postMui

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(x_i^T %*% beta, rho). This function draws from the conditional posterior of mu_i.

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

postMui function

<dl><dt>yij</dt>
<dd>Numeric vector, cycle lengths for a single individual</dd>
<dt>cij</dt>
<dd>Positive Integer vector, a sampled vector of length(yij) where the corresponding
values in cij indicate a sampled number of TRUE cycles in each cycle length given by yij</dd>
<dt>taui</dt>
<dd>Numeric &gt; 0, A sampled precision for the yijs</dd>
<dt>xib</dt>
<dd>Numeric, result of multiplying x_i^T %*% beta (single value, not vector)</dd>
<dt>rho</dt>
<dd>Numeric &gt; 0, sampled prior precision of mu_i</dd></dl>

Arguments

Sample a value from the full conditional posterior of mu_i — postMui

<dl>

<dt>yij</dt>
<dd>Numeric vector, cycle lengths for a single individual</dd>


<dt>cij</dt>
<dd>Positive Integer vector, a sampled vector of length(yij) where the corresponding
values in cij indicate a sampled number of TRUE cycles in each cycle length given by yij</dd>


<dt>taui</dt>
<dd>Numeric &gt; 0, A sampled precision for the yijs</dd>


<dt>xib</dt>
<dd>Numeric, result of multiplying x_i^T %*% beta (single value, not vector)</dd>


<dt>rho</dt>
<dd>Numeric &gt; 0, sampled prior precision of mu_i</dd>

</dl>

postMui: Sample a value from the full conditional posterior of mu_i

Description

Usage

Value

Arguments

Details