Ordinalgmifs: An R Package for Ordinal Regression in High-dimensional Data Settings. (January 2014) - Archer, Kellie J.; Hou, Jiayi; Zhou, Qing; Ferber, Kyle; Layne, John G.; Gentry, Amanda E.
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.