.newOptimObj

.newOptimObj,ANY-method

.newOptimObj,.rwl-method

Call newOptim and stores result under 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

.newOptimObj function

<dl><dt>methodObject</dt>
<dd>object containing parameters needed by a weighted 
learning method</dd>
<dt>...</dt>
<dd>additional inputs passed to optimization routine.</dd>
<dt>lambda</dt>
<dd>tuning parameters</dd>
<dt>suppress</dt>
<dd>integer indicating screen print preferences</dd></dl>

Arguments

Create an OptimObj Object — .newOptimObj

<dl>

<dt>methodObject</dt>
<dd>object containing parameters needed by a weighted 
learning method</dd>


<dt>...</dt>
<dd>additional inputs passed to optimization routine.</dd>


<dt>lambda</dt>
<dd>tuning parameters</dd>


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

</dl>

.newOptimObj: Create an OptimObj Object

Description

Usage

Arguments