mh_chain: Metropolis-Hasting for a single peptide

In liesb/BIITE: Bayesian Immunogenicity Inference Tool for ELISpot

Description

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

Usage

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

Arguments

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

liesb/BIITE documentation built on May 21, 2019, 6:13 a.m.