A0770
Title: jmcm: An R package for joint mean-covariance modelling of longitudinal data
Authors: Yi Pan - The University of Manchester (United Kingdom) [presenting]
Jianxin Pan - The University of Manchester (United Kingdom)
Abstract: Longitudinal studies commonly arise in various fields such as psychology, social science, economics and medical research, etc. It is of great importance to understand the dynamics in the mean function, covariance and/or correlation matrices of repeated measurements. However, high-dimensionality (HD) and positive-definiteness (PD) constraints are two major stumbling blocks in modelling of covariance and correlation matrices. It is evident that Cholesky-type decomposition based methods are effective in dealing with HD and PD problems, but those methods were not implemented in statistical software yet, causing a difficulty for practitioners to use. We first introduce recently developed Cholesky decomposition based methods for joint modelling of mean and covariance structures, namely modified Cholesky decomposition (MCD), alternative Cholesky decomposition (ACD) and hyperspherical parameterization of Cholesky factor (HPC). We then introduce our newly developed R package jmcm which is currently able to handle longitudinal data that follows a Gaussian distribution using the MCD, ACD and HPC methods. Demonstration is provided by running the package jmcm and comparison of those methods is made through analysing two real data sets.
