rainNLD

A numeric matrix containing gridded April total precipitation (in mm) over the Netherlands,
together with projected coordinates (UTM, km) and a distance-to-coast covariate (km).
The precipitation field is derived from the WorldClim v2.1 monthly climatology for April
(i.e., long-term monthly averages rather than a specific year).

datasets

Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et. al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples and tutorials.

Moreno Bevilacqua

GeoModels

Procedures for Gaussian and Non Gaussian Geostatistical (Large)
Data Analysis

Víctor Morales-Oñate

Francisco Cuevas-Pacheco

Christian Caamaño-Carrillo

rainNLD function

A numeric matrix with 71,401 rows and 4 columns:<dl>
 <dt>Column 1</dt>
<dd>Easting coordinate (UTM zone 31N), in kilometers.</dd> <dt>Column 2</dt>
<dd>Northing coordinate (UTM zone 31N), in kilometers.</dd> <dt>Column 3</dt>
<dd>Distance to the coastline, in kilometers (computed from Natural Earth coastline geometry, transformed to UTM).</dd> <dt>Column 4</dt>
<dd>April total precipitation, in millimeters.</dd>
</dl>

Format

April Precipitation over the Netherlands with Distance-to-Coast Covariate — rainNLD

A numeric matrix with 71,401 rows and 4 columns:<dl>
 <dt>Column 1</dt>
<dd>Easting coordinate (UTM zone 31N), in kilometers.</dd>

 <dt>Column 2</dt>
<dd>Northing coordinate (UTM zone 31N), in kilometers.</dd>

 <dt>Column 3</dt>
<dd>Distance to the coastline, in kilometers (computed from Natural Earth coastline geometry, transformed to UTM).</dd>

 <dt>Column 4</dt>
<dd>April total precipitation, in millimeters.</dd>


</dl>

rainNLD: April Precipitation over the Netherlands with Distance-to-Coast Covariate

Description

Usage

Arguments

Format

Details