mh_chain: Metropolis-Hasting for a single peptide
In liesb/BIITE: Bayesian Immunogenicity Inference Tool for ELISpot

Implementation of the Metropolis-Hastings algorithm to find posterior distributions of pHLA immunogenicity for a single peptide

1 2	mh_chain(eli.dat, mol.names, init_h, max_steps = 5000, pep, unif.prop = T, radius = 0.1, p.df = NULL)

`eli.dat`	ELISpot dataset
`mol.names`	Names of the HLA molecules in the ELISpot dataset
`init_h`	Initial state of the MH chain, can be produced randomly by generate_random_hypothesis
`max_steps`	The required length of the chain. The default value, 5000, is too low but this was done for debugging reasons.
`pep`	Peptide currently being processed
`unif.prop`	If TRUE, choose proposal states from [0,1]^m
`radius`	If unif.prop is FALSE, we look for a proposal state in a hypercube around h restricted by radius. Should be trained such that the acceptance rate of the chain is around 50%, see example.r.
`p.df`	Dataframe which describes the parameters of the prior distributions for each HLA