reg.bfa_sp

<p>The data object <code>reg.bfa_sp</code> is a pre-computed <code>spBFA</code> data object for use in the package vignette and function examples.</p>

datasets

Implements a spatial Bayesian non-parametric factor analysis model
with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC).
Spatial correlation is introduced in the columns of the factor loadings
matrix using a Bayesian non-parametric prior, the probit stick-breaking
process. Areal spatial data is modeled using a conditional autoregressive
(CAR) prior and point-referenced spatial data is treated using a Gaussian
process. The response variable can be modeled as Gaussian, probit, Tobit, or
Binomial (using Polya-Gamma augmentation). Temporal correlation is
introduced for the latent factors through a hierarchical structure and can
be specified as exponential or first-order autoregressive. Full details of
the package can be found in the accompanying vignette. Furthermore, the
details of the package can be found in "Bayesian Non-Parametric Factor
Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019),
<doi:10.1214/20-BA1253> in Bayesian Analysis.

Samuel I. Berchuck

spBFA

Spatial Bayesian Factor Analysis

reg.bfa_sp function

<p>The data object <code>reg.bfa_sp</code> is a <code>spBFA</code> data object that is the result of implementing the MCMC code in the vignette.
        It is presented here because the run-time would be unecessarily long when compiling the R package.</p>

reg.bfa_sp: Pre-computed regression results from `bfa_sp`

Description

Usage

Arguments

Format