ordinalgmifs: An R Package for Ordinal Regression in High-dimensional Data Settings

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Authors Kellie J. Archer, Jiayi Hou, Qing Zhong Zhou, Kyle L Ferber, John G Layne, Amanda Elswick Gentry
Journal/Conference Name Cancer informatics
Paper Category
Paper 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.
Date of publication 2014
Code Programming Language R

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