Dynamic ICAR Spatiotemporal Factor Models
Spatial dependence matrix of the factor loadings
tools:::Rd_package_title("DIFM")
Hyperparameters for DIFM
Initialize model attributes for DIFM
Run Dynamic ICAR Factors Model (DIFM), with C++ codes
Run Dynamic ICAR Factors Model (DIFM)
Marginal predictive density
Marginal predictive density
Order of permutation by the largest absolute value in each eigenvector
Permute the dataset by the largest absolute value in each eigenvector,...
Credible interval plot of factor loadings
Spatial plots of factor loadings
A credible interval plot of posterior of sigma squared
Credible interval plot of factor loadings variance
Credible interval plot of common factors
Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.