc system "s-function-doc.dtd" [
<!entity % S-OLD "INCLUDE">
]
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<s-function-doc>
<s-topics>
   <s-topic>lmRob.robust.control</s-topic>
</s-topics>
<s-title>
Control Parameters for Robust Linear Regression 
</s-title>
<s-description>
Allows the users to set values affecting the estimation procedure for robust  
regression in <s-expression>lmRob</s-expression>. 
</s-description>
<s-usage>
<s-old-style-usage>
lmRob.robust.control(tlo=0.0001, tua=1.5e-06, mxr=50, mxf=50, mxs=50,  
    tl=1e-06, estim="Final", initial.alg="Auto", final.alg="MM",  
    seed=1313, level=0.1, efficiency=0.9, weight=c("Optimal","Optimal"),  
    trace=T) 
</s-old-style-usage>
</s-usage>
<s-args-optional>
<s-arg name="tlo">
the relative tolerance in the iterative algorithms. 
</s-arg>
<s-arg name="tua">
the tolerance used for the determination of pseudo-rank. 
</s-arg>
<s-arg name="mxr">
the maximum number of iterations in the refinement step. 
</s-arg>
<s-arg name="mxf">
the maximum number of iterations for computing final coefficient estimates. 
</s-arg>
<s-arg name="mxs">
the maximum number of iterations for computing scale estimate. 
</s-arg>
<s-arg name="tl">
the tolerance for scale denominators. If a scale estimate becomes less than  
<s-expression>tl</s-expression>, the scale estimate is set equal to <s-expression>tl</s-expression>. 
</s-arg>
<s-arg name="estim">
parameter that determines the type of estimator to be computed. If  
<s-expression>estim="Initial"</s-expression>, only the initial estimates are computed; if  
<s-expression>estim="Final"</s-expression>, then final estimates are returned. 
</s-arg>
<s-arg name="initial.alg">
parameter that determines the algorithm for initial estimates.  
Valid choices are <s-expression>"Auto"</s-expression> for data-dependent 
algorithm, <s-expression>"Random"</s-expression> for  
random resampling, <s-expression>"Exhaustive"</s-expression> for 
exhaustive resampling, <s-expression>"Fast"</s-expression> for fast
procedure, and <s-expression>"Genetic"</s-expression>  
for genetic algorithm. By default, <s-expression>lmRob</s-expression> 
uses <s-expression>"Auto"</s-expression>. 
</s-arg>
<s-arg name="final.alg">
parameter that determines the type of the final estimates. Valid choices 
are <s-expression>"Adaptive"</s-expression> for the robust efficient 
weighted least squares as 
proposed in Gervini and Yohai (1999), and <s-expression>"MM"</s-expression> 
for MM-estimate as 
proposed in Yohai, Stahel and Zamar (1991). By default, 
<s-expression>lmRob</s-expression> uses 
<s-expression>"MM"</s-expression>. 
</s-arg>
<s-arg name="seed">
seed parameter used in the random sampling and genetic algorithm for the  
computation of initial estimates. 
</s-arg>
<s-arg name="weight">
a character vector that determines the type of loss functions to be used.  
The first determines the loss function used for the initial estimates, and 
the second determines the loss function used for the final M-estimates. 
Valid choices are <s-expression>"Optimal"</s-expression> and 
<s-expression>"Bisquare"</s-expression>. 
</s-arg>
<s-arg name="level">
the level of significance of the test for bias of the final MM-estimates, 
if desired later on. 
</s-arg>
<s-arg name="efficiency">
the asymptotic efficiency of the final estimate. 
</s-arg>
<s-arg name="trace">
logical flag: if <s-expression>TRUE</s-expression>, the remaining 
computing time will be printed. 
</s-arg>
</s-args-optional>
<s-value>
a list containing the values used for each of the control parameters. 
</s-value>
<s-see>
<s-function name="lmRob.sgm">lmRob</s-function>. 
</s-see>
<s-examples>
<s-example type = text>
control.bs &lt;- lmRob.robust.control(weight=c("Bisquare","Optimal")) 
oilcity.bs &lt;- lmRob(Oil~Market, data=oilcity, robust.control=control.bs) 
</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>
