EM.mixture {GGMridge}

Estimation of the mixture distribution using EM algorithm

Description

Estimation of the parameters, null proportion, and degrees of freedom of the exact null density in the mixture distribution.

Usage

EM.mixture(p, eta0, df, tol)

Arguments

p

A numeric vector representing partial correlation coefficients.

eta0

An initial value for the null proportion; 1-eta0 is the non-null proportion.

df

An initial value for the degrees of freedom of the exact null density.

tol

The tolerance level for convergence.

Value

A list object containing

df

Estimated degrees of freedom of the null density.

eta0

Estimated null proportion.

iter

The number of iterations required to reach convergence.

Author(s)

Min Jin Ha

References

Schafer, J. and Strimmer, K. (2005). An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics, 21, 754–764.