postBeta

In our model mui follows a normal distribution with mean Xi^T %*% beta and precision rho. Additionally
we assume that beta follows a mvnormal prior with mean 0 and precision (rho_Beta) * I.This function draws
from the posterior distribution of beta under these assumptions.

Implements a Bayesian hierarchical model designed to identify skips in mobile menstrual cycle self-tracking on mobile apps. Future developments will allow for the inclusion of covariates affecting cycle mean and regularity, as well as extra information regarding tracking non-adherence. Main methods to be outlined in a forthcoming paper, with alternative models from Li et al. (2022) <doi:10.1093/jamia/ocab182>.

Luke Duttweiler

skipTrack

A Bayesian Hierarchical Model that Controls for Non-Adherence in
Mobile Menstrual Cycle Tracking

postBeta function

<dl><dt>rhoBeta</dt>
<dd>A scalar representing the prior precision parameter for beta.</dd>
<dt>rho</dt>
<dd>A scalar representing the precision parameter.</dd>
<dt>Xi</dt>
<dd>A matrix of covariates, where each row represents an individual and each column represents a covariate.</dd>
<dt>muI</dt>
<dd>A vector where each element is the mean for individual i.</dd></dl>

Arguments

Draw from Posterior Distribution for Beta Parameters — postBeta

<dl>

<dt>rhoBeta</dt>
<dd>A scalar representing the prior precision parameter for beta.</dd>


<dt>rho</dt>
<dd>A scalar representing the precision parameter.</dd>


<dt>Xi</dt>
<dd>A matrix of covariates, where each row represents an individual and each column represents a covariate.</dd>


<dt>muI</dt>
<dd>A vector where each element is the mean for individual i.</dd>

</dl>

postBeta: Draw from Posterior Distribution for Beta Parameters

Description

Usage

Value

Arguments

Details