reformat

This function changes the format of the longitudinal data from wide format to long format

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

reformat function

<dl><dt>k</dt>
<dd>the number of repeated measurement.</dd>
<dt>y</dt>
<dd>the longitudinal response.</dd>
<dt>x</dt>
<dd>a matrix of predictors, consisting of omics and environment factors, as well as their interactions. In
the case study, the omics measurements are lipidomics data.</dd></dl>

Arguments

This function changes the format of the longitudinal data from wide format to long format — reformat

<dl>

<dt>k</dt>
<dd>the number of repeated measurement.</dd>


<dt>y</dt>
<dd>the longitudinal response.</dd>


<dt>x</dt>
<dd>a matrix of predictors, consisting of omics and environment factors, as well as their interactions. In
the case study, the omics measurements are lipidomics data.</dd>

</dl>

reformat: This function changes the format of the longitudinal data from wide format to long format

Description

Usage

Arguments