postLambdai

This function calculates a random draw from the full conditional posterior distribution
for lambda_i in the Li algorithm, given the observed values y_ij, the indicators s_ij,
and the prior hyperparameters priorK and priorG.

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

postLambdai function

<dl><dt>yij</dt>
<dd>Vector of observed values for individual i.</dd>
<dt>sij</dt>
<dd>Vector of cycle skip indicators for individual i.</dd>
<dt>priorK</dt>
<dd>Prior hyperparameter kappa.</dd>
<dt>priorG</dt>
<dd>Prior hyperparameter gamma.</dd></dl>

Arguments

Compute random draw from full conditional posterior for lambda_i in Li algorithm. — postLambdai

<dl>

<dt>yij</dt>
<dd>Vector of observed values for individual i.</dd>


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


<dt>priorK</dt>
<dd>Prior hyperparameter kappa.</dd>


<dt>priorG</dt>
<dd>Prior hyperparameter gamma.</dd>

</dl>

postLambdai: Compute random draw from full conditional posterior for lambda_i in Li algorithm.

Description

Usage

Value

Arguments