holes

The synthetic "holes" provides a set of training and test data.frame of a Gaussian process realization with a (inherently dense) nonstationary covariance function.
Four holes are present in the training dataset, and the task is to predict them.

datasets

Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions.
Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics.
An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.

Federico Blasi

cocons

Covariate-Based Covariance Functions for Nonstationary Spatial
Modeling

Reinhard Furrer

holes function

A list with training and test data.frame with rows and variables:<dl>
<dt>x</dt>
<dd>first spatial coordinate</dd><dt>y</dt>
<dd>second spatial coordinate</dd><dt>cox_x</dt>
<dd>first spatial characteristic</dd><dt>cov_y</dt>
<dd>second spatial characteristic</dd><dt>z</dt>
<dd>response variable</dd>
</dl>

Format

Holes Data Set — holes

A list with training and test data.frame with rows and variables:<dl>
<dt>x</dt>
<dd>first spatial coordinate</dd>

<dt>y</dt>
<dd>second spatial coordinate</dd>

<dt>cox_x</dt>
<dd>first spatial characteristic</dd>

<dt>cov_y</dt>
<dd>second spatial characteristic</dd>

<dt>z</dt>
<dd>response variable</dd>


</dl>

holes: Holes Data Set

Description

Usage

Arguments

Format

Examples