loglik_cpp

This is the main computation engine wrapped by <code>opsr.fit</code>.

Estimates ordered probit switching regression models - a Heckman
type selection model with an ordinal selection and continuous outcomes.
Different model specifications are allowed for each treatment/regime. For
more details on the method, see Wang & Mokhtarian (2024) <doi:10.1016/j.tra.2024.104072>
or Chiburis & Lokshin (2007) <doi:10.1177/1536867X0700700202>.

Daniel Heimgartner

OPSR

Ordered Probit Switching Regression

Xinyi Wang

loglik_cpp function

<dl><dt>theta</dt>
<dd>named coefficient vector as parsed from formula interface <code>opsr</code>.</dd>
<dt>W</dt>
<dd>list of matrices with explanatory variables for selection process for each regime.</dd>
<dt>X</dt>
<dd>list of matrices with expalanatory varialbes for outcome process for each regime.</dd>
<dt>Y</dt>
<dd>list of vectors with continuous outcomes for each regime.</dd>
<dt>weights</dt>
<dd>vector of weights. See also <code>opsr</code>.</dd>
<dt>nReg</dt>
<dd>integer number of regimes.</dd>
<dt>nThreads</dt>
<dd>number of threads to be used by <code>OpenMP</code> (should be max. <code>nReg</code>).</dd></dl>

Arguments

Interface to C++ Log-Likelihood Implementation — loglik_cpp

<dl>

<dt>theta</dt>
<dd>named coefficient vector as parsed from formula interface <code>opsr</code>.</dd>


<dt>W</dt>
<dd>list of matrices with explanatory variables for selection process for each regime.</dd>


<dt>X</dt>
<dd>list of matrices with expalanatory varialbes for outcome process for each regime.</dd>


<dt>Y</dt>
<dd>list of vectors with continuous outcomes for each regime.</dd>


<dt>weights</dt>
<dd>vector of weights. See also <code>opsr</code>.</dd>


<dt>nReg</dt>
<dd>integer number of regimes.</dd>


<dt>nThreads</dt>
<dd>number of threads to be used by <code>OpenMP</code> (should be max. <code>nReg</code>).</dd>

</dl>

loglik_cpp: Interface to C++ Log-Likelihood Implementation

Description

Usage

Value

Arguments

See Also