newCVStep

.newCVStep

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

newCVStep function

<dl><dt>cvObject</dt>
<dd>Information regarding folds and treatment groups</dd>
<dt>methodObject</dt>
<dd>Information needed for method specific objective function</dd>
<dt>lambda</dt>
<dd>numeric A single tuning parameter value</dd>
<dt>suppress</dt>
<dd>integer indicating printing preference</dd>
<dt>...</dt>
<dd>additional inputs.</dd></dl>

Arguments

An n-Fold Cross Validation Step — newCVStep

<dl>

<dt>cvObject</dt>
<dd>Information regarding folds and treatment groups</dd>


<dt>methodObject</dt>
<dd>Information needed for method specific objective function</dd>


<dt>lambda</dt>
<dd>numeric A single tuning parameter value</dd>


<dt>suppress</dt>
<dd>integer indicating printing preference</dd>


<dt>...</dt>
<dd>additional inputs.</dd>

</dl>

newCVStep: An n-Fold Cross Validation Step

Description

Usage

Value

Arguments