likVec: Monte Carlo estimate of negative marginal log-likelihood of...

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

Monte Carlo estimate of negative marginal log-likelihood of Li model

Description

This function calculates a Monte Carlo estimate of the negative marginal log-likelihood of the given hyperparameters for the generative model from Li et al. (2022). It samples M instances of the parameters from the given distributions and averages the the likelihoods, giving a marginal likelihood for the hyperparameters.

Usage

likVec(
  pars = c(kappa = 180, gamma = 6, alpha = 2, beta = 20),
  S = 10,
  M = 1000,
  cycleDat,
  verbose = FALSE,
  ...
)

Arguments

pars	Named numeric vector of hyperparameters containing the elements: kappa, gamma, alpha, beta. NOTE: MUST BE IN CORRECT ORDER. kappa: Numeric value, shape parameter of Gamma distribution for Lambda_i. gamma: Numeric value, rate parameter of Gamma distribution for Lambda_i. alpha: Numeric value, shape1 parameter of Beta distribution for Pi_i. beta: Numeric value, shape2 parameter of Beta distribution for Pi_i.
S	Integer, maximum number of allowed skips in the model.
M	Integer specifying the number of Monte Carlo iterations.
cycleDat	Data frame containing information about individuals and their tracked cycles.
verbose	Logical with default FALSE. If true, prints extra info while running.
...	Does nothing.

Value

Numeric value representing the Monte Carlo estimate of the negative marginal log-likelihood.

References

Li, Kathy, et al. "A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking." Journal of the American Medical Informatics Association 29.1 (2022): 3-11.

skipTrack documentation built on Sept. 10, 2025, 10:27 a.m.