penalty

This function gives the penalty functions

Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.

Fei Zhou

interep

Interaction Analysis of Repeated Measure Data

penalty function

<dl><dt>x</dt>
<dd>matrix of covariates.</dd>
<dt>n</dt>
<dd>the sample size.</dd>
<dt>p</dt>
<dd>the number of predictors.</dd>
<dt>q</dt>
<dd>the number of environment factors.</dd>
<dt>beta</dt>
<dd>the coefficient vector.</dd>
<dt>lam1</dt>
<dd>the tuning parameter lambda1 for individual penalty.</dd>
<dt>pmethod</dt>
<dd>the penalization method. "mixed" refers to MCP penalty to individual main effects and group MCP penalty to interactions; "individual" means MCP penalty to all effects.</dd>
<dt>p1</dt>
<dd>the number of gene factors.</dd>
<dt>lam2</dt>
<dd>the tuning parameter lambda2 for group penalty.</dd></dl>

Arguments

This function gives the penalty functions — penalty

<dl>

<dt>x</dt>
<dd>matrix of covariates.</dd>


<dt>n</dt>
<dd>the sample size.</dd>


<dt>p</dt>
<dd>the number of predictors.</dd>


<dt>q</dt>
<dd>the number of environment factors.</dd>


<dt>beta</dt>
<dd>the coefficient vector.</dd>


<dt>lam1</dt>
<dd>the tuning parameter lambda1 for individual penalty.</dd>


<dt>pmethod</dt>
<dd>the penalization method. "mixed" refers to MCP penalty to individual main effects and group MCP penalty to interactions; "individual" means MCP penalty to all effects.</dd>


<dt>p1</dt>
<dd>the number of gene factors.</dd>


<dt>lam2</dt>
<dd>the tuning parameter lambda2 for group penalty.</dd>

</dl>

penalty: This function gives the penalty functions

Description

Usage

Value

Arguments