run_em: General E-M Algorithm
In statdivlab/happi: a hierarchical approach to pangenomics inference

run_em

R Documentation

General E-M Algorithm

Description

General E-M Algorithm

Usage

run_em(
  outcome = outcome,
  quality_var = quality_var,
  change_threshold = change_threshold,
  max_iterations = max_iterations,
  min_iterations = min_iterations,
  epsilon = epsilon,
  method = method,
  firth = firth,
  spline_df = spline_df,
  nn = nn,
  em_covariate = NULL,
  em_estimates = NULL,
  em_estimated_beta = NULL,
  em_estimated_basis_weights = NULL,
  em_estimated_ftilde = NULL,
  em_estimated_p = NULL,
  em_fitted_xbeta = NULL,
  em_estimated_f = NULL
)

Arguments

`outcome`	length-n vector; this is the vector of a target gene's presence/absence; should be coded as 0 or 1
`quality_var`	length-n vector; this is the quality variable vector, currently p = 1 TODO(turn into n x q matrix)
`change_threshold`	algorithm will terminate early if the likelihood changes by this percentage or less for 5 iterations in a row for both th
`max_iterations`	the maximum number of EM steps that the algorithm will run for
`min_iterations`	the minimum number of EM steps that the algorithm will run for
`epsilon`	probability of observing a gene when it should be absent; probability between 0 and 1
`method`	method for estimating f. Defaults to "splines" which fits a monotone spline with df determined by argument spline_df; "isotone" for isotonic regression fit
`firth`	use firth penalty? Default is TRUE.
`spline_df`	degrees of freedom (in addition to intercept) to use in monotone spline fit; default 3
`nn`	length(outcome)
`em_covariate`	n x p matrix; this is the matrix for the primary predictor/covariate of interest
`em_estimates`	log likelihood estimates
`em_estimated_beta`	estimated betas
`em_estimated_basis_weights`	estimated basis weights
`em_estimated_ftilde`	estimated f_tilde aka logit(estimated_f)
`em_estimated_p`	estimated probablities
`em_fitted_xbeta`	fitted betas
`em_estimated_f`	estimated f's