postRho

In our model the data are drawn from LogN(mu_i + log(c_ij), tau_i). The prior for mu_i
is given as N(mu, rho). This function draws from the conditional posterior of rho, given
that the prior on rho is a uniform prior on the standard deviation.

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

postRho function

<dl><dt>muI</dt>
<dd>Numeric vector, log of individuals mean values.</dd>
<dt>xib</dt>
<dd>Numeric vector, result of X %*% Beta, same length as muI.</dd></dl>

Arguments

Sample a value from the full conditional posterior of rho — postRho

<dl>

<dt>muI</dt>
<dd>Numeric vector, log of individuals mean values.</dd>


<dt>xib</dt>
<dd>Numeric vector, result of X %*% Beta, same length as muI.</dd>

</dl>

postRho: Sample a value from the full conditional posterior of rho

Description

Usage

Value

Arguments