corr_mat

Matrices containing the data points. The rows and columns of both <code>X1</code> and <code>X2</code> denote individual observation settings and dimension, respectively.

X1, X2

The vector storing all the scale (aka roughness) parameters of the correlation function. See <code>reference 1</code>.

phi_full

Builds the correlation matrix given two datasets, and the type and parameters of the correlation function.

Fit response surfaces for datasets with latent-variable Gaussian process modeling, predict responses for new inputs, and plot latent variables locations in the latent space (only 1D or 2D).
The input variables of the datasets can be quantitative, qualitative/categorical or mixed.
The output variable of the datasets is a scalar (quantitative).
The optimization of the likelihood function is done using a successive approximation/relaxation algorithm similar to another GP modeling package "GPM".
The modeling method is published in "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors"
by Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018) <arXiv:1806.07504>. The package is developed in IDEAL of Northwestern University.

corr_mat: The Function for Constructing the Correlation Matrix in `LVGP` Package

Description

Usage

Arguments

Value

References

See Also

Examples