runMCMC {AnaCoDa} | R Documentation |
runMCMC
will run a monte carlo markov chain algorithm
for the given mcmc, genome, and model objects to perform a model fitting.
runMCMC(mcmc, genome, model, ncores = 1, divergence.iteration = 0)
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. |
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.
This function has no return value.
#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)