ide_spatially_varying

<p>A matrix containing simulated data points on a 20 X 20 grid at 20 time points.
The rows correspond to the 400 spatial locations,
and the columns, to the 20 time points.</p>

datasets

In contrast to other methods of modeling spatio-temporal data,
dynamic spatio-temporal models (DSTMs) directly model the dynamic
data-generating process.
'ideq' supports two main classes of DSTMs:
(1) empirical orthogonal function (EOF) models and
(2) integrodifference equation (IDE) models.
EOF models do not directly use any spatial information;
instead, they make use of observed relationships in the data
(the principal components) to model the underlying process.
In contrast, IDE models are based on diffusion dynamics and the process
evolution is governed by a (typically Gaussian) redistribution kernel.
Both types have a variety of options for specifying the model components,
including the process matrix, process error, and observation error.
The classic reference for DSTMs is
Noel Cressie and Christopher K. Wikle (2011, ISBN:978-0471692744).
For IDE models specifically, see
Christopher K. Wikle and Noel Cressie (1999, <https://www.jstor.org/stable/2673587>)
and
Christopher K. Wikle (2002, <doi:10.1191/1471082x02st036oa>).

ide_spatially_varying: Simulated data from a spatially varying IDE model

Description

Usage

Arguments

Format

See Also