initbetaGaussian

Initial values for the regression coefficients obtained from normal linear regression model with lasso regularization for <code>simGaussian</code> data set.

Empirical Bayes variable selection via ICM/M algorithm for normal, binary logistic, and Cox's regression. The basic problem is to fit high-dimensional regression which sparse coefficients. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. More information can be found in the papers listed in the URL below.

Vitara Pungpapong

icmm

Empirical Bayes Variable Selection via ICM/M Algorithm

Min Zhang

Dabao Zhang

initbetaGaussian function

A data frame with 400 rows.<dl class="dl-horizontal">
 <dt><code>V1</code></dt><dd>a numeric vector of the regression coefficients.</dd>
 </dl>

Format

Initial values for the regression coefficients used in example for running ICM/M algorithm in normal linear regression  model — initbetaGaussian

A data frame with 400 rows.<dl class='dl-horizontal'>
 <dt><code>V1</code></dt><dd>a numeric vector of the regression coefficients.</dd>
 </dl>

initbetaGaussian: Initial values for the regression coefficients used in example for running ICM/M algorithm in normal linear regression model

Description

Usage

Arguments

Format

Examples