createGP: creates a Gaussian process object

In mlegp: Maximum Likelihood Estimates of Gaussian Processes

creates a Gaussian process object

Description

creates a Gaussian process gp object

Usage

createGP(X, Z, beta, a, meanReg, sig2, nugget, 
         param.names = 1:dim(X)[2], constantMean = 1)

Arguments

X	the design matrix
Z	output obtained from the design matrix X, as a vector or a 1-column matrix
beta	vector of correlation coefficients
a	vector of smoothness parameters in the correlation function (if mlegp is used, these will be 2)
meanReg	the constant mean if constantMean = 1, otherwise the regression coefficients of the mean function such that meanReg pre-multiplied by (1 X) will produce the mean matrix
sig2	the unconditional variance of the Gaussian process
nugget	the constant nugget or a vector of length nrow(X) corresponding to the diagonal nugget matrix
param.names	optional vector of parameter names (with length equal to ncol(X)
constantMean	1 if the Gaussian process has a constant mean; 0 otherwise

Value

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 X
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 X
loglike	the log likelihood of the observations
cv	results from cross-validation, where cv[,1] are the cross-validated predictions cv[,2] are the variances of the cross-validated predictions

Note

this function is called by mlegp and should not be called by the user

Author(s)

Garrett M. Dancik [e-mail]

References

https://github.com/gdancik/mlegp/

See Also

mlegp

mlegp documentation built on March 18, 2022, 5:29 p.m.