run_wasserman: # Wasserman split test function

In statdivlab/happi: a hierarchical approach to pangenomics inference

# Wasserman split test function

Description

# Wasserman split test function

Usage

run_wasserman(
  happi_results_object,
  firth = TRUE,
  spline_df = 3,
  max_iterations = 1000,
  change_threshold = 0.01,
  epsilon = 0,
  nstarts = 1,
  h0_param = 2
)

Arguments

happi_results_object	An object of class happi.
firth	use firth penalty? Default is TRUE.
spline_df	degrees of freedom (in addition to intercept) to use in monotone spline fit; default 3
max_iterations	the maximum number of EM steps that the algorithm will run for
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
nstarts	number of starts; Integer. Defaults to 1. Number of starts for optimization.
h0_param	the column index in covariate that has beta=zero under the null

Value

A list of Wasserman pvalues

statdivlab/happi documentation built on April 19, 2024, 2:04 a.m.