evalPsi: Evaluate covariance matrix for kriging

In guiludwig/stdf: Spatio-Temporal Data Fusion Model

Description

This function is for internal use on the STDF algorithm. It evaluates the covariance matrix for fitting, and the kriging matrix for prediction.

Usage

evalPsi(DTR, L, lamb.est, theta, PhiTime, PhiTimeTE = NULL, homogeneous,
  subsetStatic, kriging = FALSE)

Arguments

DTR	Matrix of distances
L	Number of functional principal components.
lamb.est	Vector of eigenvalues.
theta	Vector of spatial dependence parameters.
PhiTime	Vector of times, in numeric mode.
PhiTimeTE	Either NULL (repeats PhiTime) or vector of times, in numeric mode (for prediction)
homogeneous	Logical flag for homogeneous model.
subsetStatic	Logical vector for subset of static sensors
kriging	Logical flag, whether the function is evaluating a covariance matrix (FALSE) or a kriging cross-covariance matrix (TRUE). Defaults to FALSE.

Value

List of one items

psi.cov

Desired covariance matrix

See Also

stdf

Examples

## Not run: 
# Syntax example
stdf::evalPsi(DTR = matrix(c(1,0,0,1), 2),
        L = 1, lamb.est = 1,
        theta = 1:2, PhiTime = matrix(1:2, ncol=1), PhiTimeTE = NULL,
        homogeneous = TRUE, subsetStatic = rep(1, 2))
        
## End(Not run)

guiludwig/stdf documentation built on May 17, 2019, 9:27 a.m.