LearningObject-class

Class <code>LearningObject</code> contains stores parameters required for 
 weighted learning optimization step

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

LearningObject-class function

<dl>
<dt><code>mu</code></dt>
<dd>Matrix of outcome regression</dd></dl>
<dt><code>txVec</code></dt>
<dd>Vector of treatment coded as -1/1</dd>
<dt><code>invPi</code></dt>
<dd>Vector of inverse propensity for treatment received</dd>
<dt><code>response</code></dt>
<dd>Vector of the response</dd>

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Class LearningObject — LearningObject-class

<dl>
<dt><code>mu</code></dt>
<dd>Matrix of outcome regression</dd>


<dt><code>txVec</code></dt>
<dd>Vector of treatment coded as -1/1</dd>


<dt><code>invPi</code></dt>
<dd>Vector of inverse propensity for treatment received</dd>


<dt><code>response</code></dt>
<dd>Vector of the response</dd>


</dl>

LearningObject-class: Class `LearningObject`

Description

Arguments

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