marginal_d_cpp

It calculates the marginal density (Lewis and Raftery, 1997) from the DIFM sample using C++.

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

marginal_d_cpp function

<dl><dt>data</dt>
<dd>The dataset</dd>
<dt>attributes</dt>
<dd>Model attributes generated from <code>difm.model.attributes</code>.</dd>
<dt>hyp_parm</dt>
<dd>Hyperparameters generated from <code>difm.hyp.parm</code>.</dd>
<dt>Gibbs</dt>
<dd>Result of Gibbs sampler from DIFM function.</dd>
<dt>burnin</dt>
<dd>Burn-in period. If not specified, one tenths of the iterations will be the burn-in period.</dd>
<dt>verbose</dt>
<dd>Print out the process.</dd></dl>

Arguments

Marginal predictive density — marginal_d_cpp

<dl>

<dt>data</dt>
<dd>The dataset</dd>


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


<dt>hyp_parm</dt>
<dd>Hyperparameters generated from <code>difm.hyp.parm</code>.</dd>


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


<dt>burnin</dt>
<dd>Burn-in period. If not specified, one tenths of the iterations will be the burn-in period.</dd>


<dt>verbose</dt>
<dd>Print out the process.</dd>

</dl>

marginal_d_cpp: Marginal predictive density

Description

Usage

Value

Arguments