EM.mixture

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

Estimation of partial correlation matrix using ridge penalty
followed by thresholding and reestimation. Under multivariate Gaussian
assumption, the matrix constitutes an Gaussian graphical model (GGM).

Shannon Holloway

GGMridge

Gaussian Graphical Models Using Ridge Penalty Followed by
Thresholding and Reestimation

EM.mixture function

<dl><dt>p</dt>
<dd>A numeric vector representing partial correlation coefficients.</dd>
<dt>eta0</dt>
<dd>An initial value for the null proportion; 1-eta0 is the non-null 
proportion.</dd>
<dt>df</dt>
<dd>An initial value for the degrees of freedom of the exact null 
density.</dd>
<dt>tol</dt>
<dd>The tolerance level for convergence.</dd></dl>

Arguments

Author

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

<dl>

<dt>p</dt>
<dd>A numeric vector representing partial correlation coefficients.</dd>


<dt>eta0</dt>
<dd>An initial value for the null proportion; 1-eta0 is the non-null 
proportion.</dd>


<dt>df</dt>
<dd>An initial value for the degrees of freedom of the exact null 
density.</dd>


<dt>tol</dt>
<dd>The tolerance level for convergence.</dd>

</dl>

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

Description

Usage

Value

Arguments

Author

References