postPi

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
posterior of pi = pi1, ..., pik, assuming that pi follows Dirichlet(priorAlphas)

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

postPi function

<dl><dt>ci</dt>
<dd>Integer vector, all of the sampled cij values for all individuals</dd>
<dt>priorAlphas</dt>
<dd>Numeric vector, prior dirichlet parameters for pi</dd></dl>

Arguments

Sample a value from the full conditional posterior of pi — postPi

<dl>

<dt>ci</dt>
<dd>Integer vector, all of the sampled cij values for all individuals</dd>


<dt>priorAlphas</dt>
<dd>Numeric vector, prior dirichlet parameters for pi</dd>

</dl>

postPi: Sample a value from the full conditional posterior of pi

Description

Usage

Value

Arguments