neg_log_l: The Negative Log-Likelehood Function in 'LVGP' Package

In LVGP: Latent Variable Gaussian Process Modeling with Qualitative and Quantitative Input Variables

Description

Calculates the negative log-likelihood (excluding all the constant terms) as described in reference 1.

Usage

neg_log_l(hyperparam, p_quant, p_qual, lvs_qual, n_lvs_qual, dim_z,
  X_quant, X_qual, Y, min_eig, k, M)

Arguments

hyperparam	Hyperparameters of the LVGP model
p_quant	Number of quantative variables
p_qual	Number of qualitative variables
lvs_qual	Levels of each qualitative variable
n_lvs_qual	Number of levels of each qualitative variable
dim_z	Dimensionality of latent variables, usually 1 or 2
X_quant	Input data of quantative variables
X_qual	Input data of qualitative variables
Y	Vector containing the outputs of data points
min_eig	The smallest eigen value that the correlation matrix is allowed to have, which determines the nugget added to the correlation matrix.
k	Number of data points, nrow(X_quant) or nrow(X_qual)
M	Vector of ones with length k

Details

LVGP_fit calls this function as its optimization objective function.

Value

The negative log-likelihood (excluding all the constant terms) value.

Note

This function is NOT exported once the package is loaded.

References

1. "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors", Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (arXiv)

See Also

LVGP_fit to see how a GP model can be fitted to a training dataset.
LVGP_predict to use the fitted LVGP model for prediction.
LVGP_plot to plot the features of the fitted model.

Examples

# see the examples in the documentation of the function LVGP_fit.

LVGP documentation built on May 1, 2019, 7:05 p.m.