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 Gammp prior calculates the posterior distribution for the fine mapping study. The number of individuals must be greater than the number of SNPs.

Usage

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

Arguments

y	A vector of the pheontype.
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.
sigmasq	A vector of the posterior distribution of σ^2.
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(rnorm(500 * 30), ncol = 30)
FS <- sample( c( 0.1, 0.5, 0.7, NA ), ncol( data ), replace = TRUE)
m_ng(y = rnorm( 500 ), data = data, FS = FS)

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