LinRegChol

Do linear regression of form solve(X^T O X + P, X^T y) where O is omega, P
is precision.

internal

Estimates hierarchical models using mean-field variational Bayes.
At present, it can estimate logistic, linear, and negative binomial models.
It can accommodate models with an arbitrary number of random effects and
requires no integration to estimate. It also provides the ability to improve
the quality of the approximation using marginal augmentation.
Goplerud (2022) <doi:10.1214/21-BA1266> and Goplerud (2024) <doi:10.1017/S0003055423000035>
provide details on the variational algorithms.

Max Goplerud

vglmer

Variational Inference for Hierarchical Generalized Linear Models

LinRegChol function

<dl><dt>X</dt>
<dd>Design Matrix</dd>
<dt>omega</dt>
<dd>Polya-Gamma weights</dd>
<dt>prior_precision</dt>
<dd>Prior Precision for Regression</dd>
<dt>y</dt>
<dd>Outcome</dd>
<dt>save_chol</dt>
<dd>Save cholesky factor</dd></dl>

Arguments

Linear Regression by Cholesky — LinRegChol

<dl>

<dt>X</dt>
<dd>Design Matrix</dd>


<dt>omega</dt>
<dd>Polya-Gamma weights</dd>


<dt>prior_precision</dt>
<dd>Prior Precision for Regression</dd>


<dt>y</dt>
<dd>Outcome</dd>


<dt>save_chol</dt>
<dd>Save cholesky factor</dd>

</dl>

LinRegChol: Linear Regression by Cholesky

Description

Usage

Arguments