force_control

This function processes the given distance list by adding certain zero-cost
edges so that the user-specified controls are forced into the final matched
samples. This function is of little interest to most users.

Multivariate matching in observational studies typically has two goals: 1. to construct
treated and control groups that have similar distribution of observed covariates and 2. to produce
matched pairs or sets that are homogeneous in a few priority variables. This packages implements a
network-flow-based method built around a tripartite graph that can simultaneously achieve both goals.
The package also implements a template matching algorithm using a variant of the tripartite graph
design. A brief description of the workflow and some examples are given in the vignette. A more elaborated
tutorial can be found at <https://www.researchgate.net/publication/359513837_Tutorial_for_R_Package_match2C>.

Bo Zhang

match2C

Match One Sample using Two Criteria

force_control function

<dl><dt>dist_list</dt>
<dd>A distance_list object.</dd>
<dt>Z</dt>
<dd>A length-n vector of treatment indicator.</dd>
<dt>include</dt>
<dd>A binary vector indicating which controls must be included (length(include) = sum(1-Z).</dd></dl>

Arguments

Force including certain controls in the final matched samples. — force_control

<dl>

<dt>dist_list</dt>
<dd>A distance_list object.</dd>


<dt>Z</dt>
<dd>A length-n vector of treatment indicator.</dd>


<dt>include</dt>
<dd>A binary vector indicating which controls must be included (length(include) = sum(1-Z).</dd>

</dl>

force_control: Force including certain controls in the final matched samples.

Description

Usage

Value

Arguments