minExactMatch

The <code>exactMatch</code> function creates a smaller matching problem by
stratifying observations into smaller groups. For a problem that is larger
than maximum allowed size, <code>minExactMatch</code> provides a way to find the
smallest exact matching problem that will allow for matching.

Distance based bipartite matching using minimum cost flow, oriented
to matching of treatment and control groups in observational studies ('Hansen'
and 'Klopfer' 2006 <doi:10.1198/106186006X137047>). Routines are provided to
generate distances from generalised linear models (propensity score
matching), formulas giving variables on which to limit matched distances,
stratified or exact matching directives, or calipers, alone or in
combination.

Josh Errickson

optmatch

Functions for Optimal Matching

Ben Hansen

Mark Fredrickson

Josh Buckner

Adam Rauh

minExactMatch function

<dl><dt>x</dt>
<dd>The object for dispatching.</dd>
<dt>scores</dt>
<dd>Optional vector of scores that will be checked against a caliper width.</dd>
<dt>width</dt>
<dd>Optional width of a caliper to place on the scores.</dd>
<dt>maxarcs</dt>
<dd>The maximum problem size to attempt to fit.</dd>
<dt>...</dt>
<dd>Additional arguments for methods.</dd></dl>

Arguments

Find the minimal exact match factors that will be feasible for a
given maximum problem size. — minExactMatch

<dl>

<dt>x</dt>
<dd>The object for dispatching.</dd>


<dt>scores</dt>
<dd>Optional vector of scores that will be checked against a caliper width.</dd>


<dt>width</dt>
<dd>Optional width of a caliper to place on the scores.</dd>


<dt>maxarcs</dt>
<dd>The maximum problem size to attempt to fit.</dd>


<dt>...</dt>
<dd>Additional arguments for methods.</dd>

</dl>

minExactMatch: Find the minimal exact match factors that will be feasible for a given maximum problem size.

Description

Usage

Value

Arguments

Details