newCVInfoObj

.newCVInfoObj,ANY,Kernel-method

Call newCVInfo and stores result in @cvInfo

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

newCVInfoObj function

<dl><dt>lambdas</dt>
<dd>Tuning parameters to be considered</dd>
<dt>kernel</dt>
<dd>Kernel (w/kernel parameters) to be considered</dd>
<dt>...</dt>
<dd>Additional arguments as needed</dd>
<dt>methodObject</dt>
<dd>Object parameters for weighted learning method</dd>
<dt>suppress</dt>
<dd>T/F indicating if screen prints are generated</dd>
<dt>cvFolds</dt>
<dd>Number of cross-validation folds</dd></dl>

Arguments

Create a New CVInfoObj Object — newCVInfoObj

<dl>

<dt>lambdas</dt>
<dd>Tuning parameters to be considered</dd>


<dt>kernel</dt>
<dd>Kernel (w/kernel parameters) to be considered</dd>


<dt>...</dt>
<dd>Additional arguments as needed</dd>


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


<dt>suppress</dt>
<dd>T/F indicating if screen prints are generated</dd>


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

</dl>

newCVInfoObj: Create a New CVInfoObj Object

Description

Usage

Arguments