buildH

It computes the spatial covariance and precision matrix of the neighboring subregions using Intrinsice Autoregressive Conditional (ICAR) process.

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.

Hwasoo Shin

DIFM

Dynamic ICAR Spatiotemporal Factor Models

Marco Ferreira

buildH function

<dl><dt>areapoly</dt>
<dd>The polygon of the areas. We can obtain this through <code>readOGR</code> function from <code>sp</code> matrix.</dd>
<dt>permutation</dt>
<dd>Permutation order of the subregions</dd></dl>

Arguments

Spatial dependence matrix of the factor loadings — buildH

<dl>

<dt>areapoly</dt>
<dd>The polygon of the areas. We can obtain this through <code>readOGR</code> function from <code>sp</code> matrix.</dd>


<dt>permutation</dt>
<dd>Permutation order of the subregions</dd>

</dl>

buildH: Spatial dependence matrix of the factor loadings

Description

Usage

Value

Arguments

Details