GridGP (Grid-based Gaussian Process Model)

This software is designed for a Gaussian process (GP) based nonparametric Bayesian variable selection method for longitudinal data. It maps multiple quantitative trait loci (QTL) without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the GP model measures the importance of each QTL, regardless of whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. To improve the flexibility of the GP model, we propose a novel grid-based method for the within-subject dependence structure. The proposed method can accurately approximate complex covariance structures. The dimension of the covariance matrix depends only on the number of fixed grid points although each subject may have different numbers of measurements at different time points. The deviance information criterion (DIC) and the Bayesian predictive information criterion (BPIC) are proposed for selecting an optimal number of grid points. To efficiently draw posterior samples, we combine a hybrid Monte Carlo method with a partially collapsed Gibbs (PCG) sampler.