GaussProcess

GaussProcess

GaussProcess(
  x,
  y,
  isotropic = TRUE,
  nugget = TRUE,
  method = "bayes",
  ngibbs = 5000,
  burnin = 1000,
  thin = 1,
  jump.ic = c(1.1, 0.2),
  prior = "IG",
  mix = "joint",
  psi = NULL,
  zeroMean = FALSE,
  exclude = NULL,
  ...
)

Arguments

x

set of independent variables

y

dependent variable

isotropic

Boolean indicating whether the GP is fit isotropically. If FALSE, distances are calculated deparately for each direction

nugget

allows additional error in Y rather than fix interpolation to go through points

method

method for calculating correlations

ngibbs

number of MCMC iterations (per chain) to run

burnin

Number of samples to discard as burnin (auto must be FALSE)

thin

thinning of the matrix to make things faster. Default is to thin to 1

jump.ic

initial condition for jump standard deviation.

prior

'unif', 'IG'

mix

joint=mix over psi simultanously, each=mix over psi individually

psi

spatial corr

zeroMean

True if mean is 0, else false

exclude

<- isn't used anywhere, should be dropped

...

Additional arguments

Author

Michael Dietze