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

We introduce an R package PGEE that implements the penalized generalized estimating equations (GEE) procedure proposed by Wang 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.

Gul Inan , Lan Wang
2017-06-08

CRAN packages used

gee, geepack, PGEE, MASS, mvtnorm, ncvreg, penalized, glmnet, rqPen

CRAN Task Views implied by cited packages

MachineLearning, SocialSciences, Distributions, Econometrics, Multivariate, Survival, Environmetrics, Finance, NumericalMathematics, Psychometrics, Robust

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Citation

For attribution, please cite this work as

Inan & Wang, "PGEE: An R Package for Analysis of Longitudinal Data with High-Dimensional Covariates", The R Journal, 2017

BibTeX citation

@article{RJ-2017-030,
  author = {Inan, Gul and Wang, Lan},
  title = {PGEE: An R Package for Analysis of Longitudinal Data with High-Dimensional Covariates},
  journal = {The R Journal},
  year = {2017},
  note = {https://doi.org/10.32614/RJ-2017-030},
  doi = {10.32614/RJ-2017-030},
  volume = {9},
  issue = {1},
  issn = {2073-4859},
  pages = {393-402}
}