monitor_mmc

This function provides a way to merge the user specified controls for the
optimization methods with their respective default controls.

internal

An implementation of the Monte Carlo techniques described in details by Dufour (2006) <doi:10.1016/j.jeconom.2005.06.007> and Dufour and Khalaf (2007) <doi:10.1002/9780470996249.ch24>. The two main features available are the Monte Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized Monte Carlo, mmc(), for statistics with nuisance parameters.

Gabriel Rodriguez Rondon

MaxMC

Maximized Monte Carlo

Julien Neves

Jean-Marie Dufour

Gabriel Rodriguez-Rondon

monitor_mmc function

<dl><dt>object</dt>
<dd>A data.frame</dd>
<dt>alpha</dt>
<dd>An atomic vector. If <code>mmc</code> finds a
p-value over <code>alpha</code>, then the algorithm will stop. This is particularly
useful if we are only looking at testing a hypothesis at a particular level.
Default is NULL.</dd>
<dt>monitor</dt>
<dd>A logical. If set to TRUE, then the p-values at every
iteration and the cumulative maximum p-value are plotted on a graphical device.
Default is FALSE.</dd></dl>

Arguments

Format the optimization method controls — monitor_mmc

<dl>

<dt>object</dt>
<dd>A data.frame</dd>


<dt>alpha</dt>
<dd>An atomic vector. If <code>mmc</code> finds a
p-value over <code>alpha</code>, then the algorithm will stop. This is particularly
useful if we are only looking at testing a hypothesis at a particular level.
Default is NULL.</dd>


<dt>monitor</dt>
<dd>A logical. If set to TRUE, then the p-values at every
iteration and the cumulative maximum p-value are plotted on a graphical device.
Default is FALSE.</dd>

</dl>

monitor_mmc: Format the optimization method controls

Description

Usage

Value

Arguments