plot_X.CI

The functions builds the plot of 95% confidence intervals of the common realizations, X. The black solid lines are the posterior mean and the dased lines are the 95% confidence intervals.

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_X.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>main.bool</dt>
<dd>Add title of the plots.</dd>
<dt>layout.dim</dt>
<dd>Dimension of panel layout for multiple common factors. If not specificed, common factor plots are layout in one column.</dd></dl>

Arguments

Credible interval plot of common factors — plot_X.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>main.bool</dt>
<dd>Add title of the plots.</dd>


<dt>layout.dim</dt>
<dd>Dimension of panel layout for multiple common factors. If not specificed, common factor plots are layout in one column.</dd>

</dl>

plot_X.CI: Credible interval plot of common factors

Description

Usage

Value

Arguments