Dependent Gaussian Processes for Longitudinal Correlated Factors
DGP4LCF: Dependent Gaussian Processes for Longitudinal Correlated Fact...
Displaying significant factor loadings in the heatmap.
Plotting figures for factor score trajectory.
Generating posterior samples for parameters (other than DGP parameters...
Combining from all chains the posterior samples for parameters in the ...
Generating different initials for multiple chains.
Loading the saved posterior samples for parameters in the model and pr...
Monte Carlo Expectation Maximization (MCEM) algorithm to return the Ma...
Visualizing cross-correlations among factors.
Parameters' setup and initial value assignment for the Monte Carlo Exp...
Numerical summary for important continuous variables that do not need ...
Numerical summary for factor loadings and factor scores, which need al...
Constructing subject-specific objects required for Gibbs sampler (for ...
Generating a table listing all possible combinations of the binary var...
Functionalities for analyzing high-dimensional and longitudinal biomarker data to facilitate precision medicine, using a joint model of Bayesian sparse factor analysis and dependent Gaussian processes. This paper illustrates the method in detail: J Cai, RJB Goudie, C Starr, BDM Tom (2023) <doi:10.48550/arXiv.2307.02781>.