bayes.model.aver: Bayesian Model Averaging
In metaMix: Bayesian Mixture Analysis for Metagenomic Community Profiling

Perform Bayesian Model Averaging. We concentrate on the chain with temperature=1 , i.e the untempered posterior, to study the distribution over the model choices and perform model averaging. We consider as present the species that have a posterior probability greater than 0.9. We then fit the mixture model with these species in order to obtain relative abundances and read classification probabilities. A tab seperated file that has a species summary is produced, as well as log-likelihood traceplots and cumulative histogram plots.

bayes.model.aver.explicit is the same function as bayes.model.aver with a more involved syntax.

bayes.model.aver(step2, step3, taxon.name.map = NULL,
  poster.prob.thr = 0.9, burnin = 0.4)

bayes.model.aver.explicit(result, pij.sparse.mat, read.weights, outDir,
  gen.prob.unknown, taxon.name.map = NULL, poster.prob.thr = 0.9,
  burnin = 0.4)

`step2`	list. The output from reduce.space(), i.e the second step of the pipeline. Alternatively, it can be a character string containing the path name of the ".RData" file where step2 list was saved.
`step3`	list. The output from parallel.temper(), i.e the third step of the pipeline. Alternatively, it can be a character string containing the path name of the ".RData" file where step3 list was saved.
`taxon.name.map`	The 'names.dmp' taxonomy names file, mapping each taxon identifier to the corresponding scientific name. It can be downloaded from ftp://ftp.ncbi.nih.gov/pub/taxonomy/taxdump.tar.gz
`poster.prob.thr`	Posterior probability of presence of species threshold for reporting in the species summary.
`burnin`	Percentage of burn in iterations, default value is 0.4
`result`	The list produced by parallel.temper() (or paraller.temper.nucl()) . It holds a detailed record for each chain, what moves were proposed, which were accepted and which were rejected as well the log-likelihood through the iterations.
`pij.sparse.mat`	see ?reduce.space
`read.weights`	see ?reduce.space
`outDir`	see ?reduce.space
`gen.prob.unknown`	see ?reduce.space

## See vignette for more details

## Not run: 
# Either load the object created by previous steps
data(step2)   ## example output of step2, i.e reduce.space()
data(step3)   ## example ouput of step3, i.e  parallel.temper()
step4<-bayes.model.aver(step2=step2, step3=step3, taxon.name.map="pathtoFile/taxon.file")

# or alternatively point to the location of the step2.RData and step3.RData objects
step4<-bayes.model.aver(step2="pathtoFile/step2.RData", step3="pathtoFile/step3.RData",
                        taxon.name.map="pathtoFile/taxon.file")


## End(Not run)