pred_rgasp | R Documentation |
A function to make prediction on robust GaSP models after the robust GaSP model has been constructed.
pred_rgasp(beta, nu, input, X, zero_mean,output, testing_input,
X_testing, L, LX, theta_hat, sigma2_hat,
q_025, q_975, r0, kernel_type, alpha,method,interval_data)
beta |
inverse-range parameters. |
nu |
noise-variance ratio parameter. |
input |
input matrix. |
X |
the mean basis function i.e. the trend function. |
zero_mean |
The mean basis function is zero or not. |
output |
output matrix. |
testing_input |
testing input matrix. |
X_testing |
mean/trend matrix of testing inputs. |
L |
a lower triangular matrix for the cholesky decomposition of |
LX |
a lower triangular matrix for the cholesky decomposition of X^tR^{-1}X. |
theta_hat |
estimated mean/trend parameters. |
sigma2_hat |
estimated variance parameter. |
q_025 |
0.025 quantile of |
q_975 |
0.975 quantile of |
r0 |
a matrix of absolute difference between inputs and testing inputs. |
kernel_type |
type of kernel. |
alpha |
Roughness parameters in the kernel functions. |
method |
method of parameter estimation. |
interval_data |
a boolean value. If |
A list of 4 elements. The first is a vector for predictive mean for testing inputs. The second is a vector for lower quantile for 95% posterior credible interval and the third is the upper quantile for 95% posterior credible interval for these testing inputs. The last is a vector of standard deviation of each testing inputs.
Mengyang Gu [aut, cre], Jesus Palomo [aut], James Berger [aut]
Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>
Mengyang Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.
predict.rgasp
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