difm.hyp.parm

Sets the hyperparameters to generate Gibbs sampler of DIFM

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

difm.hyp.parm function

<dl><dt>model.attributes</dt>
<dd>Model attributes from <code>difm.model.attributes</code></dd>
<dt>n.tau</dt>
<dd>Shape parameter for tau</dd>
<dt>n.s2.tau</dt>
<dd>Rate parameter for tau</dd>
<dt>n.sigma</dt>
<dd>Shape parameter for sigma squared</dd>
<dt>n.s2.sigma</dt>
<dd>Rate parameter for sigma squared</dd>
<dt>Hlist</dt>
<dd>Neighborhood matrix</dd>
<dt>Psi.size</dt>
<dd>The magnitude of covariance for the evolution matrix</dd></dl>

Arguments

Hyperparameters for DIFM — difm.hyp.parm

<dl>

<dt>model.attributes</dt>
<dd>Model attributes from <code>difm.model.attributes</code></dd>


<dt>n.tau</dt>
<dd>Shape parameter for tau</dd>


<dt>n.s2.tau</dt>
<dd>Rate parameter for tau</dd>


<dt>n.sigma</dt>
<dd>Shape parameter for sigma squared</dd>


<dt>n.s2.sigma</dt>
<dd>Rate parameter for sigma squared</dd>


<dt>Hlist</dt>
<dd>Neighborhood matrix</dd>


<dt>Psi.size</dt>
<dd>The magnitude of covariance for the evolution matrix</dd>

</dl>

difm.hyp.parm: Hyperparameters for DIFM

Description

Usage

Value

Arguments