logistic.loglik.ala

Log likelihood function for logistic regression with an approximate Laplace approximations used
This function is created as an example of how to create an estimator that is used
to calculate the marginal likelihood of a model.

Implements the Mode Jumping Markov Chain Monte Carlo algorithm described in <doi:10.1016/j.csda.2018.05.020> and its Genetically Modified counterpart described in <doi:10.1613/jair.1.13047> as well as the sub-sampling versions described in <doi:10.1016/j.ijar.2022.08.018> for flexible Bayesian model selection and model averaging.

Jon Lachmann

FBMS

Flexible Bayesian Model Selection and Model Averaging

Aliaksandr Hubin

logistic.loglik.ala function

<dl><dt>y</dt>
<dd>A vector containing the dependent variable</dd>
<dt>x</dt>
<dd>The matrix containing the precalculated features</dd>
<dt>model</dt>
<dd>The model to estimate as a logical vector</dd>
<dt>complex</dt>
<dd>A list of complexity measures for the features</dd>
<dt>params</dt>
<dd>A list of parameters for the log likelihood, supplied by the user</dd></dl>

Arguments

Log likelihood function for logistic regression with an approximate Laplace approximations used
This function is created as an example of how to create an estimator that is used
to calculate the marginal likelihood of a model. — logistic.loglik.ala

<dl>

<dt>y</dt>
<dd>A vector containing the dependent variable</dd>


<dt>x</dt>
<dd>The matrix containing the precalculated features</dd>


<dt>model</dt>
<dd>The model to estimate as a logical vector</dd>


<dt>complex</dt>
<dd>A list of complexity measures for the features</dd>


<dt>params</dt>
<dd>A list of parameters for the log likelihood, supplied by the user</dd>

</dl>

Log likelihood function for logistic regression with an approximate Laplace approximations used
This function is created as an example of how to create an estimator that is used
to calculate the marginal likelihood of a model.

logistic.loglik.ala: Log likelihood function for logistic regression with an approximate Laplace approximations used This function is created as an example of how to create an estimator that is used to calculate the marginal likelihood of a model.

Description

Usage

Value

Arguments

Examples