postPii: Compute M-H draw for pi_i in Li algorithm

In skipTrack: A Bayesian Hierarchical Model that Controls for Non-Adherence in Mobile Menstrual Cycle Tracking

Compute M-H draw for pi_i in Li algorithm

Description

This performs a Metropolis-Hastings draw for pi_i, assuming s_ij follows a truncated geometric distribution with parameters pi_i and S. The proposal distribution for pi_i is Beta(alpha, beta).

Usage

postPii(sij, currentPii, priorA, priorB, S)

Arguments

sij	Vector of cycle skip indicators for individual i
currentPii	Current value of pi_i
priorA	Hyperparameter alpha.
priorB	Hyperparameter beta.
S	Maximum number of skips allowed in algorithm

Value

Draw for pi_i, repeated for the number of observations from individual i

skipTrack documentation built on April 3, 2025, 6:21 p.m.