This function computes the natural logarithm of profile likelihood of the PP GaSP model after plugging in the maximum likelihood estimator of the mean (trend) and variance parameters.
log_profile_lik_ppgasp(param, nugget, nugget_est, R0, X, zero_mean
,output,kernel_type, alpha)
The numerical value of natural logarithm of the profile likelihood.
a vector of natural logarithm of inverse-range parameters and natural logarithm of the nugget-variance ratio parameter.
the nugget-variance ratio parameter if this parameter is fixed.
Boolean value of whether the nugget is estimated or fixed.
a List of matrix where the j-th matrix is an absolute difference matrix of the j-th input vector.
the mean basis function i.e. the trend function.
the mean basis function is zero or not.
a matrix where each row is one runs of the computer model output.
A vector of integer specifying the type of kernels of each coordinate of the input.
In each coordinate of the vector, 1 means the pow_exp kernel with roughness parameter specified in alpha; 2 means matern_3_2 kernel; 3 means matern_5_2 kernel; 5 means periodic_gauss kernel; 5 means periodic_exp kernel.
roughness parameters in the kernel functions.
tools:::Rd_package_author("RobustGaSP")
Maintainer: tools:::Rd_package_maintainer("RobustGaSP")
M. Gu. and J.O. Berger (2016). Parallel partial Gaussian process emulation for computer models with massive output. Annals of Applied Statistics, 10(3), 1317-1347.
M. Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.