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In excursions: Excursion Sets and Contour Credibility Regions for Random Fields
excursions-package R Documentation
Excursions: Excursion Sets and Contour Credibility Regions for Random Fields
Description
excursions contains functions that compute probabilistic excursion sets,
contour credibility regions, contour avoiding regions, contour map quality measures,
and simultaneous confidence bands for latent Gaussian
random processes and fields. A detailed manual can be found in the paper
Bolin, D and Lindgren, F (2018)
Calculating Probabilistic Excursion Sets and Related Quantities Using excursions,
Journal of Statistical Software, 86(5), 1–20.
Details
The main functions in the package fall into three different categories described below.
Excursion sets, contour credibility regions, and contour avoiding regions
The main functions for computing excursion sets, contour credibility regions, and
contour avoiding regions are
excursions The main function for Gaussian models.
excursions.inla Interface for latent Gaussian models estimated using INLA.
excursions.mc Function for analyzing models that have been
estimated using Monte Carlo methods.
The output from the functions above provides a discrete domain estimate of the regions.
Based on this estimate, the function continuous computes a continuous
domain estimate.
The main reference for these functions is Bolin, D. and Lindgren, F. (2015)
Excursion and contour uncertainty regions for latent Gaussian models,
JRSS-series B, vol 77, no 1, pp 85-106.
Contour map quality measures
The package provides several functions for computing contour maps and their quality
measures. These quality measures can be used to decide on an appropriate number of
contours to use for the contour map.
The main functions for computing contour maps and the corresponding quality measures
are
contourmap The main function for Gaussian models.
contourmap.inla Interface for latent Gaussian models estimated
using INLA.
contourmap.mc Function for analyzing models that have been
estimated using Monte Carlo methods.
Other noteworthy functions relating to contourmaps are tricontour and
tricontourmap, which compute contour curves for functinos defined on
triangulations, as well as contourmap.colors which can be used to
compute appropriate colors for displaying contour maps.
The main reference for these functions is Bolin, D. and Lindgren, F. (2017)
Quantifying the uncertainty of contour maps, Journal of Computational and
Graphical Statistics, 26:3, 513-524.
Simultaneous confidence bands
The main functions for computing simultaneous confidence bands are
simconf Function for analyzing Gaussian models.
simconf.inla Function for analyzing latent Gaussian models
estimated using INLA.
simconf.mc Function for analyzing models estimated using Monte
Carlo methods.
simconf.mixture Function for analyzing Gaussian mixture models.
The main reference for these functions is Bolin et al. (2015)
Statistical prediction of global sea level
from global temperature, Statistica Sinica, Vol 25, pp 351-367.
Author(s)
Maintainer: David Bolin davidbolin@gmail.com (ORCID)
Authors:
Finn Lindgren finn.lindgren@gmail.com (ORCID)
Other contributors:
Suen Man Ho M.H.Suen@sms.ed.ac.uk [contributor]
See Also
Useful links:
excursions documentation built on Oct. 23, 2023, 5:07 p.m.
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| excursions-package | R Documentation |
Excursions: Excursion Sets and Contour Credibility Regions for Random Fields
Description
excursions contains functions that compute probabilistic excursion sets,
contour credibility regions, contour avoiding regions, contour map quality measures,
and simultaneous confidence bands for latent Gaussian
random processes and fields. A detailed manual can be found in the paper
Bolin, D and Lindgren, F (2018)
Calculating Probabilistic Excursion Sets and Related Quantities Using excursions,
Journal of Statistical Software, 86(5), 1–20.
Details
The main functions in the package fall into three different categories described below.
Excursion sets, contour credibility regions, and contour avoiding regions
The main functions for computing excursion sets, contour credibility regions, and contour avoiding regions are
excursionsThe main function for Gaussian models.
excursions.inlaInterface for latent Gaussian models estimated using INLA.
excursions.mcFunction for analyzing models that have been estimated using Monte Carlo methods.
The output from the functions above provides a discrete domain estimate of the regions.
Based on this estimate, the function continuous computes a continuous
domain estimate.
The main reference for these functions is Bolin, D. and Lindgren, F. (2015) Excursion and contour uncertainty regions for latent Gaussian models, JRSS-series B, vol 77, no 1, pp 85-106.
Contour map quality measures
The package provides several functions for computing contour maps and their quality measures. These quality measures can be used to decide on an appropriate number of contours to use for the contour map.
The main functions for computing contour maps and the corresponding quality measures are
contourmapThe main function for Gaussian models.
contourmap.inlaInterface for latent Gaussian models estimated using INLA.
contourmap.mcFunction for analyzing models that have been estimated using Monte Carlo methods.
Other noteworthy functions relating to contourmaps are tricontour and
tricontourmap, which compute contour curves for functinos defined on
triangulations, as well as contourmap.colors which can be used to
compute appropriate colors for displaying contour maps.
The main reference for these functions is Bolin, D. and Lindgren, F. (2017) Quantifying the uncertainty of contour maps, Journal of Computational and Graphical Statistics, 26:3, 513-524.
Simultaneous confidence bands
The main functions for computing simultaneous confidence bands are
simconfFunction for analyzing Gaussian models.
simconf.inlaFunction for analyzing latent Gaussian models estimated using INLA.
simconf.mcFunction for analyzing models estimated using Monte Carlo methods.
simconf.mixtureFunction for analyzing Gaussian mixture models.
The main reference for these functions is Bolin et al. (2015) Statistical prediction of global sea level from global temperature, Statistica Sinica, Vol 25, pp 351-367.
Author(s)
Maintainer: David Bolin davidbolin@gmail.com (ORCID)
Authors:
Finn Lindgren finn.lindgren@gmail.com (ORCID)
Other contributors:
Suen Man Ho M.H.Suen@sms.ed.ac.uk [contributor]
See Also
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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