predict.CBPS

The CBPS package fits &#8216;covariate balancing propensity score&#8217; for use in propensity score
weighting. In the binary treatment case they can also be used for matching. This method helps to 
implement that process in a manner consistent with use of propensity scores elsewhere in optmatch; see
<code>scores</code> documentation.

Distance based bipartite matching using minimum cost flow, oriented
to matching of treatment and control groups in observational studies ('Hansen'
and 'Klopfer' 2006 <doi:10.1198/106186006X137047>). Routines are provided to
generate distances from generalised linear models (propensity score
matching), formulas giving variables on which to limit matched distances,
stratified or exact matching directives, or calipers, alone or in
combination.

Josh Errickson

optmatch

Functions for Optimal Matching

Ben Hansen

Mark Fredrickson

Josh Buckner

Adam Rauh

predict.CBPS function

<dl><dt>object</dt>
<dd>A CBPS object</dd>
<dt>newdata</dt>
<dd>Unused.</dd>
<dt>type</dt>
<dd>Return inverse logits of fitted values (the default) or fitted values themselves</dd>
<dt>...</dt>
<dd>Unused.</dd></dl>

Arguments

(Internal) Predict for CBPS objects — predict.CBPS

<dl>

<dt>object</dt>
<dd>A CBPS object</dd>


<dt>newdata</dt>
<dd>Unused.</dd>


<dt>type</dt>
<dd>Return inverse logits of fitted values (the default) or fitted values themselves</dd>


<dt>...</dt>
<dd>Unused.</dd>

</dl>

predict.CBPS: (Internal) Predict for CBPS objects

Description

Usage

Value

Arguments