spPredict_BPS: Perform prediction for BPS accelerated models - loop over...

In spBPS: Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning

Perform prediction for BPS accelerated models - loop over prediction set

Description

Perform prediction for BPS accelerated models - loop over prediction set

Usage

spPredict_BPS(data, X_u, priors, coords, crd_u, hyperpar, W, R, J)

Arguments

data	list two elements: first named Y, second named X
X_u	matrix unobserved instances covariate matrix
priors	list priors: named \mu_b,V_b,a,b
coords	matrix sample coordinates for X and Y
crd_u	matrix unboserved instances coordinates
hyperpar	list two elemets: first named \delta, second named \phi
W	matrix set of stacking weights
R	integer number of desired samples
J	integer number of desired partition of prediction set

Value

list BPS posterior predictive samples

spBPS documentation built on Oct. 25, 2024, 5:07 p.m.