PGEE: An R package for analysis of longitudinal data with high-dimensional covariates

Gul Inan, Lan Wang

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

We introduce an R package PGEE that implements the penalized generalized estimating equations (GEE) procedure proposed byWang et al. (2012) to analyze longitudinal data with a large number of covariates. The PGEE package includes three main functions: CVfit, PGEE, and MGEE. The CVfit function computes the cross-validated tuning parameter for penalized generalized estimating equations. The function PGEE performs simultaneous estimation and variable selection for longitudinal data with high-dimensional covariates; whereas the function MGEE fits unpenalized GEE to the data for comparison. The R package PGEE is illustrated using a yeast cell-cycle gene expression data set.

Original languageEnglish (US)
Pages (from-to)393-402
Number of pages10
JournalR Journal
Volume9
Issue number1
StatePublished - Jun 1 2017

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Longitudinal Data
Generalized Estimating Equations
Covariates
High-dimensional
Simultaneous Estimation
Cell Cycle
Parameter Tuning
Variable Selection
Gene Expression Data
Gene expression
Yeast
Tuning
Cells
Generalized estimating equations
Longitudinal data

Cite this

PGEE : An R package for analysis of longitudinal data with high-dimensional covariates. / Inan, Gul; Wang, Lan.

In: R Journal, Vol. 9, No. 1, 01.06.2017, p. 393-402.

Research output: Contribution to journalArticle

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