fitLinearModel

Method for fitting linear models.
In the fashion of this package, only binary and quantitative
outcomes are supported.

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

fitLinearModel function

<dl><dt>y</dt>
<dd>Response vector. 0-1 coding for binary outcomes,
otherwise conventional regression is performed.</dd>
<dt>Z</dt>
<dd>Numeric vector of (univariate) input samples.</dd>
<dt>logistic</dt>
<dd>Logical indicating whether, in the case of a binary
outcome, a logistic regression model should be fitted
(<code>TRUE</code>) or a LDA model should be fitted (<code>FALSE</code>)</dd></dl>

Arguments

Fitting linear models — fitLinearModel

<dl>

<dt>y</dt>
<dd>Response vector. 0-1 coding for binary outcomes,
otherwise conventional regression is performed.</dd>


<dt>Z</dt>
<dd>Numeric vector of (univariate) input samples.</dd>


<dt>logistic</dt>
<dd>Logical indicating whether, in the case of a binary
outcome, a logistic regression model should be fitted
(<code>TRUE</code>) or a LDA model should be fitted (<code>FALSE</code>)</dd>

</dl>

fitLinearModel: Fitting linear models

Description

Usage

Value

Arguments

Details