asym_m_ng: Modified NG prior via FS scores

In NGBVS: Bayesian Variable Selection for SNP Data using Normal-Gamma

Modified NG prior via FS scores

Description

Modified Normal Gamma prior calculates the posterior distribution for the fine mapping cases-controls study. The number of case-controls must be greater than the number of SNPs.

Usage

asym_m_ng (y, data, FS, medstar = c(0.01, 0.0001), numb = 100, burnin = 1, every = 1)

Arguments

y	A vector of the pheontype, where takes 0s and 1s.
data	An N \times p finemap data, where N and p denote the samples and number of SNPs respectively.
FS	FS scores for each SNP and it takes value from 0 and 1 or NA for missing FS.
medstar	The value of M where M takes two values.
numb	Number of samples for each SNP.
burnin	The amount of burn-in for the MCMC sample.
every	The amount of thining for the MCMC sample.

Value

A list including:

alpha	A vector of the posterior distribution of the intercept.
beta	A matrix of the posterior distribution of the effect sizes.
psi	A matrix of the posterior distribution of ψ.
lambda	A vector of the posterior distribution of λ.
gammasq	A vector of the posterior distribution of γ^2.
W	A vector of the posterior distribution of W.
H	A vector of the posterior distribution of H.

Author(s)

Abulaziz Alenazi.

R implementation and documentation: Abulaziz Alenazi [e-mail].

Examples

set.seed(1)
data <- matrix(sample( c( 0, 1, 2 ), 500 * 30, replace = TRUE,
prob <- c( 0.35, 0.35, 0.3)), ncol = 30 )
FS <- sample( c( 0.1, 0.5, 0.7, NA ), ncol( data ), replace = TRUE)
asym_m_ng(y = rbinom(500, 1, 0.5), data = data, FS = FS)

NGBVS documentation built on Sept. 16, 2022, 5:06 p.m.