print.PSAboot.balance

This is a crude measure of overall balance. Absolute value of the standardized
effect sizes are calculated for each covariate. Overall balance statistics are
the mean of those effect sizes after adjustment for each method across all
bootstrap samples.

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.balance function

<dl><dt>x</dt>
<dd>results from <code>balance</code>.</dd>
<dt>na.rm</dt>
<dd>whether NA balance statistics should be removed before averaging them.</dd>
<dt>...</dt>
<dd>currently unused.</dd></dl>

Arguments

Print method for balance. — print.PSAboot.balance

<dl>

<dt>x</dt>
<dd>results from <code>balance</code>.</dd>


<dt>na.rm</dt>
<dd>whether NA balance statistics should be removed before averaging them.</dd>


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

</dl>

print.PSAboot.balance: Print method for balance.

Description

Usage

Value

Arguments