pred_rgasp: Prediction for robust GaSP model

In RobustGaSP: Robust Gaussian Stochastic Process Emulation

Prediction for robust GaSP model

Description

A function to make prediction on robust GaSP models after the robust GaSP model has been constructed.

Usage

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)

Arguments

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 R, the correlation matrix.
LX	a lower triangular matrix for the cholesky decomposition of $X^tR^-1X$. X^tR^{-1}X
theta_hat	estimated mean/trend parameters.
sigma2_hat	estimated variance parameter.
q_025	0.025 quantile of t distribution.
q_975	0.975 quantile of t distribution.
r0	a matrix of absolute difference between inputs and testing inputs.
kernel_type	type of kernel. matern_3_2 and matern_5_2 are Matern kernel with roughness parameter 3/2 and 5/2 respectively. pow_exp is power exponential kernel with roughness parameter alpha. If pow_exp is to be used, one needs to specify its roughness parameter alpha.
alpha	Roughness parameters in the kernel functions.
method	method of parameter estimation. post_mode means the marginal posterior mode is used for estimation. mle means the maximum likelihood estimation is used. mmle means the maximum marginal likelihood estimation is used. The post_mode is the default method.
interval_data	a boolean value. If T, the interval of the data will be calculated. If F, the interval of the mean of the data will be calculated.

Value

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.

Author(s)

Mengyang Gu [aut, cre], Jesus Palomo [aut], James Berger [aut]

Maintainer: Mengyang Gu <[e-mail]>

References

Mengyang Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.

See Also

predict.rgasp

RobustGaSP documentation built on June 1, 2022, 9:08 a.m.