getMixtureAssignmentEstimate function

Returns mixture assignment estimates for each gene

Returns mixture assignment estimates for each gene

Posterior estimates for the mixture assignment of specified genes

getMixtureAssignmentEstimate(parameter, gene.index, samples)

Arguments

  • 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

Returns

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

Details

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.

Examples

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 mixture assignment for all genes mixAssign <- getMixtureAssignmentEstimate(parameter = parameter, gene.index = 1:length(genome), samples = 1000) # get the mixture assignment for a subsample mixAssign <- getMixtureAssignmentEstimate(parameter = parameter, gene.index = 5:100, samples = 1000) # or mixAssign <- getMixtureAssignmentEstimate(parameter = parameter, gene.index = c(10, 30:50, 3, 90), samples = 1000) ## End(Not run)