creates a Gaussian process gp
object
createGP(X, Z, beta, a, meanReg, sig2, nugget,
param.names = 1:dim(X)[2], constantMean = 1)
the design matrix
output obtained from the design matrix X
, as a vector or a 1-column matrix
vector of correlation coefficients
vector of smoothness parameters in the correlation function (if mlegp
is used, these will be 2)
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
the unconditional variance of the Gaussian process
the constant nugget or a vector of length nrow(X)
corresponding to the diagonal nugget matrix
optional vector of parameter names (with length equal to ncol(X)
1 if the Gaussian process has a constant mean; 0 otherwise
an object of class gp
that contains the following components:
matrix of observations
number of observations
the design matrix
number of dimensions of X
1 if GP has a constant mean; 0 otherwise
the mean matrix
mean function regression coefficients
correlation parameters
smoothness parameters in correlation function
unconditional variance of predicted expected output
vector of parameter names, corresponding to columns of X
inverse var-cov matrix
constant nugget or vector corresponding to the diagonal nugget matrix for a single observation generated from each element in X
the log likelihood of the observations
results from cross-validation, where
cv[,1]
are the cross-validated predictions
cv[,2]
are the variances of the cross-validated predictions