psa.strata

Propensity Score Analysis using Stratification

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

psa.strata function

<dl><dt>Y</dt>
<dd>response variable.</dd>
<dt>Tr</dt>
<dd>treatment variable.</dd>
<dt>strata</dt>
<dd>strata identifier.</dd>
<dt>trim</dt>
<dd>allows for a trimmed mean as outcome measure, where trim is from
0 to .5 (.5 implying median).</dd>
<dt>minStrata</dt>
<dd>minimum number of treatment or control units within a strata 
to include that strata.</dd></dl>

Arguments

Propensity Score Analysis using Stratification — psa.strata

<dl>

<dt>Y</dt>
<dd>response variable.</dd>


<dt>Tr</dt>
<dd>treatment variable.</dd>


<dt>strata</dt>
<dd>strata identifier.</dd>


<dt>trim</dt>
<dd>allows for a trimmed mean as outcome measure, where trim is from
0 to .5 (.5 implying median).</dd>


<dt>minStrata</dt>
<dd>minimum number of treatment or control units within a strata 
to include that strata.</dd>

</dl>

psa.strata: Propensity Score Analysis using Stratification

Description

Usage

Value

Arguments