pbLRT: parametric boostrap LRT implementation
In statdivlab/happi: a hierarchical approach to pangenomics inference

pbLRT

R Documentation

parametric boostrap LRT implementation

Description

parametric boostrap LRT implementation

Usage

pbLRT(
  happi_out,
  max_iterations = 3000,
  min_iterations = 15,
  h0_param = 2,
  change_threshold = 0.1,
  epsilon = 0,
  method = "splines",
  firth = T,
  spline_df = 3,
  nstarts = 1,
  B = 1000
)

Arguments

`happi_out`	a happi results object
`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
`h0_param`	the column index in covariate that has beta=zero under the null
`change_threshold`	algorithm will terminate early if the likelihood changes by this percentage or less for 5 iterations in a row for both the alternative and the null
`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
`nstarts`	number of starts; Integer. Defaults to `1`. Number of starts for optimization.
`B`	number of bootstrap iterations