BOWLBasic_fSet-class

Results for a single step of the BOWL algorithm for
a subset of data with binary treatment options and
subsets for propensity modeling specified.

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.

Shannon Holloway

DynTxRegime

Methods for Estimating Optimal Dynamic Treatment Regimes

BOWLBasic_fSet-class function

Objects can be created by calls of the form <code>new("BOWLBasic_fSet", ...)</code>.
These objects are for convenience in package development
and should not be created by users.

Objects from the Class

<dl class="dl-horizontal"><dt><code>txIno</code>:</dt><dd>Object of class <code>"TxSubset."</code>
 Treatment information.</dd>
 </dl>

Slots

<dl class="dl-horizontal"><dt>.predictOptimalTx</dt><dd><code>signature(x = "BOWLBasic_fSet", newdata = "data.frame")</code>: 
 Estimate optimal treatment for newdata. Method is not exported.</dd>
	 </dl>

BOWLBasic_fSet-class: Class `"BOWLBasic_fSet"`

Description

Arguments

Objects from the Class

Slots

Methods

Examples