postCij

In our model the data are drawn from LogN(mu_i + log(c_ij), tau_i) The prior for
c_ij is a categorical prior with category probabilities pi1, ..., pik, and c_ij can
take values 1, ..., k where k is the length of pi. This function samples from the
full conditional posterior of all c_ijs, given vectors of equal length yijs, muis, tauis

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

postCij function

<dl><dt>yijs</dt>
<dd>Numeric Vector, cycle lengths</dd>
<dt>pi</dt>
<dd>Numeric vector, must sum to 1. Sampled probabilities for c_ijs</dd>
<dt>muis</dt>
<dd>Numeric vector, log of sampled mean for all individuals yijs</dd>
<dt>tauis</dt>
<dd>Numeric vector &gt; 0, sampled precision for all individuals yijs</dd></dl>

Arguments

Sample a vector of values from the full conditional posterior of the c_ij vector — postCij

<dl>

<dt>yijs</dt>
<dd>Numeric Vector, cycle lengths</dd>


<dt>pi</dt>
<dd>Numeric vector, must sum to 1. Sampled probabilities for c_ijs</dd>


<dt>muis</dt>
<dd>Numeric vector, log of sampled mean for all individuals yijs</dd>


<dt>tauis</dt>
<dd>Numeric vector &gt; 0, sampled precision for all individuals yijs</dd>

</dl>

postCij: Sample a vector of values from the full conditional posterior of the c_ij vector

Description

Usage

Value

Arguments