EM.mixture: Estimation of the mixture distribution using EM algorithm

In GGMridge: Gaussian Graphical Models Using Ridge Penalty Followed by Thresholding and Reestimation

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.

GGMridge documentation built on Nov. 25, 2023, 1:08 a.m.