reformat

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

Recently, regularized variable selection has emerged as a powerful tool to identify and dissect gene-environment interactions. Nevertheless, in longitudinal studies with high dimensional genetic factors, regularization methods for G×E interactions have not been systematically developed. In this package, we provide the implementation of sparse group variable selection, based on both the quadratic inference function (QIF) and generalized estimating equation (GEE), to accommodate the bi-level selection for longitudinal G×E studies with high dimensional genomic features. Alternative methods conducting only the group or individual level selection have also been included. The core modules of the package have been developed in C++.

Fei Zhou

springer

Sparse Group Variable Selection for Gene-Environment
Interactions in the Longitudinal Study

reformat function

<dl><dt>k</dt>
<dd>the number of repeated measurements/time points.</dd>
<dt>y</dt>
<dd>the longitudinal response.</dd>
<dt>x</dt>
<dd>a matrix of predictors, consisting of clinical covariates, genetic and environment factors, as well as gene-environment interactions.</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 measurements/time points.</dd>


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


<dt>x</dt>
<dd>a matrix of predictors, consisting of clinical covariates, genetic and environment factors, as well as gene-environment interactions.</dd>

</dl>

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

Description

Usage

Arguments