compute_post_fun_sgp {BayesGP} | R Documentation |
Computing the posterior samples of the function using the posterior samples of the basis coefficients for sGP
compute_post_fun_sgp(
samps,
global_samps = NULL,
k,
refined_x,
a,
region,
boundary = TRUE,
m,
intercept_samps = NULL,
initial_location = NULL
)
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. |
A data.frame that contains different samples of the function, with the first column being the locations of evaluations x = refined_x.