Description Usage Arguments Details Value References See Also Examples
Calculates some auxiliary paramters to obtain the negative log-likelehood and its gradient.
1 | Auxil(Omega, X, Y, CorrType, MinEig, Fn, n, dy)
|
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, |
Since Auxil
is shared between NLogL
and NLogL_G
during optimization, ideally it should be run only once (e.g., via memoisation). Such an implementation is left for future editions.
ALL A list containing the following components (based on CorrType
, some other parameters are also stored in ALL
):
R
The correlation matrix whose smallest eigen value is >= MinEig
.
L
Cholesky decomposition of R
.
Raw_MinEig
The smallest eigen value of R
before adding Nug_opt
.
Nug_opt
The added nugger to R
.
B
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.
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.
1 | # see the examples in the fitting function.
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.