Approximate a Bayesian posterior from a Cyclops likelihood profile and normal prior
using the Markov chain Monte Carlo engine BEAST.
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
)
#> Detected data following grid distribution
#> Performing MCMC. This may take a while
# Report posterior expectation
mean(mcmcTraces$theta)
#> [1] 0.4205803
# (Estimates in this example will vary due to the random simulation)