PropensityObj-class

Class <code>PropensityObj</code> groups Propensity regression results under a 
 common name.

internal

Methods to estimate dynamic treatment regimes using Interactive
Q-Learning, Q-Learning, weighted learning, and value-search methods based on
Augmented Inverse Probability Weighted Estimators and Inverse Probability
Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for
Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B.,
Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.

Shannon Holloway

DynTxRegime

Methods for Estimating Optimal Dynamic Treatment Regimes

PropensityObj-class function

<dl>
<dt><code>Propensity</code></dt>
<dd>ANY - expected to be <code>PropensityFit</code>,
<code>PropensityFit_fSet</code>, <code>PropensityFit_SubsetList</code>, 
or <code>DecisionPointList</code>.</dd></dl>

Slots

Class PropensityObj — PropensityObj-class

<dl>
<dt><code>Propensity</code></dt>
<dd>ANY - expected to be <code>PropensityFit</code>,
<code>PropensityFit_fSet</code>, <code>PropensityFit_SubsetList</code>, 
or <code>DecisionPointList</code>.</dd>


</dl>

PropensityObj-class: Class `PropensityObj`

Description

Arguments

Slots