points.gp

models

smooth

spatial

Routines for creating, manipulating, and performing
Bayesian inference about Gaussian processes in
one and two dimensions using the Fourier basis approximation:
simulation and plotting of processes, calculation of
coefficient variances, calculation of process density,
coefficient proposals (for use in MCMC).  It uses R environments to
store GP objects as references/pointers.

Christopher Paciorek

spectralGP

Approximate Gaussian Processes Using the Fourier Basis

points.gp function

<dl> <dt>x</dt>
<dd>A GP object, created by <code>gp</code>.</dd> <dt>...</dt>
<dd>Extra arguments to plotting functions.</dd></dl>

Arguments

Christopher Paciorek <a href="/link/paciorek%40alumni.cmu.edu?package=spectralGP&version=1.3.4" data-mini-rdoc="spectralGP::paciorek@alumni.cmu.edu">paciorek@alumni.cmu.edu</a>

Author

Add points for a one-dimensional process based on a spectral GP object — points.gp

<dl>

 <dt>x</dt>
<dd>A GP object, created by <code>gp</code>.</dd>

 <dt>...</dt>
<dd>Extra arguments to plotting functions.</dd>

</dl>

Christopher Paciorek <a href='mailto:paciorek@alumni.cmu.edu'>paciorek@alumni.cmu.edu</a>

points.gp: Add points for a one-dimensional process based on a spectral GP object

Description

Usage

Value

Arguments

Author

References

See Also

Examples