postBeta: Draw from Posterior Distribution for Beta Parameters

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

Draw from Posterior Distribution for Beta Parameters

Description

In our model mui follows a normal distribution with mean Xi^T %*% beta and precision rho. Additionally we assume that beta follows a mvnormal prior with mean 0 and precision (rho_Beta) * I.This function draws from the posterior distribution of beta under these assumptions.

Usage

postBeta(rhoBeta = 0.01, rho, Xi, muI)

Arguments

rhoBeta	A scalar representing the prior precision parameter for beta.
rho	A scalar representing the precision parameter.
Xi	A matrix of covariates, where each row represents an individual and each column represents a covariate.
muI	A vector where each element is the mean for individual i.

Details

This function assumes that Xi is a (num Individuals) x (dimension of beta) matrix of covariates.

Value

A vector representing a draw from the posterior distribution of beta parameters.

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