cov_m {EzGP}

The Function for Constructing the Covariance Matrix in EzGP Package

Description

Builds the covariance matrix for the given dataset according to different models.

Usage

cov_m(X, p, q, m, n, 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.

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.

n

Number of training data points

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 covariance matrix is built for. 0 for EzGP model, 1 for EEzGP model. The default setting is 0.

Details

EzGP_fit, EzGP_predict, EEzGP_fit, EEzGP_predict, LEzGP_fit, and LLF_gradients will call this function.

Value

The covariance matrix for the given dataset.

Note

This function is used inside other functions in this package and is NOT exported once the EzGP package is loaded.

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 (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.