postSij

This function calculates a random draw from the full conditional posterior distribution
for s_ij in the Li algorithm, given the observed values yijs, the parameter pi,
the lambda_i value, and the truncation level S.

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

postSij function

<dl><dt>yijs</dt>
<dd>Vector of observed values for s_ij.</dd>
<dt>pii</dt>
<dd>Probability parameter pi.</dd>
<dt>lambdai</dt>
<dd>Value of lambda_i.</dd>
<dt>S</dt>
<dd>Truncation level.</dd></dl>

Arguments

Compute random draw from full conditional posterior for s_ij in Li algorithm. — postSij

<dl>

<dt>yijs</dt>
<dd>Vector of observed values for s_ij.</dd>


<dt>pii</dt>
<dd>Probability parameter pi.</dd>


<dt>lambdai</dt>
<dd>Value of lambda_i.</dd>


<dt>S</dt>
<dd>Truncation level.</dd>

</dl>

postSij: Compute random draw from full conditional posterior for s_ij in Li algorithm.

Description

Usage

Value

Arguments