EzGP (version 0.1.0)

LLF_gradients: 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)

Value

A list of the following items:

• objective The log-likelihood function value.

• gradient The analytical gradients.

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 X.

q

Number of qualitative factors in the given dataset X.

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.

References

1. "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 (tools:::Rd_expr_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.