e2_Monte_Carlo_EM: Internal: e2_Monte_Carlo_EM

In GCPBayes: Bayesian Meta-Analysis of Pleiotropic Effects Using Group Structure

Internal: e2_Monte_Carlo_EM

Description

Internal: e2_Monte_Carlo_EM

Usage

e2_Monte_Carlo_EM(
  Betah,
  Sigmah,
  kappa0 = kappa0,
  kappastar0 = kappastar0,
  sigma20 = sigma20,
  s20 = s20,
  m,
  K,
  a1 = a1,
  a2 = a2,
  d1 = d1,
  d2 = d2,
  c1 = c1,
  c2 = c2,
  e2 = e2,
  snpnames,
  genename
)

Arguments

Betah	A matrix of dimension K*m represents the regression coefficients. Each row of this matrix includes the regression coefficients for each trait.
Sigmah	A symmetric block-diagonal matrix of dimension K*m is used. Each block of this matrix shows a positive definite covariance matrix which is an estimated covariance matrix of each trait.
kappa0	Initial value for kappa.
kappastar0	Initial value for kappastar.
sigma20	Initial value for sigma2.
s20	Initial value for s2.
m	Number of variables in the group.
K	Number of traits.
a1, a2	Hyperparameters of kappa. Default is a1=0.1 and a2=0.1.
d1, d2	Hyperparameters of sigma2. Default is d1=0.1 and d2=0.1.
c1, c2	Hyperparameters of kappastar. Default is c1=0.1 and c2=0.1.
e2	Initial value for doing Monte Carlo EM algorithm to estimate hyperparameter of s2.
snpnames	Names of variables for the group.
genename	Name of group.

GCPBayes documentation built on May 29, 2024, 3:30 a.m.