gxe.test.boosting

This function takes a fitted <code>linear.logic</code> model and independent test
data as input for testing if there is a general GxE interaction.
This hypothesis test is based on a likelihood-ratio test.

A statistical learning method that tries to find the best set
of predictors and interactions between predictors for modeling binary or
quantitative response data in a decision tree. Several search algorithms
and ensembling techniques are implemented allowing for finetuning the
method to the specific problem. Interactions with quantitative
covariables can be properly taken into account by fitting local
regression models. Moreover, a variable importance measure for assessing
marginal and interaction effects is provided. Implements the
procedures proposed by Lau et al. (2024, <doi:10.1007/s10994-023-06488-6>).

Michael Lau

logicDT

Identifying Interactions Between Binary Predictors

gxe.test.boosting function

<dl><dt>model</dt>
<dd>A fitted <code>linear.logic</code> model (i.e., a model created via
<code>fitLinearLogicModel</code> or <code>fitLinearBoostingModel</code>)</dd>
<dt>X</dt>
<dd>Matrix or data frame of binary input data.
This object should correspond to the binary matrix for fitting the model.</dd>
<dt>y</dt>
<dd>Response vector. 0-1 coding for binary outcomes.</dd>
<dt>Z</dt>
<dd>Quantitative covariable supplied as a matrix or data frame</dd></dl>

Arguments

Gene-environment (GxE) interaction test based on boosted linear models — gxe.test.boosting

<dl>

<dt>model</dt>
<dd>A fitted <code>linear.logic</code> model (i.e., a model created via
<code>fitLinearLogicModel</code> or <code>fitLinearBoostingModel</code>)</dd>


<dt>X</dt>
<dd>Matrix or data frame of binary input data.
This object should correspond to the binary matrix for fitting the model.</dd>


<dt>y</dt>
<dd>Response vector. 0-1 coding for binary outcomes.</dd>


<dt>Z</dt>
<dd>Quantitative covariable supplied as a matrix or data frame</dd>

</dl>

gxe.test.boosting: Gene-environment (GxE) interaction test based on boosted linear models

Description

Usage

Value

Arguments

Details