bpr_fdmm: Gibbs sampling algorithm for BPR mixture model
In andreaskapou/mpgex: Methylation Profiles predict Gene Expression

Description Usage Arguments

bpr_fdmm implements the Gibbs sampling algorithm for performing clustering on DNA methylation profiles, where the observation model is the Binomial distributed Probit Regression function, bpr_likelihood.

bpr_fdmm(x, K = 2, pi_k = NULL, w = NULL, basis = NULL,
  w_0_mean = NULL, w_0_cov = NULL, dir_a = NULL, gibbs_nsim = 5000,
  gibbs_burn_in = 1000, is_parallel = TRUE, no_cores = NULL,
  is_verbose = FALSE)

`x`	A list of elements of length N, where each element is an L x 3 matrix of observations, where 1st column contains the locations. The 2nd and 3rd columns contain the total trials and number of successes at the corresponding locations, repsectively.
`K`	Integer denoting the number of clusters K.
`pi_k`	Vector of length K, denoting the mixing proportions.
`w`	A MxK matrix, where each column contains the basis function coefficients for the corresponding cluster.
`basis`	A 'basis' object. E.g. see `polynomial.object`
`w_0_mean`	The prior mean hyperparameter for w
`w_0_cov`	The prior covariance hyperparameter for w
`dir_a`	The Dirichlet concentration parameter, prior over pi_k
`gibbs_nsim`	Argument giving the number of simulations of the Gibbs sampler.
`gibbs_burn_in`	Argument giving the burn in period of the Gibbs sampler.
`is_parallel`	Logical, indicating if code should be run in parallel.
`no_cores`	Number of cores to be used, default is max_no_cores - 1.
`is_verbose`	Logical, print results during EM iterations