outcome: Retrieve Regression Objects for the Outcome Regression Models

Description

Retrieve the value objects returned by the regression methods used to obtain parameter estimates for the outcome regression.

Usage

"outcome"(object, ...)

Arguments

object

an object of class DynTxRegime.

...

ignored.

Value

Retrieves the value object returned by the regression method used to estimate the outcome. See details for more information.

Details

There is a pattern to the format for the returned values of all standard regression tools that have been extended to the DynTxRegime class. A list is always returned. The length and structure of the list are determined by the statistical method used to estimate the optimal dynamic treatment regime and by the combination of the input arguments.

For IQ-Learning and optimalClass, the models are always communicated using objects of class modelObj. The pattern for the returned object follows:

if( iter == 0 && class(moMain) == "modelObj" && class(moCont) == "modelObj")

names(coef()) = "Combined"

if( iter > 0 && class(moMain) == "modelObj" && class(moCont) == "modelObj")

names(coef()) = "MainEffect", "Contrast"

if( iter == 0 && class(moMain) == "modelObj" && class(moCont) == "NULL")

names(coef()) = "moMain"

if( iter == 0 && class(moMain) == "NULL" && class(moCont) == "modelObj")

names(coef()) = "moCont"

For Q-Learning, models can be defined using modelObj or a list of objects of class modelObjSubset. If regression models are defined using objects of class modelObj, the returned list will follow the pattern described for IQ-Learning. If regression models are defined using objects of class modelObjSubset, a list is returned. The kth element of that list corresponds to the kth subset model and is named accordingly. Each subset model follows the pattern for modelObj described above.

For optimalSeq, models can be defined using modelObj lists of objects of class modelObj, or lists of objects of class modelObjSubset. If regression models are defined using objects of class modelObj, the returned list will follow the pattern described for IQ-Learning. If regression models are defined using lists, the kth element of the returned list corresponds to the kth model. Each model follows the pattern for modelObj described above.