bpr_EM: EM algorithm for BPR mixture model
In andreaskapou/mpgex: Methylation Profiles predict Gene Expression

Description Usage Arguments

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

bpr_EM(x, K = 2, pi_k = NULL, w = NULL, basis = NULL,
  em_max_iter = 100, epsilon_conv = 1e-05, opt_method = "CG",
  opt_itnmax = 100, 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`
`em_max_iter`	Integer denoting the maximum number of EM iterations.
`epsilon_conv`	Numeric denoting the convergence parameter for EM.
`opt_method`	The optimization method to be used. See `optim` for possible methods. Default is 'CG'.
`opt_itnmax`	Optional argument giving the maximum number of iterations for the corresponding method. See `optim` for details.
`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