predict.STBDwDM

Predicts future observations from the <code>STBDwDM</code> model.

Implements a spatiotemporal boundary detection model with a dissimilarity
metric for areal data with inference in a Bayesian setting using Markov chain
Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget),
probit or Tobit link and spatial correlation is introduced at each time point
through a conditional autoregressive (CAR) prior. Temporal correlation is introduced
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 "Diagnosing Glaucoma
Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method",
by Berchuck et al (2019) <doi:10.1080/01621459.2018.1537911>.

Samuel I. Berchuck

womblR

Spatiotemporal Boundary Detection Model for Areal Unit Data

predict.STBDwDM function

<dl><dt>object</dt>
<dd>a <code>STBDwDM</code> model object for which predictions
are desired from.</dd>
<dt>NewTimes</dt>
<dd>a numeric vector including desired time(s) points for prediction.</dd>
<dt>...</dt>
<dd>other arguments.</dd></dl>

Arguments

Author

predict.STBDwDM — predict.STBDwDM

<dl>

<dt>object</dt>
<dd>a <code>STBDwDM</code> model object for which predictions
are desired from.</dd>


<dt>NewTimes</dt>
<dd>a numeric vector including desired time(s) points for prediction.</dd>


<dt>...</dt>
<dd>other arguments.</dd>

</dl>

predict.STBDwDM: predict.STBDwDM

Description

Usage

Value

Arguments

Author

Details