DictTestFixedP: Fit Two-Class Model with Fixed Prior Probability using Shared...

In lockEF/BayesianScreening: Perform Bayesian Screening with Gene Dependence

Description

Estimates posterior probability of a difference between two sample groups for each genomic variable (e.g., methylation site), using shared kernels and a given prior probability of equality. Returns the results for each MCMC iteration, including the kernel weights.

Usage

DictFitTestFixedP(X,Class,mu,Sigma,pA=0.5,Concentration = 0.5,NumDraws = 1000)

Arguments

X	A matrix in which rows represent variables (e.g., methylation sites) and columns represent samples. The entries of the matrix must be continuous between 0 and 1.
Class	A vector giving a class label for each sample.
mu	Vector of kernel means.
Sigma	Vector of kernel standard deviations
Concentration	Dirichlet hyperparameter for kernel weights
NumDraws	Number of MCMC draws for posterior inference.

Value

Returns an object with the following values:

postDraws	M X NumDraws array, giving the posterior probability of no difference for each marker, for each MCMC draw.
tao0Draws	M X K X NumDraws array, giving the K kernel weights for each marker, for each MCMC draw, for class 0.
tao1Draws	M X K X NumDraws array, giving the K kernel weights for each marker, for each MCMC draw, for class 1.

Author(s)

Eric F. Lock

References

Lock, E. F. & Dunson, D. B. (2016). Bayesian genome- and epigenome-wide association studies with gene level dependence. Preprint.

Lock, E. F. & Dunson, D. B. (2015). Shared kernel Bayesian screening. Biometrika, 102 (4): 829-842.

lockEF/BayesianScreening documentation built on May 24, 2020, 11:50 p.m.