GPM
PackageThe CorrMat_Sym()
function builds the auto-correlation matrix corresponding to dataset X
while the CorrMat_Vec()
function builds the correlation matrix between datasets X1
and X2
.
CorrMat_Sym(X, CorrType, Omega)
CorrMat_Vec(X1, X2, CorrType, Omega)
Matrices containing the numeric data points. The rows and columns of both X1
and X2
denote individual observation settings and dimension, respectively.
The correlation function of the GP model. Choices include 'G'
(default), 'PE'
, 'LBG'
, and 'LB'
. See the references
for the details.
The vector storing all the scale (aka roughness) parameters of the correlation function. The length of Omega
depends on the CorrType
. See reference 1
.
R The Correlation matrix with size nrow(X1)
-by-nrow(X2)
. See here.
Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.
Plumlee, M. & Apley, D. W. (2017) Lifted Brownian kriging models. Technometrics, 59, 165-177.
Fit
to see how a GP model can be fitted to a training dataset.
Predict
to use the fitted GP model for prediction.
Draw
to plot the response via the fitted model.
# NOT RUN {
# see the examples in \code{\link[GPM]{Fit}}
# }
Run the code above in your browser using DataCamp Workspace