print.PSAbootSummary

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.PSAbootSummary function

<dl><dt>x</dt>
<dd>result of `[summary.PSAboot()]`</dd>
<dt>digits</dt>
<dd>desired number of digits after the decimal point.</dd>
<dt>...</dt>
<dd>unused.</dd></dl>

Arguments

Print method for PSAboot Summary. — print.PSAbootSummary

<dl>

<dt>x</dt>
<dd>result of `[summary.PSAboot()]`</dd>


<dt>digits</dt>
<dd>desired number of digits after the decimal point.</dd>


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

</dl>

print.PSAbootSummary: Print method for PSAboot Summary.

Description

Usage

Value

Arguments