getCSPEstimates {AnaCoDa}

Return Codon Specific Paramters (or write to csv) estimates as data.frame

Description

getCSPEstimates returns the codon specific parameter estimates for a given parameter and mixture or write it to a csv file.

Usage

getCSPEstimates(
  parameter,
  filename = NULL,
  mixture = 1,
  samples = 10,
  relative.to.optimal.codon = T,
  report.original.ref = T,
  log.scale = F
)

Arguments

parameter

parameter an object created by initializeParameterObject.

filename

Posterior estimates will be written to file (format: csv). Filename will be in the format <parameter_name>_<filename>.csv.

mixture

estimates for which mixture should be returned

samples

The number of samples used for the posterior estimates.

relative.to.optimal.codon

Boolean determining if parameters should be relative to the preferred codon or the alphabetically last codon (Default=TRUE). Only applies to ROC and FONSE models

report.original.ref

Include the original reference codon (Default = TRUE). Note this is only included for the purposes of simulations, which expect the input parameter file to be in a specific format. Later version of AnaCoDa will remove this.

log.scale

Calculate posterior means, standard deviation, and posterior probability intervals on the natural log scale. Should be used for PA and PANSE models only.

Value

returns a list data.frame with the posterior estimates of the models codon specific parameters or writes it directly to a csv file if filename is specified

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)

## return estimates for codon specific parameters
csp_mat <- getCSPEstimates(parameter)

# write the result directly to the filesystem as a csv file. No values are returned
getCSPEstimates(parameter, filename=file.path(tempdir(), "test.csv"))


## End(Not run)