optimize_pi: Estimate mixture weights by maximum (penalized) likelihood

In stephenslab/mashr: Multivariate Adaptive Shrinkage

Estimate mixture weights by maximum (penalized) likelihood

Description

This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.

Usage

optimize_pi(
  matrix_lik,
  pi_init = NULL,
  prior = NULL,
  optmethod = c("mixSQP", "mixIP", "mixEM", "cxxMixSquarem"),
  control = list()
)

Arguments

matrix_lik	a matrix of likelihoods, where the (i,k)th entry is the probability of observation i given it came from component k of g
pi_init	numeric vector specifying value from which to initialize optimization
prior	numeric vector specifying prior to use in the penalized likelihood
optmethod	a string, giving name of optimization function to use
control	a list of parameters to be passed to optmethod

Value

numeric vector specifying the optimal mixture weights

stephenslab/mashr documentation built on Oct. 19, 2023, 4:14 p.m.