GGMridge (version 1.4)

Gaussian Graphical Models Using Ridge Penalty Followed by Thresholding and Reestimation

Description

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).

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Version

Install

install.packages('GGMridge')

Monthly Downloads

227

Version

1.4

License

GPL-2

Maintainer

Shannon Holloway

Last Published

November 24th, 2023

Functions in GGMridge (1.4)

lambda.TargetD

Shrinkage Estimation of a Covariance Matrix Toward an Identity Matrix

lambda.pcut.cv1

Choose the Tuning Parameter of the Ridge Inverse and Thresholding Level of the Empirical p-Values. Calculate total prediction error for test data after fitting partial correlations from train data for all values of lambda and pcut.

ne.lambda.cv

Choose the Tuning Parameter of a Ridge Regression Using Cross-Validation

lambda.cv

Choose the Tuning Parameter of the Ridge Inverse

lambda.pcut.cv

Choose the Tuning Parameter of the Ridge Inverse and Thresholding Level of the Empirical p-Values

scaledMat

Scale a square matrix

getEfronp

Estimation of empirical null distribution.

EM.mixture

Estimation of the mixture distribution using EM algorithm

R.separate.ridge

Estimation of Partial Correlation Matrix Using p Separate Ridge Regressions.

ksStat

The Kolmogorov-Smirnov Statistic for p-Values

simulateData

Generate Simulation Data from a Random Network.

structuredEstimate

Estimation of Partial Correlation Matrix Given Zero Structure.

transFisher

Fisher's Z-Transformation