plot_tau.CI

It returns a credible interval plot of factor loadings covariance, tau. The lines are 95

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

plot_tau.CI function

<dl><dt>Gibbs</dt>
<dd>Result of Gibbs sampler from DIFM function.</dd>
<dt>burnin</dt>
<dd>Number of burn-in. If not specified, it uses the first tenths as burn-in period.</dd>
<dt>true.val</dt>
<dd>True values of tau. If not available, NA.</dd>
<dt>main.bool</dt>
<dd>Add title of the plots.</dd></dl>

Arguments

Credible interval plot of factor loadings variance — plot_tau.CI

<dl>

<dt>Gibbs</dt>
<dd>Result of Gibbs sampler from DIFM function.</dd>


<dt>burnin</dt>
<dd>Number of burn-in. If not specified, it uses the first tenths as burn-in period.</dd>


<dt>true.val</dt>
<dd>True values of tau. If not available, NA.</dd>


<dt>main.bool</dt>
<dd>Add title of the plots.</dd>

</dl>

plot_tau.CI: Credible interval plot of factor loadings variance

Description

Usage

Value

Arguments