LLF_gradients {EzGP} | R Documentation |
The Log-likelihood Function and The Analytical Gradients in EzGP
Package
Description
Calculates the log-likelihood function value and the analytical gradients as described in reference 1
.
Usage
LLF_gradients(X, Y, p, q, m, parv, tau = 0, models = 0)
Arguments
X |
Matrix or data frame containing the inputs of training data. Each row represents the input setting of a data point and the columns are values of quantitative variables and qualitative variables. |
Y |
Vector containing the outputs of training data points. |
p |
Number of quantitative factors in the given dataset |
q |
Number of qualitative factors in the given dataset |
m |
A vector containing numbers of levels in qualitative factors. |
parv |
Parameters in the EzGP/EEzGP model. |
tau |
Nugget if needed. The default nugget is 0, otherwise it has to be a non-negative real value. |
models |
Model indicator that indicates which model the likelihoods and analytical gradients are applied to. 0 for EzGP model, 1 for EEzGP model. |
Value
A list of the following items:
objective
The log-likelihood function value.gradient
The analytical gradients.
References
"EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors", Qian Xiao, Abhyuday Mandal, C. Devon Lin, and Xinwei Deng (doi:10.1137/19M1288462)
See Also
EzGP_fit
to see how an EzGP model can be fitted to a training dataset.
EzGP_predict
to use the fitted EzGP model for prediction.
EEzGP_fit
to see how an EEzGP model can be fitted to a training dataset.
EEzGP_predict
to use the fitted EEzGP model for prediction.
LEzGP_fit
to see how a LEzGP model can be fitted to a training dataset.
Examples
# see the examples in the documentation of the function EzGP_fit.