PGEE: An R Package for Analysis of Longitudinal Data with High-Dimensional Covariates

被引：0
|
作者：
Inan, Gul [1 ]
Wang, Lan [2 ]
机构：
[1] Middle East Tech Univ, Dept Stat, TR-06800 Ankara, Turkey
[2] Univ Minnesota, Sch Stat, Minneapolis, MN 55455 USA
来源：
R JOURNAL | 2017年 / 9卷 / 01期
基金：
美国国家科学基金会;
关键词：
GENERALIZED ESTIMATING EQUATIONS; PARTIAL LINEAR-MODELS; DIVERGING NUMBER; SACCHAROMYCES-CEREVISIAE; COORDINATE DESCENT; VARIABLE SELECTION; REGRESSION-MODELS; CORRELATED DATA; GEE;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：393 / 402
页数：10
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