plot.GeoVariogramDir

Plots empirical directional semi-variograms for objects of class <code>"GeoVariogramDir"</code> as produced by <code>GeoVariogramDir</code>.
 All directions are displayed in a single plot, each with a different color and a legend indicating the direction (e.g., "0°", "45°", etc.).

variogram

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

plot.GeoVariogramDir function

<dl> <dt>x</dt>
<dd>An object of class <code>"GeoVariogramDir"</code> as produced by <code>GeoVariogramDir</code>.</dd> <dt>main</dt>
<dd>A main title for the plot.</dd> <dt>pch</dt>
<dd>Plotting character (point type) for the points (default: 20).</dd> <dt>lwd</dt>
<dd>Line width for the lines connecting points (default: 1).</dd> <dt>col</dt>
<dd>A vector of colors, one for each direction (default: 1:8).</dd> <dt>ylab</dt>
<dd>Label for the y-axis (default: "Semivariogram").</dd> <dt>xlab</dt>
<dd>Label for the x-axis (default: "Lag").</dd> <dt>...</dt>
<dd>Additional graphical parameters passed to <code><a href="/link/plot?package=GeoModels&version=2.2.1" data-mini-rdoc="GeoModels::plot">plot</a></code>.</dd></dl>

Arguments

Plot empirical directional semi-variogram — plot.GeoVariogramDir

<dl>

 <dt>x</dt>
<dd>An object of class <code>"GeoVariogramDir"</code> as produced by <code>GeoVariogramDir</code>.</dd>

 <dt>main</dt>
<dd>A main title for the plot.</dd>

 <dt>pch</dt>
<dd>Plotting character (point type) for the points (default: 20).</dd>

 <dt>lwd</dt>
<dd>Line width for the lines connecting points (default: 1).</dd>

 <dt>col</dt>
<dd>A vector of colors, one for each direction (default: 1:8).</dd>

 <dt>ylab</dt>
<dd>Label for the y-axis (default: "Semivariogram").</dd>

 <dt>xlab</dt>
<dd>Label for the x-axis (default: "Lag").</dd>

 <dt>...</dt>
<dd>Additional graphical parameters passed to <code><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></code>.</dd>

</dl>

plot.GeoVariogramDir: Plot empirical directional semi-variogram

Description

Usage

Value

Arguments

Details

See Also