GaussProcess
Usage
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