title: ordinalgmifs: An R Package for Ordinal Regression in High-dimensional Data Settings.
abstract: High-throughput genomic assays are performed using tissue samples with the goal of classifying the samples as normal < pre-malignant < malignant or by stage of cancer using a small set of molecular features. In such cases, molecular features monotonically associated with the ordinal response may be important to disease development; that is, an increase in the phenotypic level (stage of cancer) may be mechanistically linked through a monotonic association with gene expression or methylation levels. Though traditional ordinal response modeling methods exist, they assume independence among the predictor variables and require the number of samples (n) to exceed the number of covariates (P) included in the model. In this paper, we describe our ordinalgmifs R package, available from the Comprehensive R Archive Network, which can fit a variety of ordinal response models when the number of predictors (P) exceeds the sample size (n). R code illustrating usage is also provided.
authors.affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Kellie J
authors.initials: KJ
authors.lastname: Archer
authors.affiliation: Clinical and Translational Research Institute, University of California San Diego, San Diego, CA.
authors.forename: Jiayi
authors.initials: J
authors.lastname: Hou
authors.affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Qing
authors.initials: Q
authors.lastname: Zhou
authors.affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Kyle
authors.initials: K
authors.lastname: Ferber
authors.affiliation: Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: John G
authors.initials: JG
authors.lastname: Layne
authors.affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Amanda E
authors.initials: AE
authors.lastname: Gentry
keywords: R; high-dimensional features; ordinal response; penalized models