predict.spCP

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

Implements a spatially varying change point model with
unique intercepts, slopes, variance intercepts and slopes, and
change points at each location. Inference is within the
Bayesian setting using Markov chain Monte Carlo (MCMC). The
response variable can be modeled as Gaussian (no nugget),
probit or Tobit link and the five spatially varying parameter
are modeled jointly using a multivariate conditional
autoregressive (MCAR) prior. The MCAR is a unique process that
allows for a dissimilarity metric to dictate the local spatial
dependencies. Full details of the package can be found in the accompanying vignette.
Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics
by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma
progression using visual field data", <doi:10.1016/j.spasta.2019.02.001>.

Samuel I. Berchuck

spCP

Spatially Varying Change Points

predict.spCP function

<dl><dt>object</dt>
<dd>a <code>spCP</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.spCP — predict.spCP

<dl>

<dt>object</dt>
<dd>a <code>spCP</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.spCP: predict.spCP

Description

Usage

Value

Arguments

Author

Details