spectralGP: spectralGP - tools for specifying Gaussian processes using...

In spectralGP: Approximate Gaussian Processes Using the Fourier Basis

Description

SpectralGP is a collection of functions for creating Gaussian processes in one and two dimensions using the Fourier basis approximation. It provides fast simulation and plotting of process realizations by use of the FFT, allowing simulation and plotting on very dense grids. For inference, it provides tools for use in setting up an MCMC: calculation of coefficient variances, calculation of process density, and coefficient proposals. It uses R environments to store GP objects as references/pointers.

Some major methods include:

• gp Create a Gaussian process object

• simulate.gp Simulate a Gaussian process realization

• plot.gp Plot a Gaussian process

• predict.gp Extract process values at specified domain points

DISCLAIMER:

This is software for statistical research and not for commercial uses. The author does not guarantee the correctness of any function or program in this package. Any changes to the software should not be made without the author's permission.

ACKNOWLEDGEMENT:

Many thanks to Chris Wikle who first suggested I use the Fourier basis approximation for Gaussian processes.

REFERENCES:

For more details, type 'citation("spectralGP")' for references.

See also:

Royle, J.A., and C.K. Wikle, (2005). Efficient Statistical Mapping of Avian Count Data. Ecological and Environmental Statistics 12:225-243. http://www.stat.missouri.edu/~wikle/pub_new.html

Wikle, C.K., (2002). Spatial modeling of count data: A case study in modelling breeding bird survey data on large spatial domains. In Spatial Cluster Modelling, A. Lawson and D. Denison, eds. Chapman and Hall, 199-209. http://www.stat.missouri.edu/~wikle/pub_new.html

Examples

gp1=gp(128,matern.specdens,c(1,4))
gp2=gp(c(64,64),matern.specdens,c(1,4))
simulate(gp1)
simulate(gp2)
plot(gp1)
plot(gp2)
gridvals=predict(gp1)
newlocs=runif(100)
offgridvals=predict(gp1,newlocs)

spectralGP documentation built on May 2, 2019, 2:40 a.m.