wasserman_normal_prds_sim: Normal PRDS simulation: Covariate is effect size under...

In nignatiadis/ihwPaper: Reproduce figures in IHW paper

Description

Normal PRDS simulation: Covariate is effect size under alternative, there are latent factors driving PRDS correlations among hypotheses

Usage

wasserman_normal_prds_sim(
  m,
  pi0,
  rho = 0,
  latent_factors = 1,
  xi_min = 0,
  xi_max = 2.5,
  seed = NULL
)

wasserman_normal_prds_sim_fun(
  m,
  pi0,
  rho = 0,
  latent_factors = 1,
  xi_min = 0,
  xi_max = 2.5
)

Arguments

m	Integer, total number of hypotheses
pi0	Numeric, proportion of null hypotheses
rho	Numeric, correlation between z-scores of hypotheses driven by same latent factor
latent_factors	Integer, number of latent factors driving the correlations
xi_min, xi_max	Numeric, covariates are drawn as uniform on xi_min, xi_max
seed	Integer, Random seed to be used for simulation (default: NULL, i.e. RNG state will be used as is)

Value

A data frame containing all information about the simulation experiment

Functions

• wasserman_normal_prds_sim_fun: Creates a closure function for a given seed

Examples

sim_df <- wasserman_normal_prds_sim(20000,0.9, rho=0.1)

nignatiadis/ihwPaper documentation built on Jan. 18, 2021, 3:13 p.m.