runMCMC {AnaCoDa}

Run MCMC

Description

runMCMC will run a monte carlo markov chain algorithm for the given mcmc, genome, and model objects to perform a model fitting.

Usage

runMCMC(mcmc, genome, model, ncores = 1, divergence.iteration = 0)

Arguments

mcmc

MCMC object that will run the model fitting algorithm.

genome

Genome that the model fitting will run on. Should be the same genome associated with the parameter and model objects.

model

Model to run the fitting on. Should be associated with the given genome.

ncores

Number of cores to perform the model fitting with. Default value is 1.

divergence.iteration

Number of steps that the initial conditions can diverge from the original conditions given. Default value is 0.

Details

runMCMC will run for the number of samples times the number thinning given when the mcmc object is initialized. Updates are provided every 100 steps, and the state of the chain is saved every thinning steps.

Value

This function has no return value.

Examples

#fitting a model to a genome using the runMCMC function

genome_file <- system.file("extdata", "genome.fasta", package = "AnaCoDa")

genome <- initializeGenomeObject(file = genome_file)
sphi_init <- c(1,1)
numMixtures <- 2
geneAssignment <- c(rep(1,floor(length(genome)/2)),rep(2,ceiling(length(genome)/2)))
parameter <- initializeParameterObject(genome = genome, sphi = sphi_init, 
                                       num.mixtures = numMixtures, 
                                       gene.assignment = geneAssignment, 
                                       mixture.definition = "allUnique")
model <- initializeModelObject(parameter = parameter, model = "ROC")
samples <- 2500
thinning <- 50
adaptiveWidth <- 25
mcmc <- initializeMCMCObject(samples = samples, thinning = thinning, 
                             adaptive.width=adaptiveWidth, est.expression=TRUE, 
                             est.csp=TRUE, est.hyper=TRUE, est.mix = TRUE) 
divergence.iteration <- 10
## Not run: 
runMCMC(mcmc = mcmc, genome = genome, model = model, 
        ncores = 4, divergence.iteration = divergence.iteration)

## End(Not run)