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

<s-topics>
<s-topic> gammaRob </s-topic>
<s-topic> lognormRob </s-topic>
<s-topic> weibullRob </s-topic>
</s-topics>

<s-title>
Robust Asymmetric Distribution Parameter Estimates
</s-title>

<s-description>
Robust parameter estimation for gamma, lognormal, and Weibull distributions.
</s-description>

<s-usage>
<s-old-style-usage>
gammaRob(data, estim = c("tdmean", "M"), save.data = T,
control = gammaRob.control(estim, ...), ...)
</s-old-style-usage>
<s-old-style-usage>
lognormRob(data, estim = c("tdmean"), save.data = T,
control = lognormRob.control(estim, ...), ...)
</s-old-style-usage>
<s-old-style-usage>
weibullRob(data, estim = c("tdmean", "M"), save.data = T,
control = weibullRob.control(estim, ...), ...)
</s-old-style-usage>
</s-usage>

<s-args-required>
<s-arg name=" data ">
a vector of positive real numbers.
</s-arg>
</s-args-required>

<s-args-optional>
<s-arg name=" estim ">
the robust estimator.  The choices are "tdmean" for the truncated mean and
"M" for the M.  The M estimator is not available for the lognormal
distribution.
</s-arg>

<s-arg name=" save.data ">
if TRUE, the data is saved in the returned object.
</s-arg>

<s-arg name=" control ">
a list of control parameters for the robust estimator.  Use the links in the
SEE ALSO section to see the parameters and their default settings.
</s-arg>

<s-arg name=" ... ">
control parameters may also be passed directly.
</s-arg>
</s-args-optional>

<s-value>
<pre>
an asymmetric.dstn object with class "gammaRob", "lognormRob", or
"weibullRob" (respectively) containing the following components.
</pre>

<s-return-component name=" call ">
an image of the call that produced the object, but with the arguments all
named.
</s-return-component>

<s-return-component name=" mu ">
a robust estimate of the mean.
</s-return-component>

<s-return-component name=" alpha ">
a robust estimate of the shape parameter.
</s-return-component>

<s-return-component name=" sigma ">
a robust estimate of the scale parameter.
</s-return-component>

<s-return-component name=" V.mu ">
a robust estimate of the variance of mu.
</s-return-component>

<s-return-component name=" data ">
the data if <s-expression>data = T</s-expression>.
</s-return-component>

<s-return-component name=" ok ">
equal to 1 if all numerical procedures exit cleanly.
</s-return-component>

<pre>
The following elements are included for "tdmean" estimates.
</pre>

<s-return-component name=" Tl ">
the lower truncation point.
</s-return-component>

<s-return-component name=" Tu ">
the upper truncation point.
</s-return-component>

<pre>
The following element is included for "M" estimates.
</pre>

<s-return-component name=" cov ">
an estimate of the covariance of alpha and sigma.
</s-return-component>
</s-value>

<s-details>
The classes "gammaRob", "lognormRob", and "weibullRob" are subclasses of
"asymmetric.dstn".  The generic methods are defined for class
"asymmetric.dstn".
</s-details>

<s-section name = "REFERENCES">
Marazzi A., Ruffieux C. (1999).
The truncated mean of an asymmetric distribution.
Computational Statististic and Data Analysis, 32, pp. 79-100.
<br>
Hampel F.R., Ronchetti E.M., Rousseeuw P.J., Stahel W.A. (1986).
Robust statistics: the approach based on influence functions.
Wiley, New York.
<br>
Marazzi A., Ruffieux C. (1996).
Implementing M-estimators of the Gamma distribution.
In: Rieder H. (Ed.), Robust Statistics, Data Analysis, and Computer 
intensive Methods, Springer Verlag.
</s-section>

<s-see>
<s-function name="gammaRob.control.sgm">gammaRob.control</s-function>,
<s-function name="lognormRob.control.sgm">lognormRob.control</s-function>,
<s-function name="weibullRob.control.sgm">weibullRob.control</s-function>,
<s-function name="summary.asymmetric.dstn.sgm">summary.asymmetric.dstn</s-function>,
<s-function name="plot.asymmetric.dstn.sgm">plot.asymmetric.dstn</s-function>.
</s-see>

<s-examples>
<s-example>
los.gammarob <- gammaRob(los)
</s-example>

<s-example>
los.lognormrob <- lognormRob(los)
</s-example>

<s-example>
los.weibullrob <- weibullRob(los)
</s-example>
</s-examples>

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

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

</s-function-doc>
