print.PSAboot

It is often advantageous to test a hypothesis more than once
in the context of propensity score analysis (Rosenbaum, 2012)
<doi:10.1093/biomet/ass032>. The functions in this package facilitate
bootstrapping for propensity score analysis (PSA). By default, bootstrapping
using two classification tree methods (using 'rpart' and 'ctree'
functions), two matching methods (using 'Matching' and 'MatchIt'
packages), and stratification with logistic regression. A framework
is described for users to implement additional propensity score
methods. Visualizations are emphasized for diagnosing balance;
exploring the correlation relationships between bootstrap samples and
methods; and to summarize results.

Jason Bryer

PSAboot

Bootstrapping for Propensity Score Analysis

print.PSAboot function

<dl><dt>x</dt>
<dd>result of <code>PSAboot</code>.</dd>
<dt>...</dt>
<dd>currently unused.</dd></dl>

Arguments

Print results of PSAboot — print.PSAboot

<dl>

<dt>x</dt>
<dd>result of <code>PSAboot</code>.</dd>


<dt>...</dt>
<dd>currently unused.</dd>

</dl>

print.PSAboot: Print results of PSAboot

Description

Usage

Value

Arguments