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

q25 function

<dl><dt>x</dt>
<dd>numeric vector.</dd>
<dt>na.rm</dt>
<dd>logical; if true, any NA and NaN's are removed from x before the
quantiles are computed</dd>
<dt>...</dt>
<dd>other parameters passed to <code><a href="/link/quantile?package=PSAboot&version=1.3.6" data-mini-rdoc="PSAboot::quantile">quantile</a></code>.</dd></dl>

Arguments

Return the 25th percentile. — q25

<dl>

<dt>x</dt>
<dd>numeric vector.</dd>


<dt>na.rm</dt>
<dd>logical; if true, any NA and NaN's are removed from x before the
quantiles are computed</dd>


<dt>...</dt>
<dd>other parameters passed to <code><a href='https://rdrr.io/r/stats/quantile.html'>quantile</a></code>.</dd>

</dl>

q25: Return the 25th percentile.

Description

Usage

Value

Arguments