approximateSimplePosterior {EvidenceSynthesis} R Documentation

## Approximate simple Bayesian posterior

### Description

Approximate a Bayesian posterior from a Cyclops likelihood profile and normal prior using the Markov chain Monte Carlo engine BEAST.

### Usage

approximateSimplePosterior(
likelihoodProfile,
chainLength = 1100000,
burnIn = 1e+05,
subSampleFrequency = 100,
priorMean = 0,
priorSd = 0.5,
startingValue = 0,
seed = 1
)


### Arguments

 likelihoodProfile Named vector containing grid likelihood data from Cyclops. chainLength Number of MCMC iterations. burnIn Number of MCMC iterations to consider as burn in. subSampleFrequency Subsample frequency for the MCMC. priorMean Prior mean for the regression parameter priorSd Prior standard deviation for the regression parameter startingValue Initial state for regression parameter seed Seed for the random number generator.

### Value

A data frame with the point estimates and 95% credible intervals for the regression parameter. Attributes of the data frame contain the MCMC trace for diagnostics.

### Examples

# Simulate some data for this example:
population <- simulatePopulations(createSimulationSettings(nSites = 1))[[1]]

# Fit a Cox regression at each data site, and approximate likelihood function:
cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
data = population,
modelType = "cox")
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
likelihoodProfile <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "grid")

# Run MCMC
mcmcTraces <- approximateSimplePosterior(likelihoodProfile = likelihoodProfile,
priorMean = 0, priorSd = 100)

# Report posterior expectation
mean(mcmcTraces\$theta)

# (Estimates in this example will vary due to the random simulation)



[Package EvidenceSynthesis version 0.3.0 Index]