NLogL {GPM} | R Documentation |
The Function for calculating the Negative Log-Likelehood in GPM
Package
Description
Calculates the negative log-likelihood (excluding all the constant terms) as described in reference 1
.
Usage
NLogL(Omega, X, Y, CorrType, MinEig, Fn, n, dy)
Arguments
Omega |
The vector storing all the hyperparameters of the correlation function. The length of |
X |
Matrix containing the training (aka design or input) data points. The rows and columns of |
Y |
Matrix containing the output (aka response) data points. The rows and columns of |
CorrType |
The correlation function of the GP model. Choices include |
MinEig |
The smallest eigen value that the correlation matrix is allowed to have, which in return determines the appraopriate nugget that should be added to the correlation matrix. |
Fn |
A matrix of |
n |
Number of observations, |
dy |
Number of responses, |
Details
Fit
calls this function with scaled X
and Y
. That is, when the user fits a GP model by calling Fit(X, Y)
, X
and Y
are mapped to the [0, 1]
region and then passed to this function.
Value
nlogl The negative log-likelihood (excluding all the constant terms). See the references
.
References
Bostanabad, R., Kearney, T., Tao, S., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. Int J Numer Meth Eng, 114, 501-516.
Plumlee, M. & Apley, D. W. (2017) Lifted Brownian kriging models. Technometrics, 59, 165-177.
See Also
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.
Examples
# see the examples in the fitting function.