getExpressionEstimates: Returns the estimated phi posterior for a gene
In AnaCoDa: Analysis of Codon Data under Stationarity using a Bayesian Framework

Description Usage Arguments Details Value Examples

Posterior estimates for the phi value of specified genes

getExpressionEstimates(
  parameter,
  gene.index,
  samples,
  quantiles = c(0.025, 0.975),
  genome = NULL
)

`parameter`	on object created by `initializeParameterObject`.
`gene.index`	a integer or vector of integers representing the gene(s) of interesst.
`samples`	number of samples for the posterior estimate
`quantiles`	vector of quantiles, (default: c(0.025, 0.975))
`genome`	if genome is given, then will include gene ids in output (default is NULL)

The returned vector is unnamed as gene ids are only stored in the genome object, but the gene.index vector can be used to match the assignment to the genome.

returns a vector with the mixture assignment of each gene corresbonding to gene.index in the same order as the genome.

 
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)

# get the estimated expression values for all genes based on the mixture 
# they are assigned to at each step
estimatedExpression <- getExpressionEstimates(parameter, 1:length(genome), 1000)

## End(Not run)