OptimStep

.OptimStep

Complete Cross-Validation Step and Final Optimization

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

OptimStep function

<dl><dt>methodObject</dt>
<dd>Object parameters for weighted learning method</dd>
<dt>lambdas</dt>
<dd>tuning parameter</dd>
<dt>cvFolds</dt>
<dd>number of cross-validation folds</dd>
<dt>suppress</dt>
<dd>integer indicating screen printing preferences</dd></dl>

Arguments

Complete Cross-Validation Step and Final Optimization — OptimStep

<dl>

<dt>methodObject</dt>
<dd>Object parameters for weighted learning method</dd>


<dt>lambdas</dt>
<dd>tuning parameter</dd>


<dt>cvFolds</dt>
<dd>number of cross-validation folds</dd>


<dt>suppress</dt>
<dd>integer indicating screen printing preferences</dd>

</dl>

OptimStep: Complete Cross-Validation Step and Final Optimization

Description

Usage

Arguments