approximateHierarchicalNormalPosterior: Approximate Bayesian posterior for hierarchical Normal model

In OHDSI/EvidenceSynthesis: Synthesizing Causal Evidence in a Distributed Research Network

Approximate Bayesian posterior for hierarchical Normal model

Description

Approximate a Bayesian posterior from a set ofCyclops likelihood profiles under a hierarchical normal model using the Markov chain Monte Carlo engine BEAST.

Usage

approximateHierarchicalNormalPosterior(
  likelihoodProfiles,
  chainLength = 1100000,
  burnIn = 1e+05,
  subSampleFrequency = 100,
  effectPriorMean = 0,
  effectPriorSd = 0.5,
  nu0 = 1,
  sigma0 = 1,
  effectStartingValue = 0,
  precisionStartingValue = 1,
  seed = 1
)

Arguments

likelihoodProfiles	List of grid likelihoods profiled with Cyclops.
chainLength	Number of MCMC iterations.
burnIn	Number of MCMC iterations to consider as burn in.
subSampleFrequency	Subsample frequency for the MCMC.
effectPriorMean	Prior mean for global parameter
effectPriorSd	Prior standard deviation for the global parameter
nu0	Prior "sample size" for precision (with precision ~ gamma(nu0/2, nu0*sigma0/2))
sigma0	Prior "variance" for precision (with precision ~ gamma(nu0/2, nu0*sigma0/2))
effectStartingValue	Initial value for global & local parameter
precisionStartingValue	Initial value for the precision
seed	Seed for the random number generator.

Value

A data frame with the point estimates and 95% credible intervals for the the global and local parameter, as well as the global precision. Attributes of the data frame contain the MCMC trace for diagnostics.

Examples

# TBD

OHDSI/EvidenceSynthesis documentation built on Sept. 7, 2024, 12:09 a.m.