logistic.loglik

Log likelihood function for logistic regression with a prior p(m)=sum(total_width)
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 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 a prior p(m)=sum(total_width)
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

<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 a prior p(m)=sum(total_width)
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: Log likelihood function for logistic regression with a prior p(m)=sum(total_width) 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