createGP | R Documentation |
creates a Gaussian process gp
object
createGP(X, Z, beta, a, meanReg, sig2, nugget, param.names = 1:dim(X)[2], constantMean = 1)
X |
the design matrix |
Z |
output obtained from the design matrix |
beta |
vector of correlation coefficients |
a |
vector of smoothness parameters in the correlation function (if |
meanReg |
the constant mean if |
sig2 |
the unconditional variance of the Gaussian process |
nugget |
the constant nugget or a vector of length |
param.names |
optional vector of parameter names (with length equal to |
constantMean |
1 if the Gaussian process has a constant mean; 0 otherwise |
an object of class gp
that contains the following components:
Z |
matrix of observations |
numObs |
number of observations |
X |
the design matrix |
numDim |
number of dimensions of X |
constantMean |
1 if GP has a constant mean; 0 otherwise |
mu |
the mean matrix |
Bhat |
mean function regression coefficients |
beta |
correlation parameters |
a |
smoothness parameters in correlation function |
sig2 |
unconditional variance of predicted expected output |
params |
vector of parameter names, corresponding to columns of |
invVarMatrix |
inverse var-cov matrix |
nugget |
constant nugget or vector corresponding to the diagonal nugget matrix for a single observation generated from each element in |
loglike |
the log likelihood of the observations |
cv |
results from cross-validation, where
|
this function is called by mlegp
and should not be called by the user
Garrett M. Dancik dancikg@easternct.edu
https://github.com/gdancik/mlegp/
mlegp
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