L2R2: Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies
L2R2 is a function for estimating L2R2 model with MCMC algorithm. This L2R2 package is developed by Zhao-Hua Lu, Zakaria Khondker, and Hongtu Zhu from the BIG-S2 lab. To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm.
Citation: Hongtu Zhu, Zakaria Khondker, Zhaohua Lu and Joseph G. Ibrahim. Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers. Journal of the American Statistical Association. 2014; 109 (507) 977-990.