DIFMcpp

This function runs Dynamic ICAR factors Model (DIFM), simulated from C++ codes

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

DIFMcpp function

<dl><dt>model.attributes</dt>
<dd>Model attributes from <code>difm.model.attributes</code></dd>
<dt>hyp.parm</dt>
<dd>Hyperparameters from <code>difm.hyp.parm</code></dd>
<dt>data</dt>
<dd>The dataset</dd>
<dt>every</dt>
<dd>Save <code>every</code> iterations to final result</dd>
<dt>verbose</dt>
<dd>Print out the iteration process</dd></dl>

Arguments

Run Dynamic ICAR Factors Model (DIFM), with C++ codes — DIFMcpp

<dl>

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


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


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


<dt>every</dt>
<dd>Save <code>every</code> iterations to final result</dd>


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

</dl>

DIFMcpp: Run Dynamic ICAR Factors Model (DIFM), with C++ codes

Description

Usage

Value

Arguments