postPii

This performs a Metropolis-Hastings draw for pi_i, assuming s_ij follows a truncated geometric distribution with parameters
pi_i and S. The proposal distribution for pi_i is Beta(alpha, beta).

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

postPii function

<dl><dt>sij</dt>
<dd>Vector of cycle skip indicators for individual i</dd>
<dt>currentPii</dt>
<dd>Current value of pi_i</dd>
<dt>priorA</dt>
<dd>Hyperparameter alpha.</dd>
<dt>priorB</dt>
<dd>Hyperparameter beta.</dd>
<dt>S</dt>
<dd>Maximum number of skips allowed in algorithm</dd></dl>

Arguments

Compute M-H draw for pi_i in Li algorithm — postPii

<dl>

<dt>sij</dt>
<dd>Vector of cycle skip indicators for individual i</dd>


<dt>currentPii</dt>
<dd>Current value of pi_i</dd>


<dt>priorA</dt>
<dd>Hyperparameter alpha.</dd>


<dt>priorB</dt>
<dd>Hyperparameter beta.</dd>


<dt>S</dt>
<dd>Maximum number of skips allowed in algorithm</dd>

</dl>

postPii: Compute M-H draw for pi_i in Li algorithm

Description

Usage

Value

Arguments