sim_data

Generates simulated data for use in experiments

Deep compositional spatial models are standard spatial covariance
models coupled with an injective warping function of the spatial
domain. The warping function is constructed through a composition
of multiple elemental injective functions in a deep-learning
framework. The package implements two cases for the univariate setting; first,
when these warping functions are known up to some weights that
need to be estimated, and, second, when the weights in each layer are random.
In the multivariate setting only the former case is available.
Estimation and inference is done using `tensorflow`, which makes use of
graphics processing units.
For more details see Zammit-Mangion et al. (2022) <doi:10.1080/01621459.2021.1887741>,
Vu et al. (2022) <doi:10.5705/ss.202020.0156>,
Vu et al. (2023) <doi:10.1016/j.spasta.2023.100742>, and
Shao et al. (2025) <doi:10.48550/arXiv.2505.12548>.

Quan Vu

deepspat

Deep Compositional Spatial Models

Andrew Zammit-Mangion

Xuanjie Shao

sim_data function

<dl><dt>type</dt>
<dd>type of function. Can be 'step1D', 'Monterrubio1D', 'dampedwave1D', 'step2D', 'AWU_RBF_2D', or 'AWU_RBF_LFT_2D'</dd>
<dt>ds</dt>
<dd>spatial grid length</dd>
<dt>n</dt>
<dd>number of data points</dd>
<dt>sigma2y</dt>
<dd>measurement-error variance</dd></dl>

Arguments

Generate simulation data for testing — sim_data

<dl>

<dt>type</dt>
<dd>type of function. Can be 'step1D', 'Monterrubio1D', 'dampedwave1D', 'step2D', 'AWU_RBF_2D', or 'AWU_RBF_LFT_2D'</dd>


<dt>ds</dt>
<dd>spatial grid length</dd>


<dt>n</dt>
<dd>number of data points</dd>


<dt>sigma2y</dt>
<dd>measurement-error variance</dd>

</dl>

sim_data: Generate simulation data for testing

Description

Usage

Value

Arguments

Examples