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Excursions: Excursion Sets and Contour Credibility Regions for Random Fields

Source: R/exursions-package.R
excursions-package.Rd

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.

See also

Useful links:

Author

Maintainer: David Bolin davidbolin@gmail.com (ORCID)

Authors:

Other contributors: