compute_post_fun_sgp {BayesGP} | R Documentation |
Computing the posterior samples of the function using the posterior samples of the basis coefficients for sGP
Description
Computing the posterior samples of the function using the posterior samples of the basis coefficients for sGP
Usage
compute_post_fun_sgp(
samps,
global_samps = NULL,
k,
refined_x,
a,
region,
boundary = TRUE,
m,
intercept_samps = NULL,
initial_location = NULL
)
Arguments
samps |
A matrix that consists of posterior samples for the O-spline basis coefficients. Each column represents a particular sample of coefficients, and each row is associated with one basis function. This can be extracted using 'sample_marginal' function from 'aghq' package. |
global_samps |
A matrix that consists of posterior samples for the global basis coefficients. If NULL, assume there will be no global polynomials and the boundary conditions are exactly zero. |
k |
The number of the sB basis. |
refined_x |
A vector of locations to evaluate the sB basis |
a |
The frequency of sGP. |
region |
The region to define the sB basis |
boundary |
A boolean variable to indicate whether the boundary condition should be considered in the prediction. |
m |
The number of harmonics to consider |
intercept_samps |
A matrix that consists of posterior samples for the intercept parameter. If NULL, assume there is no intercept samples to adjust. |
initial_location |
The initial location of the sGP. |
Value
A data.frame that contains different samples of the function, with the first column being the locations of evaluations x = refined_x.