bpr_EM: EM algorithm for BPR mixture model

Description Usage Arguments

View source: R/bpr_EM.R

Description

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.

Usage

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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)

Arguments

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


andreaskapou/mpgex documentation built on Nov. 25, 2017, 8:08 a.m.