LVGP_plot

The LVGP model fitted by <code><a rd-options="LVGP" href="/link/LVGP_fit?package=LVGP&version=2.1.5&to=LVGP" data-mini-rdoc="LVGP::LVGP_fit">LVGP_fit</a></code>

model

An array of index (indices) of the qualitative/categorical variable(s) to be plotted.
Default is NULL, in which case all the qualitative/categorical variables will be plotted.

ind_qual_plot

Plots the qualitative/categorical variable levels in the latent space (only for 1D or 2D cases).
 If the qualitative/categorical variables are not specified, all the qualified variables will be plotted.
 See <code>Arguments</code> for more details on the options.

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.

Siyu Tao

LVGP

Latent Variable Gaussian Process Modeling with Qualitative and
Quantitative Input Variables

LVGP_plot function

The LVGP model fitted by <code><a rd-options='LVGP' href='LVGP_fit'>LVGP_fit</a></code>

LVGP_plot: The Plotting Function of `LVGP` Package

Description

Usage

Arguments

References

See Also

Examples