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.

Methods for Estimating Optimal Dynamic Treatment Regimes

package-ubuntu-20.04-r-4.0.5#cran#DynTxRegime-4.10

<p>Retrieve the value object returned by rgenoud() in optimalSeq().
</p>

genetic(object, ...)

## S4 method for signature 'OptimalSeq'
genetic(object, ...)

<table summary="R argblock">
<tr valign="top">
<td><code>object</code></td>
<td>
<p>Value object returned by optimalSeq()</p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>Optional inputs. Ignored.</p>
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#DynTxRegime-4.10#docs#genetic

genetic

Description

Usage

Arguments

DynTxRegime

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`object`	Value object returned by optimalSeq()
`...`	Optional inputs. Ignored.