GPM
PackagePredicts the reponse(s), associated prediction uncertainties, and gradient(s) of the GP model fitted by Fit
.
Predict(XF, Model, MSE_on = 0, YgF_on = 0, grad_dim = rep(1, ncol(XF)))
Matrix containing the locations (settings) where the predictions are desired. The rows and columns of XF
denote individual observation settings and input dimension, respectively.
The GP model fitted by Fit
.
Flag (a scalar) indicating whether the uncertainty (i.e., mean squared error MSE
) associated with prediction of the response(s) should be calculated. Set to a non-zero value to calculate MSE
.
Flag (a scalar) indicating whether the gradient(s) of the response(s) are desired. Set to a non-zero value to calculate the gradient(s). See note
below.
A binary vector of length ncol(XF)
. The gradient of the response(s) will be calculated along the dimensions where the corresponding element of grad_dim
is 1
. grad_dim
is ignored if YgF_on == 0
.
Output A list containing the following components:
YF
A matrix with n
rows (the number of prediction points) and dy
columns (the number of responses).
MSE
A matrix with n
rows and dy
columns where each element represents the prediction uncertainty (i.e., the expected value of the squared difference between the prediction and the true response) associated with the corresponding element in YF
.
YgF
An array of size n
by sum{grad_dim}
by dx
.
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.
Draw
to plot the response via the fitted model.
# NOT RUN {
# See the examples in the fitting function.
# }
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